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Relevant bibliographies by topics / Triaxial accelerometer sensor

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Academic literature on the topic 'Triaxial accelerometer sensor'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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Journal articles on the topic "Triaxial accelerometer sensor"

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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2

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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Books on the topic "Triaxial accelerometer sensor"

1

Thomas, John E. Space acceleration measurement system triaxial sensor head error budget. National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Triaxial accelerometer sensor"

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Bernal-Polo, P., and H. Martínez-Barberá. "Triaxial Sensor Calibration: A Prototype for Accelerometer and Gyroscope Calibration." In ROBOT 2017: Third Iberian Robotics Conference. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70836-2_7.

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Rodríguez-Martín, Daniel, Albert Samà, Carlos Pérez-López, and Andreu Català. "Posture Transitions Identification Based on a Triaxial Accelerometer and a Barometer Sensor." In Advances in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59147-6_29.

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Radmanesh, Elahe, Mehdi Delrobaei, Oussama Habachi, Somayyeh Chamani, Yannis Pousset, and Vahid Meghdadi. "A Wearable IoT-Based Fall Detection System Using Triaxial Accelerometer and Barometric Pressure Sensor." In Ubiquitous Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58008-7_13.

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4

Ranakoti, Shubham, Shagneet Arora, Shweta Chaudhary, et al. "Human Fall Detection System over IMU Sensors Using Triaxial Accelerometer." In Computational Intelligence: Theories, Applications and Future Directions - Volume I. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_39.

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Conference papers on the topic "Triaxial accelerometer sensor"

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Nessle, S., M. bin Mansoor, R. Grumann, et al. "High-Performance Triaxial MEMS Accelerometer for Applications with Harsh Environmental Conditions." In 2024 DGON Inertial Sensors and Applications (ISA). IEEE, 2024. https://doi.org/10.1109/isa62769.2024.10786053.

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Kadokura, Miyuki, Tokio Kasai, Kazuhiro Watanabe, and Michiko Nishiyama. "Practical Triaxial Accelerometer Using a Base-Embedded Semicircular Hetero-Core Fiber-Optic Sensor." In Optical Fiber Sensors. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ofs.2023.tu3.89.

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We propose a base-embedded practical triaxial hetero-core fiber-optic accelerometer and evaluate the frequency and amplitude characteristics. The proposed design shows less transverse sensitivity than a conventional triaxial hetero-core fiber optic accelerometer.

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Wu, Zhongcheng, Yu Ge, and Zengfu Wang. "Monolithic triaxial accelerometer design in thick-film technology." In International Conference on Sensing units and Sensor Technology, edited by Yikai Zhou and Shunqing Xu. SPIE, 2001. http://dx.doi.org/10.1117/12.440205.

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Tsai, Yi-Lung, Ting-Ting Tu, Hyeoungho Bae, and Pai H. Chou. "EcoIMU: A Dual Triaxial-Accelerometer Inertial Measurement Unit for Wearable Applications." In 2010 International Conference on Body Sensor Networks (BSN). IEEE, 2010. http://dx.doi.org/10.1109/bsn.2010.47.

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Aiguang Li, Lianying Ji, Shaofeng Wang, and Jiankang Wu. "Physical activity classification using a single triaxial accelerometer based on HMM." In IET International Conference on Wireless Sensor Network 2010 (IET-WSN 2010). IET, 2010. http://dx.doi.org/10.1049/cp.2010.1045.

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Amit Purwar, Do Un Jeong, and Wan Young Chung. "Activity monitoring from real-time triaxial accelerometer data using sensor network." In 2007 International Conference on Control, Automation and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iccas.2007.4406764.

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Panagiota, Anstasopoulou, Shammas Layal, and Hey Stefan. "Assessment of Human Gait Speed and Energy Expenditure Using a Single Triaxial Accelerometer." In 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2012. http://dx.doi.org/10.1109/bsn.2012.7.

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Tolkiehn, Marie, Louis Atallah, Benny Lo, and Guang-Zhong Yang. "Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090120.

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Juhas, Brett D., Jessica M. Wong, Nicole J. Boroumand, and Paul H. Rigby. "Semi-Rigid Helmet Rotation Measurement Using Linear Accelerometers." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64677.

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The number of sensors placed on warfighters’ personal protective equipment (PPE) continues to increase each year. It is important to be able to accurately measure the dynamic response of PPE in order to characterize new sensors that are meant to track warfighter movement. In an effort to help predict head motion, a method has been developed to accurately measure the angular and linear acceleration of a semi-rigid helmet using four triaxial linear accelerometers. This four-accelerometer array configuration is based on the 3-2-2-2 nine accelerometer package (NAP) method and was tailored to accurately measure the helmet response during impact and blast overpressure events. Method development and testing were performed using U.S. Army Advanced Combat Helmets. Since angular motion calculation using the NAP method requires orthogonal sensor placement, it was necessary to revise the standard NAP sensor configuration to account for the geometric constraints of a helmet. Modal analysis was performed to determine the locations of least vibration, and shock tube and drop tests were conducted to investigate helmet flex during impacts. Knowledge concerning the dominant vibration modes of the helmet guided accelerometer placement and helped mitigate the effects of sensor data oscillation on the calculated angular motion. Local helmet deformation strongly depends on the impact site; several accelerometer array configurations were developed to account for various impact directions. Linear accelerations were measured and angular accelerations were calculated for guided free drop and shock tube tests in the laboratory. In guided free drop tests, the helmet and headform were dropped onto an anvil at various velocities and were allowed to freely bounce after impact. In shock tube tests, the helmet and headform were allowed to swing freely when subjected to a high shock wave simulating an IED blast. The modified NAP method was able to accurately measure the linear and angular acceleration of the helmet for both types of tests. The angular motion calculation was validated using a high-speed video camera recording the helmet response at 10,000 frames per second. Results were also compared to angular rate sensors available on the market. It was determined that with a detailed understanding of a semi-rigid body’s vibration and proper placement of linear accelerometers, angular acceleration during high-shock impacts can be accurately calculated for semi-rigid, irregular shaped objects. This accelerometer placement method has been applied to several other military grade helmets and been used in models predicting head motion from helmet motion data.

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Sakib, Mohammad, and Syeda Shanaz Pervez. "Automated Stress Level Detection for Hospital Nurses: A Single Triaxial Wearable Accelerometer Sensor System Approach." In 2023 20th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2023. http://dx.doi.org/10.1109/cce60043.2023.10332832.

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