Journal articles: 'Movement detection system'

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Kozhekin, Nikita, Ichiro Hagiwara, Mikhail Senin, and Vladimir Savchenko. "Simple CSRBF animation system with movement interpolation and collision detection." Proceedings of Design & Systems Conference 2004.14 (2004): 99–100. http://dx.doi.org/10.1299/jsmedsd.2004.14.99.

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Kozhekin, N., I. Hagiwara, M. Senin, and V. Savchenko. "Simple CSRBF animation system with movement interpolation and collosion detection." Proceedings of The Computational Mechanics Conference 2004.17 (2004): 213–14. http://dx.doi.org/10.1299/jsmecmd.2004.17.213.

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Nakano, T. "System for driver's eye movement detection." JSAE Review 16, no. 1 (January 1995): 74–76. http://dx.doi.org/10.1016/0389-4304(94)00054-w.

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Mohd Tamil, Emran, Ti Siang Tey, Mohd Rais Mustafa, Mohd Yamani Idna Idris, and Mohd Hairul Nizam Md Nasir. "Automated Clinical Research Mice Movement Detection System." International Journal of Technology, Knowledge, and Society 4, no. 6 (2008): 63–76. http://dx.doi.org/10.18848/1832-3669/cgp/v04i06/55953.

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Felisberto, Filipe, Rosalía Laza, Florentino Fdez-Riverola, and António Pereira. "A Distributed Multiagent System Architecture for Body Area Networks Applied to Healthcare Monitoring." BioMed Research International 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/192454.

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In the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors. At the same time, the availability of new biomedical sensors and suitable network protocols has led to the appearance of a new generation of wireless sensor networks, the so-called wireless body area networks. Nowadays, these networks are routinely used for continuous monitoring of vital parameters, movement, and the surrounding environment of people, but the large volume of data generated in different locations represents a major obstacle for the appropriate design, development, and deployment of more elaborated intelligent systems. In this context, we present an open and distributed architecture based on a multiagent system for recognizing human movements, identifying human postures, and detecting harmful activities. The proposed system evolved from a single node for fall detection to a multisensor hardware solution capable of identifying unhampered falls and analyzing the users’ movement. The experiments carried out contemplate two different scenarios and demonstrate the accuracy of our proposal as a real distributed movement monitoring and accident detection system. Moreover, we also characterize its performance, enabling future analyses and comparisons with similar approaches.

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Srikanth, K. "Alert System for Fall Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1739–47. http://dx.doi.org/10.22214/ijraset.2021.37658.

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Abstract: Healthcare is one of the most important industries, yet new ideas must travel a long way before being fully adopted due to its complexity, scope of duty, and stringent laws. The Internet of Things (IoT) may be the key to resolving healthcare challenges. The Internet of Things (IoT) has a lot of potential in healthcare, but it's still in its early stages. With the advancement of medical IoT, new possibilities for telemedicine, remote monitoring of a patient's status, and much more will emerge. Falling is a significant health danger for the elderly. If the problem is not detected in a timely manner, it can result in the death or impairment of the elderly, lowering their quality of life. Falls are a major public health concern for the elderly around the world. When it comes to old age, we must keep an eye on our loved ones to ensure their health and safety. It is therefore critical to determine if an elderly person has fallen so that help can be provided promptly. Proposing a person fall detection system based on a wearable device for detecting the falls of people in every situation, which takes advantage of lowpower wireless sensor networks, smart devices, and analyses human body motions. The system detects movement using an accelerometer and a gyro sensor. The sensor is wired to a microprocessor, which transmits the acceleration data continuously. Fall detection and sudden movement changes in the person would be monitored by the system. The sensors are getting values from a quick movement shift with shock in the system. When a person falls and becomes unconscious, the system determines whether the person has indeed fallen. If the person has truly fallen, the system will send an alert to the caregivers and sound an alarm to alert anyone nearby. When the system detects that a person has fallen, it immediately sends an alert to the individual's care takers. It is an IoT-based fall detection system that assists people by telling their caregivers about their fall so that quick attention may be drawn to the situation and essential actions can be taken to save the person who has fallen. Keywords: Threshold Based Fall Detection, Arduino, Bi-Axial, Accelerometer, Gyroscope,

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Che, Zhong-Yong, and Sangchul Kim. "A Surveillance System Using Images and Movement Detection Sensors." Journal of the Institute of Webcasting, Internet and Telecommunication 13, no. 1 (February 28, 2013): 181–89. http://dx.doi.org/10.7236/jiibc.2013.13.1.181.

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Baec, Sung-Ho, Min-Sik Jeon, and Bong-Jin Ko. "Implementation of Movement Detection System for Patient on Bed." Journal of Korea Navigation Institute 19, no. 5 (October 30, 2015): 458–63. http://dx.doi.org/10.12673/jant.2015.19.5.458.

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Lin, Chih-Lung, Wen-Ching Chiu, Ting-Ching Chu, Yuan-Hao Ho, Fu-Hsing Chen, Chih-Cheng Hsu, Ping-Hsiao Hsieh, et al. "Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements." Sensors 20, no. 20 (October 12, 2020): 5774. http://dx.doi.org/10.3390/s20205774.

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This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer’s head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick’s filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.

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Lin, Chin-Teng, Wei-Ling Jiang, Sheng-Fu Chen, Kuan-Chih Huang, and Lun-De Liao. "Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation." Biosensors 11, no. 9 (September 17, 2021): 343. http://dx.doi.org/10.3390/bios11090343.

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In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

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Lu, Liang-Hsuan, Female, Shang-Lin Chiang, Chueh-Ho Lin, and Wen-Hsu Sung. "Development And Clinical Application Of The Body Movement Detection System." Medicine & Science in Sports & Exercise 48 (May 2016): 798. http://dx.doi.org/10.1249/01.mss.0000487391.29700.c9.

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Komorkiewicz, Mateusz. "Real-Time Detection of Movement in Prohibited Direction for Video Surveillance System." Image Processing & Communications 17, no. 4 (December 1, 2012): 251–64. http://dx.doi.org/10.2478/v10248-012-0054-7.

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Abstract Video surveillance systems are well established tools for monitoring important areas and detecting abnormal situations. In places such as one way road or tunnel, airport arrival gate, subway entry gate etc. it is important to monitor the direction of movement and to detect those which are prohibited. If the event is detected in the same time when the situation happens, a fast reaction can fix the problem (turning on the red light to prevent cars from entering the tunnel, sending security force to stop and search the suspect etc.). In the article a working system which is able to detect movement in prohibited direction is presented. The algorithm proved a very good detection rate for tested movie sequences. By optimizing various aspects of the algorithm a real-time efficiency (30fps) for 640×480 resolution frames is achieved.

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Köhler, Anselm, Lai Bun Lok, Simon Felbermayr, Nial Peters, Paul V. Brennan, and Jan-Thomas Fischer. "mGEODAR—A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements." Sensors 20, no. 21 (November 9, 2020): 6373. http://dx.doi.org/10.3390/s20216373.

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Radar measurements of gravitational mass-movements like snow avalanches have become increasingly important for scientific flow observations, real-time detection and monitoring. Independence of visibility is a main advantage for rapid and reliable detection of those events, and achievable high-resolution imaging proves invaluable for scientific measurements of the complete flow evolution. Existing radar systems are made for either detection with low-resolution or they are large devices and permanently installed at test-sites. We present mGEODAR, a mobile FMCW (frequency modulated continuous wave) radar system for high-resolution measurements and low-resolution gravitational mass-movement detection and monitoring purposes due to a versatile frequency generation scheme. We optimize the performance of different frequency settings with loop cable measurements and show the freespace range sensitivity with data of a car as moving point source. About 15 dB signal-to-noise ratio is achieved for the cable test and about 5 dB or 10 dB for the car in detection and research mode, respectively. By combining continuous recording in the low resolution detection mode with real-time triggering of the high resolution research mode, we expect that mGEODAR enables autonomous measurement campaigns for infrastructure safety and mass-movement research purposes in rapid response to changing weather and snow conditions.

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Navada, Megha, Deepshikha Mishra, Saloni Parkar, Parag Patil, and Chaitanya Jage. "Early Stage Detection of Parkinson Disease." ITM Web of Conferences 40 (2021): 03050. http://dx.doi.org/10.1051/itmconf/20214003050.

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Parkinson’s disease is a chronic neurodegenerative condition that demonstrate the progressive loss of the ability to correlate movements mainly occurs in the elderly. For the purpose of monitoring tremors in Parkinson’s disease, a system has to be designed and developed. For coordination of movements, people with Parkinson’s, deprive of a chemical called dopamine which behaves as the messenger between the brain parts and the nervous system .Detecting Parkinson’s disease is a very arduous task as there is no evidence currently present to do this. Therefore, the main intention of our work is the designing of a system for recognizing Parkinson’s disease at an initial stage. An Android application is being designed that allows the status of PD patients to be assessed based on the tests found on the Unified Parkinson’s Disease Rating Scale approved by the Movement Disorders Society (MDS-UPDRS).

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Choudhury, Ananya, Anjan Kumar Talukdar, Manas Kamal Bhuyan, and Kandarpa Kumar Sarma. "Movement Epenthesis Detection for Continuous Sign Language Recognition." Journal of Intelligent Systems 26, no. 3 (July 26, 2017): 471–81. http://dx.doi.org/10.1515/jisys-2016-0009.

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AbstractAutomatic sign language recognition (SLR) is a current area of research as this is meant to serve as a substitute for sign language interpreters. In this paper, we present the design of a continuous SLR system that can extract out the meaningful signs and consequently recognize them. Here, we have used height of the hand trajectory as a salient feature for separating out the meaningful signs from the movement epenthesis patterns. Further, we have incorporated a unique set of spatial and temporal features for efficient recognition of the signs encapsulated within the continuous sequence. The implementation of an efficient hand segmentation and hand tracking technique makes our system robust to complex background as well as background with multiple signers. Experiments have established that our proposed system can identify signs from a continuous sign stream with a 92.8% spotting rate.

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Tolle, Herman, and Kohei Arai. "Design of Head Movement Controller System (HEMOCS) for Control Mobile Application through Head Pose Movement Detection." International Journal of Interactive Mobile Technologies (iJIM) 10, no. 3 (July 26, 2016): 24. http://dx.doi.org/10.3991/ijim.v10i3.5552.

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Head movement has been found to be a natural way of interaction. It can be used as an alternative control method and provides accessibility for users when used in human computer interface solutions. The combination of Head-mounted displays (HMDs) with mobile devices, provide an innovation of new low cost of human-computer interaction. Such devices are hands-free systems. In this paper, we introduce a new method for recognizing head movement as the controller of mobile application and proposed a new control system using head movement only. The proposed method can determine specific head pose movement and respond it as a controller. The implementation of a music player application on an iOS devices shows that the proposed method is appropriate for a new experience of real-time human-computer interaction with head movement control only.

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Frasetyo, Mohammad Brado, Elvira Sukma Wahyuni, and Hendra Setiawan. "Comparison of Motion History Image and Approximated Ellipse Method in Human Fall Detection System." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 2 (April 30, 2019): 199. http://dx.doi.org/10.22146/ijccs.43632.

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This paper compares two different method in human fall detection system namely motion history image and approximated ellipse. Research has been done in small studio with 4 CCTV camera as video data recorder, whereas video data are processed using MATLAB software. The experiment was carried out using three object’s fall direction and two type of falling movement. The fall direction is consist of front, side, and back fall. Whereas the falling movement is consist of direct and indirect fall movement. Meanwhile, the object’s initial position is standing and size of captured object is constant. The result is motion history image has accuracy 74.26% for direct falling movement, and 75.69% for indirect falling movement. Whereas approximated ellipse has accuracy 56.85% for direct falling movement, and 61.81% for indirect falling movement. Therefore, motion history image is better than approximated ellipse in human fall detection system.

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Mohammad Faisal Kholid, Jian Budiarto, Ahmad Ashril Rizal, and Gibran Satya Nugraha. "HUMAN MOVEMENT DETECTION DENGAN ACCUMULATIVE DIFFERENCES IMAGE." TEKNIMEDIA: Teknologi Informasi dan Multimedia 1, no. 1 (May 23, 2020): 1–7. http://dx.doi.org/10.46764/teknimedia.v1i1.7.

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Based on police data quoted from one of the online news portal pages, there are 43,842 thousand criminal acts in the The Capital City of Jakarta. Of all these criminal cases burglary empty houses included in the top three acts of crime. Houses that are abandoned by their owners are often targeted by crime operations due to lack of close supervision and security support technology. The purpose of this study is to detect human motion which can later be used to prevent crime in the form of theft. Another purpose of this research is to find out how the method used works in identifying changes in the image of several consecutive frames. This research develops a motion detection system in humans on video using a Closed Circuit Television (CCTV) camera which is simulated using sample video. The motion detection process uses the Accumulative Differences Image (ADI) method and the human detection process uses the classification of Opencv, the Haar Cascade Classification. Which with this method compares more than two different frames and the classification parameters used are full-body, upper body and face. System testing is done using several video samples taken with the distance and height of the camera against different objects. The results obtained from testing using video samples show an accuracy rate of 95.23%.

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Borghese, N. A., M. Di Rienzo, G. Ferrigno, and A. Pedotti. "ELITE: A goal oriented vision system for moving objects detection." Robotica 9, no. 3 (July 1991): 275–82. http://dx.doi.org/10.1017/s0263574700006433.

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SUMMARYA specially designed system for movement monitoring is here presented. The system has a two level architecture. At the first level, a hardware processor analyses in real-time the images provided by a set of standard TV cameras and, using a technique based on the convolution operator, recognizes in each frame objects that have a specific shape. The coordinates of these objects are fed to a computer, the second level of the system, that analyses the movement of these objects with the aid of a set of rules representing the knowledge of the context. The system was extensively tested on the field and the main results are reported.The whole system can work as a controlling device in robotics or as a general real-time image processor as well as an automatic movement analyser in biomechanics, orthopedic and neurological medicine.

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Liang, Sensong, Jiansheng Peng, and Yong Xu. "Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology." Journal of Healthcare Engineering 2021 (August 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/1745292.

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Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.

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Chavat, Juan, Sergio Nesmachnow, Andrei Tchernykh, and Vladimir Shepelev. "Active Safety System for Urban Environments with Detecting Harmful Pedestrian Movement Patterns Using Computational Intelligence." Applied Sciences 10, no. 24 (December 17, 2020): 9021. http://dx.doi.org/10.3390/app10249021.

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This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge of extracting relevant features of the processed images, by applying image processing and object tracking, and the second stage is responsible for the patterns detection. The experimental analysis of the proposed system was performed over more than 1450 problem instances, using PETS09-S2L1 videos, and the results were compared with part of the Multiple Object Tracking Challenge benchmark results. Experiments covered the two main stages of the system. Results indicate that the proposed system is competitive yet simpler than other similar software methods. Overall, this article provides the theoretical frame and a proof of concept needed for the implementation of a real-time system that takes as input a group of image sequences, extracts relevant features, and detects a set of predefined patterns. The proposed implementation is a reliable proof of the viability of building pedestrian movement pattern detection systems.

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Duan, Jing Li, Chun Fei Zhang, and Qiu Shuang Wang. "Research of Fatigue Driving Warning System Based on Android." Applied Mechanics and Materials 701-702 (December 2014): 400–404. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.400.

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This paper proposed a method for detecting fatigue detection Android smart phone system, and applied to the Android system. The system monitors the state of fatigue by smart phones photographs. Face detection method is used to localize the eyes of the driver and the eye region is extracted to monitor the movement of eyelids. An alarm rule is designed based on the PERCLOS standard to detect drowsy driving.Experiments show that the method is accurate rate, running speed, and can be used to monitor driver fatigue during the day.

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Mayekar, Vijay. "Crack Detection by Ultrasonic System on Locomotive-Track." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1910–16. http://dx.doi.org/10.22214/ijraset.2021.37681.

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Abstract: It has been found that major cause of the Railways accidents is due to the derailment attributed to the cracks developed on the rails. Crack development and its prolongation happens due to regular movement of locomotives, absence of timely detection. The maintenance lapse poses serious questions on the security of Railways transport operation. Manual detection of cracks on locomotive-tracks by maintenance team is arduous task, time consuming, irregular due to environmental factors and tight scheduling of trains, which may lead to maintenance work errors. This project aims at addressing the issue of crack detection by ultrasonic crack detection system, which would work in real time, with the control action taken to avoid major accident. Ultrasonic crack detection system is mounted on the locomotive itself, include precision ultrasonic sensor, LCD display, GPS module and its power supply. System will detect crack, subsequently it will generate Visual warning-signal for the authorities and generate location coordinates of the Crack. This will help for authority to generate caution signal for locomotives running on the same track for slow movement and emergency maintenance action could be undertaken. Keywords: locomotive-track, crack, ultrasonic Sensor, GPS Module, Arduino microcontroller

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Rodpongpun, Sura, Thapanan Janyalikit, and Chotirat Ann Ratanamahatana. "Influential Factors of an Asynchronous BCI for Movement Intention Detection." Computational and Mathematical Methods in Medicine 2020 (March 23, 2020): 1–12. http://dx.doi.org/10.1155/2020/8573754.

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In recent years, asynchronous brain computer interface (BCI) systems have been utilized in many domains such as robot controlling, assistive technology, and rehabilitation. In such BCI systems, movement intention detection algorithms are used to detect movement desires. In recent years, movement-related cortical potential (MRCP), an electroencephalogram (EEG) pattern representing voluntary movement intention, attracts wide attention in movement intention detection. Unfortunately, low MRCP detection accuracy makes the asynchronous BCI system impractical for real usage. In order to develop an effective MRCP detection algorithm, EEG data have to be properly preprocessed. In this work, we investigate the relationship and effects of three factors including frequency bands, spatial filters, and classifiers on MRCP classification performance to determine best settings. In particular, we performed a systematic performance investigation on combinations of five frequency bands, five spatial filters, and six classifiers. The EEG data were acquired from subjects performing series of self-paced ankle dorsiflexions. Analysis of variance (ANOVA) statistical test was performed on F1 scores to investigate effects of these three factors. The results show that frequency bands and spatial filters depend on each other. The combinations directly affect the F1 scores, so they have to be chosen carefully. The results can be used as guidelines for BCI researchers to effectively design a preprocessing method for an advanced asynchronous BCI system, which can assist the stroke rehabilitation.

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Vehkaoja, Antti, Mikko Peltokangas, Jarmo Verho, and Jukka Lekkala. "Combining the Information of Unconstrained Electrocardiography and Ballistography in the Detection of Night-Time Heart Rate and Respiration Rate." International Journal of Monitoring and Surveillance Technologies Research 1, no. 3 (July 2013): 52–67. http://dx.doi.org/10.4018/ijmstr.2013070104.

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An unobtrusive bed integrated system for monitoring physiological parameters during sleep is presented and evaluated. The system uses textile electrodes attached to a bed sheet for measuring multiple channels of electrocardiogram. The channels are also combined in order to form several additional ECG leads. One lead at a time is selected for beat-to-beat-interval detection. The system also includes force sensors located under a bed post for detecting respiration and movements. The movement information is also used to assist in heart rate detection and combining the ECG derived respiration information with respiration information derived from force sensors, is investigated. The authors tested the system with ten subjects in one hour recordings and achieved an average of 95.9% detection coverage and 99 percentile absolute error of 3.47 ms for the BB-interval signal. The relative mean absolute error of the detected respiration cycle lengths was 2.1%.

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刘, 倩. "Design of Intelligent Fetal Movement Detection System Based on Bluetooth Transmission." Instrumentation and Equipments 07, no. 04 (2019): 228–38. http://dx.doi.org/10.12677/iae.2019.74031.

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Nishi, Eri, Yoshiyuki Ueno, and Takuya Niikawa. "Construction of Tongue Movement Detection System using Infant Toy during Sucking." IEEJ Transactions on Electronics, Information and Systems 138, no. 1 (2018): 88–89. http://dx.doi.org/10.1541/ieejeiss.138.88.

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Tan, Bo, Karl Woodbridge, and Kevin Chetty. "Awireless passive radar system for real-time through-wall movement detection." IEEE Transactions on Aerospace and Electronic Systems 52, no. 5 (October 2016): 2596–603. http://dx.doi.org/10.1109/taes.2016.140207.

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Bowman, M. E., and A. K. Forrest. "Visual detection of differential movement: applications to robotics." Robotica 6, no. 1 (January 1988): 7–12. http://dx.doi.org/10.1017/s0263574700003532.

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SUMMARYA method has been developed for accurately determining the differential movement of known objects from multiple camera views. The method has been applied to a robot System to find the repeatability and accuracy of the robot in both rotational and translational terms and also for tracking an object using visual feedback.

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Wang, Hao, and Dian Ren Chen. "Radar Non-Contact Life Parameter Detection System." Advanced Materials Research 1044-1045 (October 2014): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.833.

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The main principle of radar non - contact life detection technology is the use of electromagnetic wave by the human body, the echo signal is modulated by the surface movement caused the life activities of the human body, so that some parameters of the return signal change. The research on the non contact detection technology, completed the construction of life monitoring system module of hardware, design of software algorithm, real-time data acquisition, and the use of special software algorithms, to detect these changes, and then extracting parameters of human life.

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Liang, Wenyuan. "Mechanical Design and Control Strategy for Hip Joint Power Assisting." Journal of Healthcare Engineering 2018 (August 15, 2018): 1–7. http://dx.doi.org/10.1155/2018/9712926.

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The basic requirements for mechanical design and control strategy are adapting to human joint movements and building an interaction model between human and robot. In this paper, a 3-UPS parallel mechanism is adopted to realize that the instantaneous rotation center of the assistive system coincides with human joint movement center, and a force sensory system is used to detect human movement intention and build the modeling of control strategy based on the interactive force. Then, based on the constructed experimental platform, the feasibility of movement intention detection and power assisting are verified through the experimental results.

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Wiegandt, Felix C., David Biegger, Jacob F. Fast, Grzegorz Matusiak, Jan Mazela, Tobias Ortmaier, Theodor Doll, Andreas Dietzel, Bettina Bohnhorst, and Gerhard Pohlmann. "Detection of Breathing Movements of Preterm Neonates by Recording Their Abdominal Movements with a Time-of-Flight Camera." Pharmaceutics 13, no. 5 (May 14, 2021): 721. http://dx.doi.org/10.3390/pharmaceutics13050721.

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In order to deliver an aerosolized drug in a breath-triggered manner, the initiation of the patient’s inspiration needs to be detected. The best-known systems monitoring breathing patterns are based on flow sensors. However, due to their large dead space volume, flow sensors are not advisable for monitoring the breathing of (preterm) neonates. Newly-developed respiratory sensors, especially when contact-based (invasive), can be tested on (preterm) neonates only with great effort due to clinical and ethical hurdles. Therefore, a physiological model is highly desirable to validate these sensors. For developing such a system, abdominal movement data of (preterm) neonates are required. We recorded time sequences of five preterm neonates’ abdominal movements with a time-of-flight camera and successfully extracted various breathing patterns and respiratory parameters. Several characteristic breathing patterns, such as forced breathing, sighing, apnea and crying, were identified from the movement data. Respiratory parameters, such as duration of inspiration and expiration, as well as respiratory rate and breathing movement over time, were also extracted. This work demonstrated that respiratory parameters of preterm neonates can be determined without contact. Therefore, such a system can be used for breathing detection to provide a trigger signal for breath-triggered drug release systems. Furthermore, based on the recorded data, a physiological abdominal movement model of preterm neonates can now be developed.

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Harris, Laurence R., and Lori A. Lott. "Sensitivity to full-field visual movement compatible with head rotation: Variations with eye-in-head position." Visual Neuroscience 13, no. 2 (March 1996): 277–82. http://dx.doi.org/10.1017/s0952523800007513.

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AbstractVariations in velocity detection thresholds for full-field visual rotation about various axes are compatible with a simple channel-based system for coding the axis and velocity of the rotation (Harris & Lott, 1995). The present paper looks at the frame of reference for this system. The head-centered, craniotopic reference system and the retinal-based, retinotopic reference systems were separated by using eccentric eye positions. We measured the threshold for detecting full-field visual rotation about a selection of axes in the sagittal plane with the eyes held either 22½ degs up, straight ahead or 22½ degs down in the head. The characteristic features of the variation in detection thresholds did not stay stable in craniotopic coordinates but moved with the eyes and were constant in retinotopic coordinates. This suggests that the coding of head rotation by the visual system is in retinotopic coordinates.

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Tawk, Youssef, Aleksandar Jovanovic, Phillip Tomé, Jérôme Leclère, Cyril Botteron, Pierre-André Farine, Ruud Riem-Vis, and Bertrand Spaeth. "A New Movement Recognition Technique for Flight Mode Detection." International Journal of Vehicular Technology 2013 (January 30, 2013): 1–18. http://dx.doi.org/10.1155/2013/149813.

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Nowadays, in the aeronautical environments, the use of mobile communication and other wireless technologies is restricted. More specifically, the Federal Communications Commission (FCC) and the Federal Aviation Administration (FAA) prohibit the use of cellular phones and other wireless devices on airborne aircraft because of potential interference with wireless networks on the ground, and with the aircraft's navigation and communication systems. Within this context, we propose in this paper a movement recognition algorithm that will switch off a module including a GSM (Global System for Mobile Communications) device or any other mobile cellular technology as soon as it senses movement and thereby will prevent any forbidden transmissions that could occur in a moving airplane. The algorithm is based solely on measurements of a low-cost accelerometer and is easy to implement with a high degree of reliability.

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Ali, Asad, Sanaul Hoque, and Farzin Deravi. "Directed Gaze Trajectories for Biometric Presentation Attack Detection." Sensors 21, no. 4 (February 17, 2021): 1394. http://dx.doi.org/10.3390/s21041394.

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Presentation attack artefacts can be used to subvert the operation of biometric systems by being presented to the sensors of such systems. In this work, we propose the use of visual stimuli with randomised trajectories to stimulate eye movements for the detection of such spoofing attacks. The presentation of a moving visual challenge is used to ensure that some pupillary motion is stimulated and then captured with a camera. Various types of challenge trajectories are explored on different planar geometries representing prospective devices where the challenge could be presented to users. To evaluate the system, photo, 2D mask and 3D mask attack artefacts were used and pupillary movement data were captured from 80 volunteers performing genuine and spoofing attempts. The results support the potential of the proposed features for the detection of biometric presentation attacks.

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G., Dr Ranganathan. "Real Life Human Movement Realization in Multimodal Group Communication Using Depth Map Information and Machine Learning." Journal of Innovative Image Processing 2, no. 2 (June 6, 2020): 93–101. http://dx.doi.org/10.36548/jiip.2020.2.004.

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The latest advancements in the evolution of depth map information’s has paved way for interesting works like object recognition sign detection and human movement detection etc. The real life human movement detection or their activity identification is very challenging and tiresome. Since the real life activities of the humans could be of much interest in almost all areas, the subject of identifying the human activities has gained significance and has become a most popular research field. Identifying the human movements /activities in the public places like airport, railways stations, hospital, home for aged become very essential due to the several benefits incurred form the human movement recognition system such as surveillance camera, monitoring devices etc. since the changes in the space and the time parameters can provide an effective way of presenting the movements, yet in the case of natural color vision, as the flatness is depicted in almost all portions of images. So the work laid out in the paper in order to identify the human movement in the real life employs the space and the time depth particulars (Spatial-Temporal depth details –STDD) and the random forest in the final stage for movement classification. The technology put forth utilize the Kinect sensors to collecting the information’s in the data gathering stage. The mechanism laid out to identify the human movements is test with the MATLAB using the Berkley and the Cornell datasets. The mechanism proposed through the acquired results proves to deliver a better performance compared to the human movements captured using the normal video frames.

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Ishikawa, Tadashi, and Yusuke Yokota. "Detection of Seafloor Movement in Subduction Zones Around Japan Using a GNSS-A Seafloor Geodetic Observation System from 2013 to 2016." Journal of Disaster Research 13, no. 3 (June 1, 2018): 511–17. http://dx.doi.org/10.20965/jdr.2018.p0511.

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In Japan, interplate megathrust earthquakes frequently occur in subduction zones where oceanic plates subduct beneath continental plates, and it is important to elucidate the physical mechanism involved in such earthquakes to prevent associated disasters. Crustal movement data provide essential information to understand plate motion and earthquake source processes. We developed a system that combines GNSS measurements and acoustic ranging techniques to detect seafloor crustal movement. This paper reports the acquisition of recent seafloor crustal movements obtained during campaign observations with a survey vessel, from 2013 to 2016.

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Ye, Ning, Yu Ge Sun, and Dan Yang. "Noninvasive Heart Rate Variability Detection Device for Fatigue Driving Detection System." Applied Mechanics and Materials 246-247 (December 2012): 194–98. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.194.

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Monitor psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. Heart rate variability (HRV) has its particular advantage comparing with other methods, such as its accuracy and conveniences. ECG is a regular signal to obtain HRV, but during driving condition, electrodes and wires need to be placed on driver’s body and may disturb the driver’s normal driving behavior. Since ballistocardiogram (BCG) can reflect heart movement, so HRV can also be extracted from BCG. This paper gives a novel noninvasive method to detect driver’s BCG. Using PVDF sensor which is embedded in safety belt to get driver's BCG and designing hardware and software to amplify and convert it to digital signal for next processing. The result shows that the proposed device can obtain the driver’s BCG properly and the HRV of the driver can be calculated accurately and conveniently, so the design is reasonable.

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Kutílek, Patrik, David Škoda, Ján Hýbl, Rudolf Černý, Daniel Frynta, Eva Landová, Petra Frýdlová, Aniko Kurali, Radek Doskočil, and Václav Křivánek. "PLATFORM WITH CAMERA SYSTEM FOR MEASUREMENT OF COMPENSATORY MOVEMENTS OF SMALL ANIMALS." Acta Polytechnica 57, no. 5 (October 31, 2017): 321–30. http://dx.doi.org/10.14311/ap.2017.57.0321.

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The article introduces systems and methods of a controllable rotational platform used for measuring compensatory movement of small animals. The system, based on a camera subsystem, is located on a mechanical platform powered by a set of three actuators. The subsystems and methods allow to measure angles of the platform’s orientation in space and body segment angles in both anatomical and Earth’s coordinate systems. The methods of video processing, selection of measurement parameters and detection of anatomical angles are thoroughly described in this article. The study also deals with the software designed in MatLab®, which controls the platform, records and processes videos, and obtains angles for the movement analysis. The system was tested for measuring a head rotation of a small reptile/amphibian and monitored reflective markers on the creature’s body by the camera system. This method has never been described before. The new subsystems of the platform and methods for monitoring animal’s head compensatory movements can be used in studies of the neural system and its evolution.

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LIU, JIANLI, XUWEN LI, SONG ZHANG, QIANG ZHANG, LIN YANG, YIMIN YANG, and DONGMEI HAO. "FETAL MOVEMENT SIGNAL DETECTION METHOD BASED ON MULTIPLE PRESSURE SENSORS." Journal of Mechanics in Medicine and Biology 21, no. 05 (April 17, 2021): 2140024. http://dx.doi.org/10.1142/s0219519421400248.

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Objective: Fetal movement (FM) is one of the objective phenomena of fetal life. The decrease of FM is a clinical indicator of fetal hypoxia. A fetal motion detection method is based on multiple pressure sensors and multi features. Methods: Collecting the abdominal data of pregnant women using multi-pressure sensors, preprocessing the signal with a digital filter, extracting the time–frequency characteristics of FM signal. The designed classifier is used for recognition of the extracted features. Results: The detection system used for FM time series can provide reliable results with a detection rate of 93.0% and a positive rate is 75.9%. Conclusion: The portable detection system proposed in this paper is a good alternative that will optimize medical use, professionals and hospital resources and has potential application prospects in the field of home electronic medical treatment.

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Venskus, Julius, Povilas Treigys, Jolita Bernatavičienė, Gintautas Tamulevičius, and Viktor Medvedev. "Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding." Sensors 19, no. 17 (August 31, 2019): 3782. http://dx.doi.org/10.3390/s19173782.

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The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

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Gu, Jason J., Max Meng, Albert Cook, and Peter Xiaoping Liu. "Intelligent Sensor Fusion in Robotic Prosthetic Eye System." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 3 (May 20, 2004): 313–23. http://dx.doi.org/10.20965/jaciii.2004.p0313.

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This paper is concerned with the design, sensing and control of a robotic prosthetic eye that moves horizontally in synchronization with the movement of the natural eye. Two generations of robotic prosthetic eye models have been developed. Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. Simulation and experimental studies are performed and the results are included to demonstrate the effectiveness of the research project reported in this paper.

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Hoshino, Satoshi, and Kyohei Niimura. "Robot Vision System for Human Detection and Action Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 3 (May 20, 2020): 346–56. http://dx.doi.org/10.20965/jaciii.2020.p0346.

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Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow.

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Sakashita, Yuusuke, Hironobu Fujiyoshi, Yutaka Hirata, Hisanori Takamaru, and Naoki Fukaya. "Real-time Measurement System of Cycloduction Movement Based on Fast Ellipse Detection." IEEJ Transactions on Electronics, Information and Systems 127, no. 4 (2007): 591–98. http://dx.doi.org/10.1541/ieejeiss.127.591.

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Jiang, Qifeng, Bin Gao, and Yang Li. "Development of Portable Monitoring System for Real-Time Detection of Fetal Movement." Advances in Bioscience and Biotechnology 09, no. 08 (2018): 380–89. http://dx.doi.org/10.4236/abb.2018.98026.

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Schmid, A. "Neurotransmitters involved in movement detection in the visual system of an arthropod." Naturwissenschaften 79, no. 12 (December 1992): 564–67. http://dx.doi.org/10.1007/bf01131414.

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Andreatta, Richard D., and Steven M. Barlow. "Movement-related modulation of vibrotactile detection thresholds in the human orofacial system." Experimental Brain Research 149, no. 1 (December 19, 2002): 75–82. http://dx.doi.org/10.1007/s00221-002-1336-x.

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Yang, Juechen, Jun Kong, and Chunying Zhao. "A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques." Sensors 21, no. 5 (February 28, 2021): 1665. http://dx.doi.org/10.3390/s21051665.

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The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.

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Lowry, Michael, Alistair Becker, Heath Folpp, James McLeod, and Matthew D. Taylor. "Residency and movement patterns of yellowfin bream (Acanthopagrus australis) released at natural and artificial reef sites." Marine and Freshwater Research 68, no. 8 (2017): 1479. http://dx.doi.org/10.1071/mf16351.

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The present study investigated the long-term (>2 years) site fidelity, residency and movement patterns of Acanthopagrus australis (Sparidae) at artificial (AR) and natural reef (NR) sites. Acoustic telemetry was used to assess movement patterns of 39 fish released at NR and AR locations and other habitat types within the study area. Detection periods ranged from 1 day to a maximum of 912 days, with 36% of fish detected by the array for >1 year and a further 7% detected for >2 years. Results indicate that tagged fish tended to remain associated with the release site; however, AR fish were detected for considerably longer periods with greater numbers of fish identified as resident within the AR system. AR-released fish were also identified more frequently across the entire array, with the majority (90%) of detections between receiver stations located within the AR system. Results were affected by short detection periods (<6 days) of a relatively high proportion of fish released at the NR, possibly indicating differential rates of fishing mortality between locations. Longer range movements of >200km were also detected, but there was no obvious trend with release location. The results of the present study indicate interactions between existing and introduced artificial habitat are more complex than a ‘draw-down’ effect and provide further evidence that AR systems provide suitable habitat for a variety of species, as well as further support for the use of AR systems in fisheries enhancement initiatives. The results also have important implications for understanding the effect of AR systems and indicate that the size of the reef system may be an important factor in controlling for levels of fishing-related mortality.

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Rank, Markus, Zhuanghua Shi, and Sandra Hirche. "Perception of Delay in Haptic Telepresence Systems." Presence: Teleoperators and Virtual Environments 19, no. 5 (October 1, 2010): 389–99. http://dx.doi.org/10.1162/pres_a_00021.

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Time delay is recognized as an important issue in haptic telepresence systems as it is inherent to long-distance data transmission. What factors influence haptic delay perception in a time-delayed environment are, however, largely unknown. In this article, we examine the impact of manual movement frequency and amplitude in a sinusoidal exploratory movement as well as the stiffness of the haptic environment on the detection threshold for delay in haptic feedback. The results suggest that the detection of delay in force feedback depends on the movement frequency and amplitude, while variation of the absolute feedback force level does not influence the detection threshold. A model based on the exploration movement is proposed and guidelines for system design with respect to the time delay in haptic feedback are provided.

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Journal articles on the topic 'Movement detection system'

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