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1
Nishihara, Kazue. "Fundamental Study on Hand Waving Sensors." Journal of Robotics and Mechatronics 2, no. 5 (1990): 325–34. http://dx.doi.org/10.20965/jrm.1990.p0325.
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In order to develop a dynamic man-machine interface which measures angular motions of multi-link mechanisms, a uniaxial hand wave sensor is experimented with and triaxial hand wave sensors are simulated numerically. It was confirmed that a uniaxial hand wave sensor composed of a pair of uniaxially located accelerometers directly obtains exact angular acceleration by subtracting each acceleration signal. A triaxial hand wave sensor by a six (i.e. three pairs) accelerometer method, however, contains duplex angular velocities influenced by other axes in addition to the exact angular acceleration, so it is necessary to separate those physical values by a software algorithm. Adams-Moulton's method for solving differential equations was best suited to solve this nonlinear problem. A nine accelerometer method obtains linear equations for angular accelerations readily after arithmetic calculations of the nine signals.
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Zheng, Yuhuang. "Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework." Journal of Electrical and Computer Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/140820.
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Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.
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Ikurior, Seer J., Nelly Marquetoux, Stephan T. Leu, Rene A. Corner-Thomas, Ian Scott, and William E. Pomroy. "What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture." Sensors 21, no. 20 (2021): 6816. http://dx.doi.org/10.3390/s21206816.
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Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.
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Staunton, Craig A., Mikael Swarén, Thomas Stöggl, Dennis-Peter Born, and Glenn Björklund. "The Relationship Between Cardiorespiratory and Accelerometer-Derived Measures in Trail Running and the Influence of Sensor Location." International Journal of Sports Physiology and Performance 17, no. 3 (2022): 474–83. http://dx.doi.org/10.1123/ijspp.2021-0220.
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Purpose: To examine the relationship between cardiorespiratory and accelerometer-derived measures of exercise during trail running and determine the influence of accelerometer location. Methods: Eight trail runners (7 males and 1 female; age 26 [5] y; maximal oxygen consumption [] 70 [6] mL·kg−1·min−1) completed a 7-km trail run (elevation gain: 486 m), with concurrent measurements of , heart rate, and accelerations recorded from 3 triaxial accelerometers attached at the upper spine, lower spine, and pelvis. External exercise intensity was quantified from the accelerometers using PlayerLoad™ per minute and accelerometry-derived average net force. External exercise volume was calculated using accumulated PlayerLoad and the product of average net force and duration (impulse). Internal intensity was calculated using heart rate and -metrics; internal volume was calculated from total energy expenditure (work). All metrics were analyzed during both uphill (UH) and downhill (DH) sections of the trail run. Results: PlayerLoad and average net force were greater during DH compared with UH for all sensor locations (P ≤ .004). For all accelerometer metrics, there was a sensor position × gradient interaction (F2,1429.003; P <.001). The upper spine was lower compared with both pelvis (P ≤ .003) and lower spine (P ≤ .002) for all accelerometer metrics during both UH and DH running. Relationships between accelerometer and cardiorespiratory measures during UH running ranged from moderate negative to moderate positive (r = −.31 to .41). Relationships were stronger during DH running where there was a nearly perfect correlation between work and impulse (r = .91; P < .001). Conclusions: Simultaneous monitoring of cardiorespiratory and accelerometer-derived measures during trail running is suggested because of the disparity between internal and external intensities during changes in gradient. Sensor positioning close to the center of mass is recommended.
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Xu, Haotian, Yan Wang, Zhenzhao Zhou, and Jinyong Xu. "Design and simulation of micro piezoelectric fiber triaxial acceleration sensor." Journal of Physics: Conference Series 2483, no. 1 (2023): 012031. http://dx.doi.org/10.1088/1742-6596/2483/1/012031.
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Abstract Aiming at the demand for passive sonar systems for medium and low-frequency detection, low cost, and small working platform working sensor, a three-axis pressure piezoelectric accelerometer based on piezoelectric ceramic fiber is designed. Firstly, based on the acoustic vibration pick-up principle and extended Hamilton, the action mode of the accelerometer is analyzed theoretically to determine the accelerometer structure size. The accelerometer with a special mass structure is designed by using axially polarized piezoelectric ceramic fiber. The characteristic frequency and frequency domain analysis of the accelerometer are simulated by the FEM method. The results show that the overall size of the piezoelectric accelerometer is 20 mm ×20 mm×35 mm, the first-order natural frequency is 410 Hz, the external field directivity is standard “8” type, and the acceleration sensitivity reaches 112.2 mV/g, which verifies the great potential of the three-axis accelerometer designed by piezoelectric ceramic fiber material in the field of medium and low frequency.
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Łuczak, Sergiusz, Maciej Zams, Bogdan Dąbrowski, and Zbigniew Kusznierewicz. "Tilt Sensor with Recalibration Feature Based on MEMS Accelerometer." Sensors 22, no. 4 (2022): 1504. http://dx.doi.org/10.3390/s22041504.
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The main errors of MEMS accelerometers are misalignments of their sensitivity axes, thermal and long-term drifts, imprecise factory calibration, and aging phenomena. In order to reduce these errors, a two-axial tilt sensor comprising a triaxial MEMS accelerometer, an aligning unit, and solid cubic housing was built. By means of the aligning unit it was possible to align the orientation of the accelerometer sensitive axes with respect to the housing with an accuracy of 0.03°. Owing to the housing, the sensor could be easily and quickly recalibrated, and thus errors such as thermal and long-term drifts as well as effects of aging were eliminated. Moreover, errors due to local and temporal variations of the gravitational acceleration can be compensated for. Procedures for calibrating and aligning the accelerometer are described. Values of thermal and long-term drifts of the tested sensor, resulting in tilt errors of even 0.4°, are presented. Application of the sensor for monitoring elevated loads is discussed.
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Yuan, Bo, Zhifeng Tang, Pengfei Zhang, and Fuzai Lv. "Thermal Calibration of Triaxial Accelerometer for Tilt Measurement." Sensors 23, no. 4 (2023): 2105. http://dx.doi.org/10.3390/s23042105.
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The application of MEMS accelerometers used to measure inclination is constrained by their temperature dependence, and each accelerometer needs to be calibrated individually to increase stability and accuracy. This paper presents a calibration and thermal compensation method for triaxial accelerometers that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized based on the Levenberg-Marquardt algorithm, and then, based on this optimized calibration number, thermal compensation is performed based on the least squares method, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results achieved nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of −40 °C to 60 °C. The temperature drifts of x- and y-axes are reduced from −13.2 and 11.8 mg to −0.9 and −1.1 mg, respectively. The z-axis temperature drift is reduced from −17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. This research proposes a new low-cost and efficient strategy for increasing the practical applicability of triaxial accelerometers.
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Chiominto, Luciano, Giulio D’Emilia, Antonella Gaspari, and Emanuela Natale. "Dynamic Multi-Axis Calibration of MEMS Accelerometers for Sensitivity and Linearity Assessment." Sensors 25, no. 7 (2025): 2120. https://doi.org/10.3390/s25072120.
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A set of commercial triaxial micro-electromechanical systems (MEMS) accelerometers was calibrated using a custom-designed test bench featuring a rotating table. The calibration setup enabled simultaneous assessment of all accelerometer measurement components, generating precise reference accelerations within a frequency range of 0 to 8 Hz. A working model of the calibration setup and procedure was described to provide a complete uncertainty budget for both the reference and sensor accelerations. Through experimental uncertainty assessment of all the accelerometers, linearity and sensitivity were evaluated at different sensor levels. These parameters were determined by considering a single value for each accelerometer and detailing the analysis for each axis. Data processing revealed the achievable level of uncertainty and how it was influenced by the evaluation method employed for analyzing the calibration data.
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Pesti, Richárd, Dominik Csík, Péter Sarcevic, and Ákos Odry. "Measurement System for the Calibration of Accelerometer Arrays." Analecta Technica Szegedinensia 18, no. 2 (2024): 30–37. http://dx.doi.org/10.14232/analecta.2024.2.30-37.
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This paper addresses accelerometer array calibration, focusing on determining the errors between multiple sensors. Micro-electromechanical system (MEMS) based triaxial accelerometers, key components of Inertial Measurement Units (IMUs), are used in localization, robotics, and navigation systems. The requirements of these applications necessitate low-cost sensors, which makes MEMS IMUs a reasonable choice. However, these low-cost IMUs are significantly affected by systematic (i.e., bias, misalignment, scale-factor) and random errors. Achieving reliable sensor output depends on the precision of the executed calibration method. While traditional laboratory-based sensor calibration using specialized equipment (i.e., three-axis turntable) is accurate, it is time-consuming and costly. In contrast, in-field calibration techniques, which can be performed using a mechatronic actuator or a robotic arm, have gained popularity. These techniques involve comparing sensor measurements to established reference values. The MEMS sensors are increasingly being used in multi-sensor applications, which demands not only individual sensor error calibration but also important to determine the axis misalignment between the used sensors. During calibration process, various optimization algorithms (e.g., GA, PSO) can also be used to find the error parameters. The proposed measurement system allows for individual calibration of misalignment, bias, and scale factor of the sensor array, and eliminates between-sensor misalignment errors.
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Waite, Jim. "Accelerometer intensity vector sensor network for environmental noise monitoring with source direction and location." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 6 (2023): 1517–24. http://dx.doi.org/10.3397/in_2022_0210.
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AIVS (Accelerometer-based Intensity Vector Sensors, /āvs/) represent a new way to measure 3-d sound and are designed to integrate into existing noise monitoring solutions. Standard microphones measure sound pressure, which cannot alone deduce the direction of sound propagation. AIVS is based on the measurement of the velocity of a small parcel of air surrounding a triaxial accelerometer, from which a vector-based representation of sound intensity is calculated. AIVS integrates a MEMS triaxial accelerometer with one MEMS microphone and synchronously measures particle velocity and pressure, resulting in a 3-d intensity vector at each AIVS node. An AIVS network is synchronized to GNSS time and sensors are deployed in groups surrounding and/or within a local measurement site. Low power AIVS nodes are location-aware and estimate azimuth and elevation angles to detected noise sources as a function of frequency. Range to source is computed when noise events are observed from multiple nodes. AIVS nodes are managed by a Raspberry-PI (RPI) sensor hub in a wired CAN-bus supporting distances up to 100 m, or via the Bluetooth Low Energy (BLE) protocol. More widely separated nodes are joined through WWLAN technologies via the local RPI hub.
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Komarizadehasl, Seyedmilad, Fidel Lozano, Jose Antonio Lozano-Galant, Gonzalo Ramos, and Jose Turmo. "Low-Cost Wireless Structural Health Monitoring of Bridges." Sensors 22, no. 15 (2022): 5725. http://dx.doi.org/10.3390/s22155725.
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Nowadays, low-cost accelerometers are getting more attention from civil engineers to make Structural Health Monitoring (SHM) applications affordable and applicable to a broader range of structures. The present accelerometers based on Arduino or Raspberry Pi technologies in the literature share some of the following drawbacks: (1) high Noise Density (ND), (2) low sampling frequency, (3) not having the Internet’s timestamp with microsecond resolution, (4) not being used in experimental eigenfrequency analysis of a flexible and a less-flexible bridge, and (5) synchronization issues. To solve these problems, a new low-cost triaxial accelerometer based on Arduino technology is presented in this work (Low-cost Adaptable Reliable Accelerometer—LARA). Laboratory test results show that LARA has a ND of 51 µg/√Hz, and a frequency sampling speed of 333 Hz. In addition, LARA has been applied to the eigenfrequency analysis of a short-span footbridge and its results are compared with those of a high-precision commercial sensor.
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Langford, Joss, Ahmed Barakat, Engy Daghash, Harvinder Singh, and Alex V. Rowlands. "Digital Health Technologies for Optimising Treatment and Rehabilitation Following Surgery: Device-Based Measurement of Sling Posture and Adherence." Sensors 25, no. 1 (2024): 166. https://doi.org/10.3390/s25010166.
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Background: Following shoulder surgery, controlled and protected mobilisation for an appropriate duration is crucial for appropriate recovery. However, methods for objective assessment of sling wear and use in everyday living are currently lacking. In this pilot study, we aim to determine if a sling-embedded triaxial accelerometer and/or wrist-worn sensor can be used to quantify arm posture during sling wear and adherence to sling wear. Methods: Four participants were asked to wear a GENEActiv triaxial accelerometer on their non-dominant wrist for four hours in an office environment, and, for two of those hours, they also wore a sling in which an additional GENEActiv accelerometer was secured. During sling wear, they were asked to move their arm in the sling through a series of pre-specified arm postures. Results: We found that upper arm angle and posture type during sling wear can be predicted from a sling sensor alone (R2 = 0.79, p < 0.001 and Cohen’s kappa = 0.886, respectively). The addition of a wrist-worn sensor did not improve performance. The optimisation of an existing non-wear algorithm accurately detected adherence (99.3%). Conclusions: the remote monitoring of sling adherence and the quantification of immobilisation is practical and effective with digital health technology.
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Sarkar, Ajay K., Daniel A. James, Andrew W. Busch, and David V. Thiel. "Triaxial accelerometer sensor trials for bat swing interpretation in cricket." Procedia Engineering 13 (2011): 232–37. http://dx.doi.org/10.1016/j.proeng.2011.05.078.
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Rezinkina, Marina, and Claus Braxmaier. "Designs of Optomechanical Acceleration Sensors with the Natural Frequency from 5 Hz to 50 kHz." Designs 8, no. 2 (2024): 33. http://dx.doi.org/10.3390/designs8020033.
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In many applications, such as space navigation, metrology, testing, and geodesy, it is necessary to measure accelerations with frequencies ranging from fractions of a hertz to several kilohertz. For this purpose, optomechanical sensors are used. The natural frequency of such sensors should be approximately ten times greater than the frequency of the measured acceleration. In the case of triaxial acceleration measurements, a planar design with two sensors that measure accelerations in two perpendicular in-plane directions and a third sensor that measures out-of-plane acceleration is effective. The mechanical characteristics of the existing designs of both in-plane and out-of-plane types of sensors were analyzed, and the improved designs were elaborated. Using numerical simulation, the dependencies of the natural frequency level in the range from several hertz to tens of kilohertz on the designs and geometric parameters of opto-mechanical accelerometers were modeled. This allows one to select the accelerometer design and its parameters to measure the acceleration at the assigned frequency. It is shown that the opto-mechanical accelerometers of the proposed designs have reduced dissipation losses and crosstalk.
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Łuczak, Sergiusz, Robert Grepl, and Maciej Bodnicki. "Selection of MEMS Accelerometers for Tilt Measurements." Journal of Sensors 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9796146.
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In order to build a tilt sensor having a desired sensitivity and measuring range, one should select an appropriate type, orientation, and initial position of an accelerometer. Various cases of tilt measurements are considered: determining exclusively pitch, axial tilt, or both pitch and roll, where Cartesian components of the gravity acceleration are measured by means of low-g uni-, bi-, tri-, or multiaxial micromachined accelerometers. 15 different orientations of such accelerometers are distinguished (each illustrated with respective graphics) and related to the relevant mathematical formulas. Results of the performed experimental study revealed inherent misalignments of the sensitive axes of micromachined accelerometers as large as 1°. Some of the proposed orientations make it possible to avoid a necessity of using the most misaligned pairs of the sensitive axes; some increase the accuracy of tilt measurements by activating all the sensitive axes or reducing the effects of anisotropic properties of micromachined triaxial accelerometers; other orientations make it possible to reduce a necessary number of the sensitive axes at full measurement range. An increase of accuracy while using multiaxial accelerometers is discussed. Practical guidelines for an optimal selection of a particular micromachined accelerometer for a specific case of tilt measurement are provided.
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Johari, Nur Nazleen, Fuei Pien Chee, Siti Rahayu Mohd Hashim, Bailon Golutin, Jedol Dayou, and Jedol Dayou. "DESCRIPTIVE STATISTICAL CALIBRATION METHOD OF TRIAXIAL DIGITAL ACCELEROMETER ADXL345 AS EARTHQUAKES SENSOR." Borneo Science | The Journal of Science and Technology 44, no. 1 (2023): 13–19. http://dx.doi.org/10.51200/bsj.v44i1.4340.
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Seismic monitoring networks are the crucial elements in strong motion seismology for effective risk reduction. Low scale lateral variation of high intensity ground movement caused by earthquakes will be detected more effectively with densely located networks. However, the limitations of developing such project are rooted in expensive costs associated with the construction and installation in addition to bulky size of the conventional seismic observation system. Recently, micro-electromechanical system (MEMS) has being recognized in the applications of seismological and earthquake engineering due to the high precision obtained in these micron size semiconductor instruments and cheaper alternative for traditional seismic detector. ADXL345 is a type of digital triaxial MEMS accelerometer that is ideal for measurement of low-frequency vibrations and static accelerations of gravity, which makes it suitable for ground motion detection. Thus, this study aims at calibrating ADXL345 sensor that is required as sensing component in an affordable earthquake monitoring system with the Earthquake Benchmarking System (Penanda Aras Gempa Bumi, PAG) available in the inventory of Department of Mineral and Geoscience Malaysia, Sabah. Soil vibrations in EW (east-west or x-axis), NS (north-south or y-axis), and UD (up-down or z-axis) directions during random forces hit on the surface are recorded by both accelerometers. Acceleration magnitudes recorded by PAG and ADXL345 are extracted and data exploration is performed. Predominantly, ADXL345 measurements in horizontal and vertical ground movements are on a higher scale than the reference device. Subsequently, evaluation by using descriptive statistical analysis is chosen to produce numerical equations for data correction operations. İmplementation of the mathematical functions in ADXL345 for observing land movements in EW, NS, and UD directions resulted in decreasing the range values of output readings. Higher approximation of magnitudes of ground motion with the PAG system is achieved.
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Na, Youngmin, Hyejin Yang, and Jihwan Woo. "Classification of the Korean Sign Language Alphabet Using an Accelerometer with a Support Vector Machine." Journal of Sensors 2021 (August 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/9304925.
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Recognition and understanding of sign language can aid communication between nondeaf and deaf people. Recently, research groups have developed sign language recognition algorithms using multiple sensors. However, in everyday life, minimizing the number of sensors would still require the use of a sign language interpreter. In this study, a sign language classification method was developed using an accelerometer to recognize the Korean sign language alphabet. The accelerometer is worn on the proximal phalanx of the index finger of the dominant hand. Triaxial accelerometer signals were used to segment the sign gesture (i.e., the time period when a user is performing a sign) and recognize the 31 Korean sign language letters (producing a chance level of 3.2%). The vector sum of the accelerometer signals was used to segment the sign gesture with 98.9% segmentation accuracy, which is comparable to that of previous multisensor systems (99.49%). The system was able to classify the Korean sign language alphabet with 92.2% accuracy. The recognition accuracy of this approach was found to be higher than that of a previous work in the same sign language alphabet classification task. The findings demonstrate that a single-sensor accelerometer with simple features can be reliably used for Korean sign language alphabet recognition in everyday life.
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Saunders, Nathan W., Panagiotis Koutakis, Anne D. Kloos, Deborah A. Kegelmeyer, Jessica D. Dicke, and Steven T. Devor. "Reliability and Validity of a Wireless Accelerometer for the Assessment of Postural Sway." Journal of Applied Biomechanics 31, no. 3 (2015): 159–63. http://dx.doi.org/10.1123/jab.2014-0232.
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Clinicians are in need of valid and objective measures of postural sway. Accelerometers have been shown to be suitable alternatives to expensive and stationary force plates. We evaluated the test-retest reliability and balance task discrimination capability of a new wireless triaxial accelerometer (YEI 3-Space Sensor). Four testing conditions (eyes open or closed, while on a firm or compliant surface) were used to progressively challenge the static balance of 20 healthy male (n = 8) and female (n = 12) older adults (mean age 81 ± 4.3 y). Subjects completed 2 blocks of three 30-second trials per condition. The accelerometer was positioned on the lower back to acquire mediolateral (M-L) and anterior-posterior (A-P) accelerations. Intraclass correlation coefficients were all good to excellent, with values ranging from .736 to .972 for trial-to-trial and from .760 to .954 for block-to-block. A significant stepwise increase in center of mass acceleration root mean square values was found across the 4 balance conditions (F[1.49, 28.26] = 39.54, P < .001). The new accelerometer exhibited good to excellent trial-to-trial and block-to-block reliability and was sensitive to differences in visual and surface conditions and acceleration axes.
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Cascone, Valeria, Jacopo Boaga, and Giorgio Cassiani. "Small Local Earthquake Detection Using Low-Cost MEMS Accelerometers: Examples in Northern and Central Italy." Seismic Record 1, no. 1 (2021): 20–26. http://dx.doi.org/10.1785/0320210007.
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Abstract This study evaluates the seismicity detection efficiency of a new low-cost triaxial accelerometer prototype based on microelectromechanical systems (MEMS) technology. Networks of MEMS sensors were installed in telecommunication infrastructures to build two small arrays in northern and central Italy. The sensor prototypes recorded major earthquakes as well as nine small seismic events with 2.0&lt;ML&lt;3.0. Where possible, MEMS were compared to the closest high-quality seismic stations belonging to the national accelerometric network. The comparison, in terms of peak ground accelerations and spectral responses, confirms that the signals are in good agreement. The tested inexpensive MEMS sensors were able to detect small local events with epicentral distances as large as 50 km and provided an efficient characterization of the main motion parameters. This confirms that the proposed accelerometer prototypes are promising tools to integrate into traditional networks for local seismicity monitoring.
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Wu, Ti, Xiaolong Zhang, Dong Wang, Weigong Zhang, Deng Pan, and Liang Tao. "Comparative Study and Real-World Validation of Vertical Load Estimation Techniques for Intelligent Tire Systems." Sensors 25, no. 7 (2025): 2100. https://doi.org/10.3390/s25072100.
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Accurate vertical load measurement through intelligent tire technology is crucial for vehicle stability, handling, and safety. Existing studies have mainly focused on modeling and bench experiments, overlooking a detailed comparative analysis of real sensor performance and validation under actual driving conditions. This study addresses this gap by performing sensor comparisons and extensive real-road validation to ensure the accuracy and reliability of the proposed methods. First, finite element modeling (FEM) is used to assess the feasibility of accelerometer and strain-based sensors for vertical load prediction. High-precision bench tests quantitatively compare the performance of multiple triaxial Integrated Electronics Piezoelectric (IEPE) accelerometers and Polyvinylidene Fluoride (PVDF) sensors, identifying accelerometers as the superior choice due to their better stability and linearity. Vertical load prediction algorithms are developed using Support Vector Machine (SVM) and linear regression, considering variables like contact length, vehicle speed, and tire pressure. The algorithms are validated under real-road conditions using high-performance instruments across constant speed, acceleration, braking, and cornering, and a self-designed compact Intelligent Tire Test Unit (ITTU) is deployed for product-level implementation, confirming its effectiveness in real-world driving scenarios. The findings provide a validated framework for accurate vertical load estimation and real-time tire parameter prediction, offering practical insights for improving intelligent tire technology in dynamic driving conditions.
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Schuster, Alexander, Andreas Otto, Hendrik Rentzsch, and Steffen Ihlenfeldt. "Multi-Sensory Tool Holder for Process Force Monitoring and Chatter Detection in Milling." Sensors 24, no. 17 (2024): 5542. http://dx.doi.org/10.3390/s24175542.
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Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task.
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Martin, Peter G., Gregory W. Hall, Jeff R. Crandall, and Walter D. Pilkey. "Measuring the Acceleration of a Rigid Body." Shock and Vibration 5, no. 4 (1998): 211–24. http://dx.doi.org/10.1155/1998/134562.
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Two methods to measure the six-degree-of-freedom acceleration of a point on a rigid body are presented. The first, referred to as the periphery scheme, makes use of three clusters of accelerometers mounted orthogonal to each other and coincident with the axes of the point. One of the clusters consists of the three accelerometers attached to a cube-shaped triaxial angular rate sensor (ARS). The second method, called the compact cube scheme, uses a single 3-accelerometer/ARS cluster that may be mounted anywhere on the rigid body. During impact tests with an instrumented rigid body, both methods produced measurements that were highly correlated near the time of peak acceleration. Whereas the compact cube scheme was more economical and easier to implement, the periphery scheme produced results that were less disrupted by instrument signal errors and noisy environments.
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De Angelis, Alessio, Francesco Santoni, Paolo Carbone, Manuela Cecconi, Alessia Vecchietti, and Francesco Di Lorenzo. "Development of an IoT Structural Monitoring System Applied to a Hypogeal Site." Sensors 20, no. 23 (2020): 6769. http://dx.doi.org/10.3390/s20236769.
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This paper describes the development of a distributed sensing system that can be disseminated in an environment of interest to monitor the vibration of a structure. This low-cost system consists of several sensor nodes and a central receiving node. All nodes are built using off-the-shelf electronic components. Each of the sensor nodes is battery-powered and equipped with a triaxial MEMS accelerometer, a wireless Long Range (LoRa) transceiver module for data transmission, a GPS module used for synchronization, and a microcontroller. The operation of the sensor node is validated by controlled laboratory tests where it is compared to a commercial reference accelerometer. Furthermore, the feasibility and potential benefits of the application of the proposed system to a structure in an archaeological site is investigated. Results show that the proposed sensor node could successfully monitor the vibration at several locations within the site. Therefore, it may be employed to detect the most relevant stresses to the structure, allowing for the identification of risks.
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Khalil, Rana M., Lisa M. Shulman, Ann L. Gruber-Baldini, et al. "Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson’s Disease." Sensors 24, no. 15 (2024): 4983. http://dx.doi.org/10.3390/s24154983.
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Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson’s disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
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Li, Huailiang, Xianguo Tuo, Tong Shen, Mark Julian Henderson, and Jérémie Courtois. "A rapid seismic data calibration technique using integrated micro-electro-mechanical system inertial sensor groups for a 3C vertical seismic profile." GEOPHYSICS 82, no. 6 (2017): P109—P118. http://dx.doi.org/10.1190/geo2016-0218.1.
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Calibration of 3C vertical seismic profile (VSP) data is an exciting challenge because the orientation of the tool is random when only seismic data are considered. We have augmented the sensor package on the VSP tool with micro-electro-mechanical system (MEMS) inertial sensors and applied a gesture measuring method to determine the tool orientation and calibration. This technique can quickly produce high precision, orientation, and angle information when integrated with the seismometer. The augmented sensor package consists of a low-cost triaxial MEMS gyroscope, an electronic compass, and an accelerometer. The technique to process the gesture information is based on the OpenGL software for 3D modeling. We have tested this approach on a large number of field data sets and it appeared to be faster and more reliable than other approaches.
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Eom, Heesang, Jongryun Roh, Yuli Sun Hariyani, et al. "Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation." Sensors 21, no. 21 (2021): 7058. http://dx.doi.org/10.3390/s21217058.
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Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.
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Lee, Jin-Hee, Yeong-Ju Lee, Minseok Song, and Byeong-Seok Shin. "Adaptive Power Saving Method for Mobile Walking Guidance Device Using Motion Context." Mobile Information Systems 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/540512.
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It is important to recognize the motion of the user and the surrounding environment with multiple sensors. We developed a guidance system based on mobile device for visually impaired person that helps the user to walk safely to the destination in the previous study. However, a mobile device having multiple sensors spends more power when the sensors are activated simultaneously and continuously. We propose a method for reducing the power consumption of a mobile device by considering the motion context of the user. We analyze and classify the user’s motion accurately by means of a decision tree and HMM (Hidden Markov Model) that exploit the data from a triaxial accelerometer sensor and a tilt sensor. We can reduce battery power consumption by controlling the number of active ultrasonic sensors and the frame rate of the camera used to acquire spatial context around the user. This helps us to extend the operating time of the device and reduce the weight of the device’s built-in battery.
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Chernyak, Mykola, and Evgen Burym. "Experimental strapdown inertial navigation system for the autonomous guidance system of a short-range uav." MECHANICS OF GYROSCOPIC SYSTEMS, no. 43 (May 15, 2022): 34–45. http://dx.doi.org/10.20535/0203-3771432022275280.
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The task of experimental verification of the possibility of using a miniature strapdown inertial navigation system (SINS) on micromechanical sensors for autonomous guidance of a short-range manned aircraft moving along a programmed hypothetical trajectory is considered.
 A mathematical model of the first approximation of the instrumental errors of the two-coordinate autonomous SINS of the aircraft with a short flight time was obtained. The mathematical model allows both to calculate the values of these errors for specific types of inertial sensors used in SINS (direct analysis problem), and to choose the types of accelerometers and gyroscopes based on ensuring the given accuracy of SINS (inverse synthesis problem). The requirements for permissible errors of accelerometers and gyroscopes of the SINS sensor unit have been determined.
 The design and manufacturing technology of an experimental SINS based on a triaxial micromechanical accelerometer and three uniaxial micromechanical gyroscopes are described.
 The technology of flight tests of the experimental SINS is considered. The test results of the experimental SINS of the aircraft, created in accordance with the requirements specified in the article, confirmed the correctness of the chosen approach to ensuring the accuracy of the SINS for the autonomous inertial guidance system of short-range aircraft.
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Hoffmann, Gundula, Saskia Strutzke, Daniel Fiske, Julia Heinicke, and Roman Mylostyvyi. "A New Approach to Recording Rumination Behavior in Dairy Cows." Sensors 24, no. 17 (2024): 5521. http://dx.doi.org/10.3390/s24175521.
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Rumination behavior in cattle can provide valuable information for monitoring health status and animal welfare, but continuous monitoring is essential to detect changes in rumination behavior. In a previous study validating the use of a respiration rate sensor equipped with a triaxial accelerometer, the regurgitation process was also clearly visible in the pressure and accelerometer data. The aim of the present study, therefore, was to measure the individual lengths of rumination cycles and to validate whether the sensor data showed the same number of regurgitations as those counted visually (video or direct observation). For this purpose, 19 Holstein Friesian cows equipped with a respiration rate sensor were observed for two years, with a focus on rumination behavior. The results showed a mean duration of 59.27 ± 9.01 s (mean ± SD) per rumination cycle and good agreement (sensitivity: 99.1–100%, specificity: 87.8–95%) between the two methods (sensor and visual observations). However, the frequency of data streaming (continuously or every 30 s) from the sensor to the data storage system strongly influenced the classification performance. In the future, an algorithm and a data cache will be integrated into the sensor to provide rumination time as an additional output.
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González-Otero, Digna M., Jesus Ruiz, Sofía Ruiz de Gauna, Unai Irusta, Unai Ayala, and Erik Alonso. "A New Method for Feedback on the Quality of Chest Compressions during Cardiopulmonary Resuscitation." BioMed Research International 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/865967.
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Quality of cardiopulmonary resuscitation (CPR) improves through the use of CPR feedback devices. Most feedback devices integrate the acceleration twice to estimate compression depth. However, they use additional sensors or processing techniques to compensate for large displacement drifts caused by integration. This study introduces an accelerometer-based method that avoids integration by using spectral techniques on short duration acceleration intervals. We used a manikin placed on a hard surface, a sternal triaxial accelerometer, and a photoelectric distance sensor (gold standard). Twenty volunteers provided 60 s of continuous compressions to test various rates (80–140 min−1), depths (3–5 cm), and accelerometer misalignment conditions. A total of 320 records with 35312 compressions were analysed. The global root-mean-square errors in rate and depth were below 1.5 min−1and 2 mm for analysis intervals between 2 and 5 s. For 3 s analysis intervals the 95% levels of agreement between the method and the gold standard were within −1.64–1.67 min−1and −1.69–1.72 mm, respectively. Accurate feedback on chest compression rate and depth is feasible applying spectral techniques to the acceleration. The method avoids additional techniques to compensate for the integration displacement drift, improving accuracy, and simplifying current accelerometer-based devices.
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Hughes, Liam David, Martin Bencsik, Maria Bisele, and Cleveland Thomas Barnett. "Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach." Sensors 23, no. 22 (2023): 9241. http://dx.doi.org/10.3390/s23229241.
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Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category’s cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.
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Roh, Taehoun, Hong Goo Yeo, Cheeyoung Joh, et al. "Fabrication and Underwater Testing of a Vector Hydrophone Comprising a Triaxial Piezoelectric Accelerometer and Spherical Hydrophone." Sensors 22, no. 24 (2022): 9796. http://dx.doi.org/10.3390/s22249796.
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A vector hydrophone is an underwater acoustic sensor that can detect the direction of a sound source. Wide-band characteristics and high sensitivity enhance the performance of underwater surveillance systems in complex environments. A vector hydrophone comprising a triaxial piezoelectric accelerometer and spherical hydrophone was fabricated and tested in the air and underwater. The vector hydrophone was designed to exceed the quantitative figures of merit (i.e., receiving voltage sensitivity and bandwidth) of commercially available hydrophones. Accelerometer performance was enhanced by placing a pair of piezoelectric single crystals on each axis and modifying the seismic mass material. The receiving voltage sensitivity of the omnidirectional hydrophone was approximately −160 dB relative to 1 V/μPa with the amplifier in water; the sensitivity of the accelerometer exceeded 300 mV/g in air and −215 dB relative to 1 V/μPa underwater over the frequency range of interest. The receiving directivity of the vector hydrophone was validated underwater, which confirmed that it could detect the direction of a sound source.
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JUZEK, Michał, and Paweł SŁOWIŃSKI. "THE IMPACT OF ACCELEROMETER MOUNTING ON THE CORRECTNESS OF THE RESULTS OBTAINED IN NDT-TYPE TESTS." Transport Problems 19, no. 1 (2024): 97–105. http://dx.doi.org/10.20858/tp.2024.19.1.08.
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The paper attempts to evaluate the effect of acceleration sensor mounting on the recorded vibration time course. The study used a prepared model of a railroad rail and triaxial acceleration sensors. Three non-invasive methods of mounting the vibration acceleration transducers were selected for analysis: mounting with cyanoacrylate glue, mounting with a magnet, and mounting with wax. The information capacity of the signals was analyzed based on the recorded time waveforms, which totaled more than 90, and their vibration signals. The analysis compared both the basic parameters of the signals (maximum amplitudes and root mean square values) and a comprehensive analysis of the signals using the short-time Fourier transform method, as well as the wavelet transform. The results show significant differences in the recorded signal parameters depending on how the acceleration sensor is mounted, as well as the axis analyzed. The differences can negatively affect the correctness of the measurements made and falsify the picture of the real condition.
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Jia, Jingqing, Shuo Feng, and Wei Liu. "A triaxial accelerometer monkey algorithm for optimal sensor placement in structural health monitoring." Measurement Science and Technology 26, no. 6 (2015): 065104. http://dx.doi.org/10.1088/0957-0233/26/6/065104.
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Evans, Stuart A., Daniel A. James, David Rowlands, and James B. Lee. "Evaluation of Accelerometer-Derived Data in the Context of Cycling Cadence and Saddle Height Changes in Triathlon." Sensors 21, no. 3 (2021): 871. http://dx.doi.org/10.3390/s21030871.
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In the multisport of triathlon cycling is the longest of the three sequential disciplines. Triathlon bicycles differ from road bicycles with steeper seat tube angles with a change to saddle height altering the seat tube angle. This study evaluated the effectiveness of a tri axial accelerometer to determine acceleration magnitudes of the trunk in outdoor cycling in two saddle positions. Interpretation of data was evaluated based on cadence changes whilst triathletes cycled in an aerodynamic position in two saddle positions. The evaluation of accelerometer derived data within a characteristic overground setting suggests a significant reduction in mediolateral acceleration of the trunk, yielding a 25.1% decrease when saddle height was altered alongside reduced rate of perceived exertion (3.9%). Minimal differences were observed in anteroposterior and longitudinal acceleration. Evaluation of sensor data revealed a polynomial expression of the subtle changes between both saddle positions. This study shows that a triaxial accelerometer has capability to continuously measure acceleration magnitude of trunk movements during an in-the-field, varied cadence cycle protocol. Accessible and practical sensor technology could be relevant for postural considerations when exploring saddle position in dynamic settings.
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Shi, Yun Bo, Zhi Jun Zhou, Sheng Fei Dong, and Jun Tang. "Design, Fabrication and Test of In-Plane MEMS Piezoresistive High-g Accelerometer." Key Engineering Materials 609-610 (April 2014): 1111–16. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.1111.
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The new structure of in-plane high-g MEMS sensor for 10,000-150,000g has been designed in our paper, and it was packaged in dimensions of 17.5mm×12mm×4.5mm. In our experiments, the sensitivity reach to 0.335uv/g by the Hopkinson bar system and it also is in quick respose to the frequence. Meanwhile, the results show that all acceleration-time pulses are in very close agreement for acceleration amplitudes to about 150000g. It is a new way to monolithically integral triaxial piezoresistive high-g accelerometer in the future.
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Horiguchi, Yusuke, Haiyun WU, Masataka Murata, et al. "Innovative Approach to Fish Stress Analysis: AI-Enhanced Biosensing and Accelerometer Integration." ECS Meeting Abstracts MA2024-02, no. 67 (2024): 4733. https://doi.org/10.1149/ma2024-02674733mtgabs.
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Background and Objectives: In our collaborative efforts, we have pioneered the development of a wireless biosensor system that can monitor blood glucose levels in fish, serving as a indicator of stress response. However, while the response value of the biosensor detected the increase in the fish's blood glucose level, it was a challenge to pinpoint the exact factors causing it. To address this, we augmented our measurements with data from a triaxial acceleration sensor and posture estimation using deep learning. This integration of biosensors, physical sensors, and video analysis technology, a testament to the power of interdisciplinary research, allowed us to delve deeper into the state of the fish. Materials and Methods: The glucose biosensor, constructed using a Pt/Ir wire (φ: 0.178 mm) as the working electrode, Ag/AgCl as the reference electrode, and glucose oxidase immobilized on the working electrode surface, was connected to a data logger with a built-in triaxial acceleration sensor. This biosensor was then implanted in a Nile tilapia (Oreochromis niloticus), and the data logger was attached to the fish's side. After allowing the fish to swim freely in the experimental tank overnight, it was introduced to a larger individual, and its stress responses and acceleration due to fighting behavior were recorded. The confrontation was filmed from above the tank, and the positional coordinates of each individual in the filmed images were obtained using DeepLabCut. Results and Discussion: The response value of the biosensor of the individual that became subordinate in the tank from the middle of the confrontation increased. It is thought this was a stress response to being attacked by a larger individual and cornered against the wall in the tank. Acceleration also captured fluctuations derived from the intense fighting behavior during the confrontation. The positional coordinates of each individual reflected the power relationship between the larger and smaller individuals. The larger individual was often located near the center of the tank because of its dominance in the water tank. In contrast, the smaller, subordinate individual was often located near the wall and occasionally pushed against the wall by the larger individual. In this experiment, we analyzed one-hour-long videos during the confrontation to observe the positional relationship between the two individuals from the beginning to the end. Combined with detecting stress responses by biosensors, the possibility of exploring which fish behaviors are accompanied by changes in physiological conditions while the fish are swimming has become apparent. Conclusion: Data from physical sensors such as triaxial accelerometers and video analysis can enhance the information from biosensor response values. The fighting behavior of fish and the social relationships among fish in the tank were quantified by accelerometers and video analysis, shedding light on the causes of fluctuations in fish blood glucose levels. Given the close relationship between fish behavior and physiological state, the integration of physical sensor and video analysis technology with biosensor technology holds promise for improving fish health management in the field. Moreover, by experimentally correlating the fluctuations in biosensor values with the behavior of the fish in the video, it may be possible in the future to estimate the physiological state of the fish simply by monitoring the fish with a camera, a potential game-changer in fish health monitoring. Figure 1
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K., N. Apinaya Prethi, Sangeetha M., Nithya S., Priyadharshini G., and Anithadevi N. "An Electric Eye for Human Activity Recognition: A Hybrid Neural Network." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2806–9. https://doi.org/10.35940/ijeat.C5957.029320.
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A real time detection of human movements is a practical solution to monitor aged people or mentally challenged people with the permission of their family. Household person is needed to monitor the elder and differently abled people. Instead of monitoring their activities with the help of other people, smart phones are used as a remote to monitor their activities and simultaneously send the message to their family members. The accelerometer sensor placed in the mobile phones. It is used to identify the activities of the person who holds the mobile phones. The most commonly used classifier technique is Naive Bayes classifier which has a limitation of handle with the large set of data. To overcome this defect, the proposed system classifies the data using k-nearest neighbor (K-NN) technique and Neuro evolution. This system recognize some representative human movements such as walking, climbing upstairs, climbing downstairs, standing, sitting and running ,using a conventional mobile equipped with a single tri-axial accelerometer sensor.
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Crognale, Marianna, Cecilia Rinaldi, Francesco Potenza, Vincenzo Gattulli, Andrea Colarieti, and Fabio Franchi. "Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers." Sensors 24, no. 8 (2024): 2435. http://dx.doi.org/10.3390/s24082435.
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Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.
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Nogoy, Kim Margarette Corpuz, Sun-il Chon, Ji-hwan Park, Saraswathi Sivamani, Dong-Hoon Lee, and Seong Ho Choi. "High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor." Sensors 22, no. 16 (2022): 5961. http://dx.doi.org/10.3390/s22165961.
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Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.
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Rorato, Oscar. "A Wireless Sensor Network Board for Environmental Monitoring Using GNSS and Analog Triaxial Accelerometer." International Journal of Embedded Systems and Applications 2, no. 4 (2012): 35–43. http://dx.doi.org/10.5121/ijesa.2012.2403.
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Roth, John T. "Using the Eigenvalues of Multivariate Spectral Matrices to Achieve Cutting Direction and Sensor Orientation Independence." Journal of Manufacturing Science and Engineering 128, no. 1 (2005): 350–54. http://dx.doi.org/10.1115/1.2123067.
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There is a strong need for monitoring techniques capable of tracking the health of cutting tools under varying conditions. Unfortunately, most monitoring techniques are dependent on the cutting direction and/or the sensor orientation, limiting their effectiveness in the typical industrial environment. With this in mind, this research develops a monitoring technique that is independent of both of these factors. This is accomplished by using multivariate autoregressive models that are fit to the output from a triaxial accelerometer. The work shows that the eigenvalues of multivariate spectral matrices, calculated at the machining frequencies, are not only sensitive to the condition of the tool but are also independent of the direction of cutting and the orientation of the sensor. This independence is verified experimentally through tests conducted under a variety of cutting directions and sensor orientations.
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Zeng, Kun, Sheng Zeng, Hai Huang, et al. "Sensing Mechanism and Real-Time Bridge Displacement Monitoring for a Laboratory Truss Bridge Using Hybrid Data Fusion." Remote Sensing 15, no. 13 (2023): 3444. http://dx.doi.org/10.3390/rs15133444.
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Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.
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Balek, Jan, and Petr Klokočník. "Development of low-cost inclination sensor based on MEMS accelerometers." IOP Conference Series: Earth and Environmental Science 906, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1755-1315/906/1/012057.
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Abstract Rapid development of Micro Electro Mechanical Systems (MEMS) and the minimization of sensor cost, size and energy consumption in the last two decades leads to an effort to replace traditional sensors with their MEMS alternatives. The power consumption is one of the key problems, due to necessity to provide long term device power supply. Therefore a newly developed device was designed with the accent to low power consumption, to be able to operate with one small internal battery at range several months to years. The main goal was to develop a wireless monitoring system capable of continuous stability monitoring of various building structures. The sensor is designed to measure slow inclination variations or changes and in combination with variant designed for high frequency monitoring should represent complete solution of real time structure health monitoring. The STATOTEST compact measurement system is mainly composed of triaxial MEMS accelerometer as a sensing unit, motherboard containing IOT modules and battery, all placed in single waterproof box. The raw signal measured by MEMS accelerometer is preprocessed inside the unit and the data are sent to the cloud via LoRaWan, NBIoT or satellite. The results can be displayed, managed and exported through the web application. This paper presents current state of sensor development, refer to number of problems, which were solved during the process and deals with estimation of its accuracy characteristics in the laboratory conditions. During the laboratory experiment, small defined changes of inclination were performed and compared with values registered by the inclination sensor. The testing was performed before and after calibration procedures. After eliminating of accelerometers production errors, the accuracy of the unit measurement RMSE is less than 0.002° for the step change of 0.09°, tested in six different orientations of the sens or. One measurement is mean of 1000 measurements and its residual random error for one measurement is 2°x10e-5. Series of laboratory tests proved high short-term device accuracy in stable conditions. It is well known, that MEMS accelerometers strongly depend on the sensor temperature. To perform temperature compensation, we built own climate chamber, which is able to change automatically temperature of the several devices at once in specified ranges. Temperature compensation was then performed by using of polynomial approximation to obtain the field measurement accuracy close to laboratory conditions. This task is challenging because it is necessary to improve the proper material composition between the MEMS and the monitored structure and the device fixing methods.
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Tedy, Devlin, and Wiryanto Dewobroto. "EVALUASI PENGUJIAN VIBRASI STRUKTUR (STUDI KASUS : DERMAGA DONGGALA)." Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan 3, no. 1 (2019): 71. http://dx.doi.org/10.24912/jmstkik.v3i1.2730.
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Setiap struktur memiliki perilaku dinamik berupa frekuensi alami yang dapat dicari dengan uji vibrasi di lapangan secara empiris dan analisis numerik. Frekuensi alami terdiri dari massa, kekakuan, dan arah (mode shape). Parameter-parameter tersebut menghasilkan banyak variasi model struktur. Model yang paling tepat dengan kondisi lapangan dapat dicari dengan bantuan hasil uji vibrasi yang berfungsi sebagai kalibrator. Dalam mencari frekuensi alami melalui uji vibrasi sangat tergantung pada teknologi seperti tipe sensor dan cara penempatannya yang digunakan untuk merekam getaran yang diberikan. Tipe sensor terdiri dari berbagai macam seperti uniaxial, biaxial, dan triaxial. Pada kasus uji vibrasi struktur dermaga Donggala menggunakan 6 buah sensor accelerometer uniaxial. Sensor dipasang dalam 3 tempat berbeda masing-masing tempat dalam arah lateral dan vertikal. Hasil pengujian dari pihak surveyor didalam mengevaluasi hasilnya hanya rata-rata tanpa memperhitungkan pengaruh arah. Hal ini yang akan dievaluasi pada penelitian ini. Evaluasi yang akan dilakukan adalah membandingkan hasil pengujian vibrasi dengan analisis numerik. Dari berbagai model analisis numerik dapat diketahui bahwa meskipun nilai frekuensi alaminya bervariasi tetapi masih didalam batas nilai tertentu. Dengan melihat apakah arah penempatan sensor dan arah tumbukan kapal, maka dapat diprediksi perilaku dinamik dermaga apakah translasi atau rotasi yang terjadi. Dengan demikian evaluasi yang digunakan oleh surveyor dengan melakukan rata-rata tanpa melihat arah adalah tidak tepat. Oleh sebab itu akan dilakukan evaluasi ulang mempelajari arah pemberian gaya, arah pemasangan dan penempatan sensor accelerometer serta perlu melakukan pengelompokan hasil pengujian vibrasi berdasarkan arah sensor. Setelah mempelajari model numerik dari dermaga dapat diketahui bahwa model numerik yang bertranslasi mempunyai kesesuaian dengan data tumbukan pada salah satu titik sensor yang dipasang. Pemodelan numerik yang mendekati nilai ini adalah sesuai dengan data perencanaan sebelumnya. Dari penelitian ini dapat diketahui bahwa pemahaman pengujian vibrasi perlu dilakukan pengelompokan sesuai arah penempatan sensor dan tidak dapat dilakukan rata-rata. Each structure has dynamic behavior in the form of natural frequencies that can be searched by vibration testing in the field empirically and numerical analysis. Natural frequency consists of mass, stiffness, and direction (shape mode). These parameters produce many variations of the structural model. The most appropriate model with field conditions can be sought with the help of vibration test results that function as a calibrator. In searching for natural frequencies through vibration testing it is very dependent on technology such as the type of sensor and the way it is used to record the vibrations given. Sensor types consist of various types such as uniaxial, biaxial, and triaxial. In the case of vibration test the Donggala pier structure uses 6 uniaxial accelerometer sensors. Sensors are installed in 3 different places each in lateral and vertical directions. Test results from the surveyor in evaluating the results are only average without taking into account the influence of direction. This will be evaluated in this study. The evaluation will be done is to compare the results of vibration testing with numerical analysis. From various numerical analysis models, it can be seen that although the natural frequency values vary, they are still within certain limits. By looking at the direction of the placement of the sensor and the direction of the collision of the ship, it can be predicted the dynamic behavior of the pier whether translation or rotation is happening. Thus the evaluation used by the surveyors by averaging without looking at directions is incorrect. Therefore a re-evaluation will be conducted to study the direction of the force, the direction of the placement and placement of the accelerometer sensor and the need to group the results of vibration testing based on the sensor direction. After studying the numerical model from the dock, it can be seen that the numerical model that translates has conformity to the collision data at one of the installed sensor points. Numerical modeling which is close to this value is in accordance with previous planning data. From this research it can be seen that the understanding of vibration testing needs to be grouped according to the direction of the sensor placement and cannot be carried out on average.
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Purwar, Amit, Risto Myllyla, and Wan-Young Chung. "A Wireless Sensor Network Compatible Triaxial Accelerometer: Application for Detection of Falls in the Elderly." Sensor Letters 6, no. 2 (2008): 319–25. http://dx.doi.org/10.1166/sl.2008.044.
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Palamar, Mykhaylo, Taras Horyn, Andriy Palamar, and Vitaliy Batuk. "Method of calibration mems accelerometer and magnetometer for increasing the accuracy determination angular orientation of satellite antenna reflector." Scientific journal of the Ternopil national technical university 108, no. 4 (2022): 79–88. http://dx.doi.org/10.33108/visnyk_tntu2022.04.079.
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The paper is devoted to the measurement errors investigation that arise due to the influence of MEMS accelerometers' nonlinear characteristics. They appear at large inclination angles of the antenna system support-rotary platform, as well as in the presence of a magnetic inclination, which is due to the peculiarity of the Earth's magnetic field for the magnetometer. The study was conducted to assess the possibility of using such devices to increase the accuracy of a satellite antenna control with a classic rotary platform. The experimental setup for researching the parameters of MEMS sensors allows comparison of measurement results with data obtained from precision optical encoder. The experimental results show the main sources of MEMS sensors errors. An accuracy increasing method of antenna system angular position determining using a triaxial accelerometer and a magnetometer is proposed. The main advantage of the proposed estimation vector determining approach using the least squares method is the possibility of carrying out the calibration procedure without reference to the coordinate system. The method makes it possible to get rid of the zero offset error, as well as compensate for the non-unit scale of the sensor axes and the error of the magnetometer angular orientation. This method can be used for many applications including robotics, design of unmanned aerial vehicles and many other technical systems. The proposed method makes it possible to increase the reliability and reduce the cost of such systems.
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Tang, Xing, Suihuai Yu, Jianjie Chu, and Hao Fan. "Damaged/missing proximity sensor induces screen mistouch when answering calls: Prediction of smartphone answering status by posture data." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1963–74. http://dx.doi.org/10.3233/jifs-210646.
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When the proximity sensor of a smartphone is impaired, it would easily lead to screen mistouch during conversation, which will significantly affect the user experience. However, there are relatively few studies that have been focused on the quality of user experience following sensor impairment. The purpose of this study was to compare and evaluate different machine learning models in forecasting the user’s posture during a phone call, thereby providing a compensation approach for detecting proximity to the human ear during a phone call following sensor damage. The built-in accelerometer sensors of smartphones were employed to collect posture data while users were employing their smartphones. Three main postures (holding, moving and answering) were identified; the posture data were obtained through training and prediction using five machine learning models. The results showed that the model that utilized triaxial data had better prediction accuracy than the model that used single-axis data. Furthermore, models with time-domain features had a higher accuracy rate. Among the five models, neural networks had the best prediction accuracy (0.982). The proposed approach could be of immense benefit to the users following proximity sensor damage, and would be advantageous in the design of the smartphone, particularly in the early stages of the design process.
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Airaksinen, Manu, Okko Räsänen, and Sampsa Vanhatalo. "Trade-Offs Between Simplifying Inertial Measurement Unit–Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations." JMIR mHealth and uHealth 13 (June 3, 2025): e58078-e58078. https://doi.org/10.2196/58078.
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Abstract Background Human movement activity is commonly recorded with inertial measurement unit (IMU) sensors in many science disciplines. The IMU data can be used for an algorithmic detection of different postures and movements, which may support more detailed assessments of complex behaviors, such as daily activities. Studies on human behavior in real-life environments need to strike a balance between simplifying the recording settings and preserving sufficient analytic gains. It is poorly understood, however, what the trade-offs are between alternative recording configurations and the attainable analyses of naturalistic behavior at different levels of inspection, or with respect to achievable scientific questions. Objective This study assessed systematically the effects of IMU recording configurations (placement and number of IMU sensors, sampling frequency, and sensor modality) on the high temporal resolution detections of postures and movements, and on their lower temporal resolution derivative statistics when the data represents naturalistic daily activity without excessively repetitive movements. Methods We used a dataset from spontaneously moving infants (N=41; age range 4‐18 months) recorded with a multisensor wearable suit. The analysis benchmark was obtained using human annotations of postures and movements from a synchronously recorded video, and the reference IMU recording configuration included 4 IMU sensors collecting triaxial accelerometer and gyroscope modalities at 52 Hz. Then, we systematically tested how the algorithmic classification of postures (N=7), and movements (N=9), as well as their distributions and a derivative motor performance score, are affected by reducing IMU data sampling frequency, sensor modality, and sensor placement. Results Our results show that reducing the number of sensors has a significant effect on classifier performance, and the single sensor configurations were nonfeasible (posture classification Cohen kappa<0.75; movement<0.45). Reducing sensor modalities to accelerometer only, that is, dropping gyroscope data, leads to a modest reduction in movement classification performance (kappa=0.50-0.53). However, the sampling frequency could be reduced from 52 to 6 Hz with negligible effects on the classifications (posture kappa=0.90-0.92; movement=0.56-0.58). Conclusions The present findings highlight the significant trade-offs between IMU recording configurations and the attainability of sufficiently reliable analyses at different levels. Notably, the single-sensor recordings employed in most of the literature and wearable solutions are of very limited use when assessing the key aspects of real-world movement behavior at relevant temporal resolutions. The minimal configuration with an acceptable classifier performance includes at least a combination of one upper and one lower extremity sensor, at least 13 Hz sampling frequency, and at least an accelerometer, but preferably also a gyroscope (posture kappa=0.89-0.91; movement=0.50-0.53). These findings have direct implications for the design of future studies and wearable solutions that aim to quantify spontaneously occurring postures and movements in natural behaviors.
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Vizárraga, Jorge, Roberto Casas, Álvaro Marco, and J. David Buldain. "Dimensionality Reduction for Smart IoT Sensors." Electronics 9, no. 12 (2020): 2035. http://dx.doi.org/10.3390/electronics9122035.
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Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.