Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936034
André Stollenwerk, F. Sehl, G. Marx, S. Kowalewski, Thorsten Janisch
Decompression algorithms in hyperbaric applications currently usually base on information about the ambient pressure in a temporal course. However, the impact of other factors like temperature or physical activity is well documented in literature. Therefore, we elaborated a prototypic setup, which is not only able to enrich the decompression algorithms run on a diving computer by this data, but also store this information for successive data mining.
{"title":"Enrichment of a diving computer with body sensor network data","authors":"André Stollenwerk, F. Sehl, G. Marx, S. Kowalewski, Thorsten Janisch","doi":"10.1109/BSN.2017.7936034","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936034","url":null,"abstract":"Decompression algorithms in hyperbaric applications currently usually base on information about the ambient pressure in a temporal course. However, the impact of other factors like temperature or physical activity is well documented in literature. Therefore, we elaborated a prototypic setup, which is not only able to enrich the decompression algorithms run on a diving computer by this data, but also store this information for successive data mining.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122143876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936006
Zhi-Bo Wang, Lin Yang, Zhipei Huang, Jiankang Wu, Zhiqiang Zhang, Lixin Sun
Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.
{"title":"Human motion tracking based on complementary Kalman filter","authors":"Zhi-Bo Wang, Lin Yang, Zhipei Huang, Jiankang Wu, Zhiqiang Zhang, Lixin Sun","doi":"10.1109/BSN.2017.7936006","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936006","url":null,"abstract":"Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"328 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125329277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7935997
Enzo Mastinu, B. Håkansson, M. Ortiz-Catalán
Bioelectric potentials provide an intuitive source of control in human-machine interfaces. In this work, a low-cost system for bioelectric signals acquisition and processing was developed and made available as open source. A single module based on the ADS1299 (Texas Instruments, USA) can acquire up to 8 differential or single-ended channels with a resolution of 24 bits and programmable gain up to 24 V/V. Several modules can be daisy-chained together to increase the number of input channels. Opto-isolated USB communication was included in the design to interface safely with a personal computer. The system was designed to be compatible with a low-cost and widely available microcontroller development platform, namely the Tiva LaunchPad (Texas Instruments, USA) featuring an ARM Cortex-M4 core. We made the source files for the PCB, firmware, and high-level software available online (GitHub: ADS_BP). Digital processing was used for float conversion and filtering. The high-level software for control and acquisition was integrated into an already existent open source platform for advanced myoelectric control, namely BioPatRec. This integration provide a complete system for intuitive myoelectric control where signal processing, machine learning, and control algorithms are used for the prediction of motor volition and control of robotic and virtual devices.
{"title":"Low-cost, open source bioelectric signal acquisition system","authors":"Enzo Mastinu, B. Håkansson, M. Ortiz-Catalán","doi":"10.1109/BSN.2017.7935997","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935997","url":null,"abstract":"Bioelectric potentials provide an intuitive source of control in human-machine interfaces. In this work, a low-cost system for bioelectric signals acquisition and processing was developed and made available as open source. A single module based on the ADS1299 (Texas Instruments, USA) can acquire up to 8 differential or single-ended channels with a resolution of 24 bits and programmable gain up to 24 V/V. Several modules can be daisy-chained together to increase the number of input channels. Opto-isolated USB communication was included in the design to interface safely with a personal computer. The system was designed to be compatible with a low-cost and widely available microcontroller development platform, namely the Tiva LaunchPad (Texas Instruments, USA) featuring an ARM Cortex-M4 core. We made the source files for the PCB, firmware, and high-level software available online (GitHub: ADS_BP). Digital processing was used for float conversion and filtering. The high-level software for control and acquisition was integrated into an already existent open source platform for advanced myoelectric control, namely BioPatRec. This integration provide a complete system for intuitive myoelectric control where signal processing, machine learning, and control algorithms are used for the prediction of motor volition and control of robotic and virtual devices.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121684347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936023
Junkai Xu, Ung Hee Lee, T. Bao, Yangjian Huang, K. Sienko, P. Shull
Gait retraining is an important rehabilitation method for re-establishing health gait patterns resulting from disease or injury. Optical marker-based motion capture systems are effective for sensing but aren't used widely, due to cost and lack of portability. Moreover, to perform gait retraining, feedback is needed in addition to sensing. This paper presents a wearable sensing and haptic feedback research platform for gait retraining. The platform contains eight distributed nodes (Dots) and a central control unit (Hub) that wirelessly connects to the Dots. Each Dot provides 9-axis inertial sensing and can be configured for sensing or/and providing vibrotactile feedback according to movement training requirements. The Hub receives the sensor data, performs algorithm computation and distributes feedback commands based on the feedback strategy. A foot progression angle (FPA) gait retraining task was performed by six healthy older adults. Participants used the wearable system to learn toe-in gait (foot pointing more inward) and toe-out gait (foot pointing more outward) by adjusting their FPA based on haptic cues to fall within the no feedback zone, i.e. the desired range of acceptable FPAs. After gait retraining, FPA during toe-in gait (1.8±5.6 deg) was significantly higher than during baseline walking (−4.3±5.1 deg) (p<0.01) and during toe-out gait (−9.9±3.2 deg) (p<0.01). The no feedback zone was easily found by participants as the percentage of time with no feedback for toe-in gait was 68.3%, and for toe-out gait it was 89.4%. This work demonstrates that the wearable system can be an effective gait retraining research platform.
{"title":"Wearable sensing and haptic feedback research platform for gait retraining","authors":"Junkai Xu, Ung Hee Lee, T. Bao, Yangjian Huang, K. Sienko, P. Shull","doi":"10.1109/BSN.2017.7936023","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936023","url":null,"abstract":"Gait retraining is an important rehabilitation method for re-establishing health gait patterns resulting from disease or injury. Optical marker-based motion capture systems are effective for sensing but aren't used widely, due to cost and lack of portability. Moreover, to perform gait retraining, feedback is needed in addition to sensing. This paper presents a wearable sensing and haptic feedback research platform for gait retraining. The platform contains eight distributed nodes (Dots) and a central control unit (Hub) that wirelessly connects to the Dots. Each Dot provides 9-axis inertial sensing and can be configured for sensing or/and providing vibrotactile feedback according to movement training requirements. The Hub receives the sensor data, performs algorithm computation and distributes feedback commands based on the feedback strategy. A foot progression angle (FPA) gait retraining task was performed by six healthy older adults. Participants used the wearable system to learn toe-in gait (foot pointing more inward) and toe-out gait (foot pointing more outward) by adjusting their FPA based on haptic cues to fall within the no feedback zone, i.e. the desired range of acceptable FPAs. After gait retraining, FPA during toe-in gait (1.8±5.6 deg) was significantly higher than during baseline walking (−4.3±5.1 deg) (p<0.01) and during toe-out gait (−9.9±3.2 deg) (p<0.01). The no feedback zone was easily found by participants as the percentage of time with no feedback for toe-in gait was 68.3%, and for toe-out gait it was 89.4%. This work demonstrates that the wearable system can be an effective gait retraining research platform.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122251366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936028
L. Eudave, M. Valencia
In this pilot study, we aimed to explore the sense of presence and the physiological response evoked by an immersive virtual environment by using a modern head-mounted display (HMD) while performing a driving task in our simulator. We found an increase in mean whole session electrodermal activity (EDA) and heart rate (HR) as well as during emergency maneuvers events, showing a stronger response and more extended response when driving in the immersive simulation when compared with the standard simulation. At this proof of concept phase we were able to register and detect physiological signal differences between display modalities, suggesting deeper sense of presence when driving in an immersive environment.
{"title":"Physiological response while driving in an immersive virtual environment","authors":"L. Eudave, M. Valencia","doi":"10.1109/BSN.2017.7936028","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936028","url":null,"abstract":"In this pilot study, we aimed to explore the sense of presence and the physiological response evoked by an immersive virtual environment by using a modern head-mounted display (HMD) while performing a driving task in our simulator. We found an increase in mean whole session electrodermal activity (EDA) and heart rate (HR) as well as during emergency maneuvers events, showing a stronger response and more extended response when driving in the immersive simulation when compared with the standard simulation. At this proof of concept phase we were able to register and detect physiological signal differences between display modalities, suggesting deeper sense of presence when driving in an immersive environment.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134382328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936018
Alexander P. Welles, David P. Looney, W. Rumpler, M. Buller
Metabolic energy expenditure is a physiological measure of importance to multiple scientific fields including nutrition, athletic performance, and thermoregulatory modeling. However, measuring metabolic rate in non-laboratory settings is difficult due to the restrictions imposed by laboratory grade measurement methods. The use of probabilistic graphical models, a type of machine learning model, may provide a means to estimate hidden variables such as metabolic rate from more easily observed variables such as heart rate and core body temperature. Using a probabilistic graphical model approach, a particle filter was applied to estimate metabolic rate from continuous heart rate and core body temperature observations. This paper examines which set of observations allows the particle filter to make more accurate estimations of metabolic rate and whether or not the addition of change in metabolic rate as a state variable improves accuracy. Observation and state parameters were learned by linear regression from continuous heart rate, core temperature, and metabolic rate collected from 15 volunteers (age: 23 ± 3 yr, ± SD) over N = 24, 3-hour periods during which 1 hour was spent running up to 8 km distance. State segmentations were learned using k-means clustering with up to 10 states. Observations of heart rate alone and with core temperature were used to predict metabolic rate with a root mean square error ± standard deviation of 166 ± 27 W and 133 ± 26 W.
{"title":"Estimating human metabolic energy expenditure using a bootstrap particle filter","authors":"Alexander P. Welles, David P. Looney, W. Rumpler, M. Buller","doi":"10.1109/BSN.2017.7936018","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936018","url":null,"abstract":"Metabolic energy expenditure is a physiological measure of importance to multiple scientific fields including nutrition, athletic performance, and thermoregulatory modeling. However, measuring metabolic rate in non-laboratory settings is difficult due to the restrictions imposed by laboratory grade measurement methods. The use of probabilistic graphical models, a type of machine learning model, may provide a means to estimate hidden variables such as metabolic rate from more easily observed variables such as heart rate and core body temperature. Using a probabilistic graphical model approach, a particle filter was applied to estimate metabolic rate from continuous heart rate and core body temperature observations. This paper examines which set of observations allows the particle filter to make more accurate estimations of metabolic rate and whether or not the addition of change in metabolic rate as a state variable improves accuracy. Observation and state parameters were learned by linear regression from continuous heart rate, core temperature, and metabolic rate collected from 15 volunteers (age: 23 ± 3 yr, ± SD) over N = 24, 3-hour periods during which 1 hour was spent running up to 8 km distance. State segmentations were learned using k-means clustering with up to 10 states. Observations of heart rate alone and with core temperature were used to predict metabolic rate with a root mean square error ± standard deviation of 166 ± 27 W and 133 ± 26 W.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132390695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936035
Guglielmo Cola, M. Avvenuti, Fabio Musso, Alessio Vecchio
Wrist-worn devices, such as smartwatches and smart bands, have brought about the unprecedented opportunity to continuously monitor gait during daily routines. However, the use of a single wrist-worn unit for gait analysis is challenging for a variety of reasons. Indeed, the signal collected at the user's wrist is subject to a significant “noise” with respect to other body positions (e.g. waist), mainly due to the arm swing while walking and other unpredictable hand movements. The aim of this paper is to investigate the design and evaluation of a lightweight and reliable gait detection technique for wrist-worn devices. To this end, the proposed method creates a personalized model of the user's gait patterns. The model is created through an automatic training phase, which requires the temporary use of an additional device (smartphone) to gather true gait segments. After, anomaly detection is used to distinguish gait from other activities. Gait data from 20 volunteers have been collected to test and evaluate the proposed technique. Volunteers were asked to walk at different pace, with their normal arm swing or placing the hand inside of a pocket. Results show that the proposed method can reliably distinguish gait from spurious hand movements.
{"title":"Personalized gait detection using a wrist-worn accelerometer","authors":"Guglielmo Cola, M. Avvenuti, Fabio Musso, Alessio Vecchio","doi":"10.1109/BSN.2017.7936035","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936035","url":null,"abstract":"Wrist-worn devices, such as smartwatches and smart bands, have brought about the unprecedented opportunity to continuously monitor gait during daily routines. However, the use of a single wrist-worn unit for gait analysis is challenging for a variety of reasons. Indeed, the signal collected at the user's wrist is subject to a significant “noise” with respect to other body positions (e.g. waist), mainly due to the arm swing while walking and other unpredictable hand movements. The aim of this paper is to investigate the design and evaluation of a lightweight and reliable gait detection technique for wrist-worn devices. To this end, the proposed method creates a personalized model of the user's gait patterns. The model is created through an automatic training phase, which requires the temporary use of an additional device (smartphone) to gather true gait segments. After, anomaly detection is used to distinguish gait from other activities. Gait data from 20 volunteers have been collected to test and evaluate the proposed technique. Volunteers were asked to walk at different pace, with their normal arm swing or placing the hand inside of a pocket. Results show that the proposed method can reliably distinguish gait from spurious hand movements.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134104554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936043
Xiao Lu, M. Qasaimeh
This paper considers a microelectromechanical system (MEMS)-based surface-micromachined poly-silicon germanium (poly-SiGe) thermopile, and examines the effects of the geometry of an air tunnel beneath the thermocouple on the effectiveness of its efficiency in energy harvesting. Intended to increase voltage output by enhancing thermal isolation between the two ends of a thermocouple, the air tunnel consists of the upper and lower parts. The upper tunnel results from the elevation of the semiconductor leg and the lower tunnel results from etching the silicon substrate. In our finite element analysis, we parametrized the depth and width of the lower tunnel and the elevation of the upper tunnel to examine the resulting voltage output. We found that, contrary to the intended benefits of thermal isolation effects, the voltage output decreased as the size of the lower tunnel increased. In contrast, voltage output increased as the size of the upper tunnel increased. Based on the above results, we propose that the lower tunnel should be omitted, and the elevation of the upper tunnel should be maximized for higher voltage output.
{"title":"Effects of air tunnel geometry on thermal resistance and thermocouple efficiency in a thermal electric generator","authors":"Xiao Lu, M. Qasaimeh","doi":"10.1109/BSN.2017.7936043","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936043","url":null,"abstract":"This paper considers a microelectromechanical system (MEMS)-based surface-micromachined poly-silicon germanium (poly-SiGe) thermopile, and examines the effects of the geometry of an air tunnel beneath the thermocouple on the effectiveness of its efficiency in energy harvesting. Intended to increase voltage output by enhancing thermal isolation between the two ends of a thermocouple, the air tunnel consists of the upper and lower parts. The upper tunnel results from the elevation of the semiconductor leg and the lower tunnel results from etching the silicon substrate. In our finite element analysis, we parametrized the depth and width of the lower tunnel and the elevation of the upper tunnel to examine the resulting voltage output. We found that, contrary to the intended benefits of thermal isolation effects, the voltage output decreased as the size of the lower tunnel increased. In contrast, voltage output increased as the size of the upper tunnel increased. Based on the above results, we propose that the lower tunnel should be omitted, and the elevation of the upper tunnel should be maximized for higher voltage output.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133513592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7935998
Manh Thang Nguyen, Quoc Khanh Dang, Y. Suh, Y. Chee
This paper proposed a reliable method for estimating the neck tilting angle while walking using a 3-axis accelerometer. Conventionally tilting angle is estimated by observing the gravitational force that applies on the sensor in static pose. However, while walking the external acceleration of the movement might heavily affect the estimation result. Therefore, we proposed a simple method based on human gait characteristics and step detection to eliminate the external forces in the accelerometer output. The experiment was done with five persons walking in a treadmill with different speeds under the observation of a camera system. The result showed that the proposed method provided an accurate estimation compared with conventional method of direct estimation.
{"title":"A method for estimating text neck while walking using a 3-axis accelerometer","authors":"Manh Thang Nguyen, Quoc Khanh Dang, Y. Suh, Y. Chee","doi":"10.1109/BSN.2017.7935998","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935998","url":null,"abstract":"This paper proposed a reliable method for estimating the neck tilting angle while walking using a 3-axis accelerometer. Conventionally tilting angle is estimated by observing the gravitational force that applies on the sensor in static pose. However, while walking the external acceleration of the movement might heavily affect the estimation result. Therefore, we proposed a simple method based on human gait characteristics and step detection to eliminate the external forces in the accelerometer output. The experiment was done with five persons walking in a treadmill with different speeds under the observation of a camera system. The result showed that the proposed method provided an accurate estimation compared with conventional method of direct estimation.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128685050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936036
A. Akbari, Xien Thomas, R. Jafari
The aim of this paper is to propose a robust, accurate and portable system for human body motion measurement. The system includes Inertial Measurement Units (IMU) and a Kinect or vision sensors. Since Kinect sampling rate is low (30 Hz per second) and it suffers from occlusion, it cannot individually measure human body motion accurately and robustly. On the other hand, IMU does not suffer from these problems, but it suffers from drift in particular with long-term motion monitoring and other types of errors (e.g., high acceleration motions, temperature and voltage variations). Thus, in this study, IMU and Kinect data were fused using a context enhanced extended Kalman filter. Rules were generated based on the context of motion in order to adjust Kalman filter parameters. In addition, an automated approach is introduced to estimate the variance of the noise of the sensors during the operation. Considering motion context and automatic noise detection, the robustness of monitoring is enhanced against errors related to motion context (i.e., high acceleration and long-term motions); furthermore, offline calibration is no longer required to set the parameters of the filter. The system was tested on leg and arm motions. The root mean square error of our fusion method was 6.08° lower than using only gyroscope, 16.98° lower than using only accelerometer, 2.49° lower than using only the Kinect and 8.99° lower than using simple EKF fusion method, which does not consider motion context and automatic noise estimation.
{"title":"Automatic noise estimation and context-enhanced data fusion of IMU and Kinect for human motion measurement","authors":"A. Akbari, Xien Thomas, R. Jafari","doi":"10.1109/BSN.2017.7936036","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936036","url":null,"abstract":"The aim of this paper is to propose a robust, accurate and portable system for human body motion measurement. The system includes Inertial Measurement Units (IMU) and a Kinect or vision sensors. Since Kinect sampling rate is low (30 Hz per second) and it suffers from occlusion, it cannot individually measure human body motion accurately and robustly. On the other hand, IMU does not suffer from these problems, but it suffers from drift in particular with long-term motion monitoring and other types of errors (e.g., high acceleration motions, temperature and voltage variations). Thus, in this study, IMU and Kinect data were fused using a context enhanced extended Kalman filter. Rules were generated based on the context of motion in order to adjust Kalman filter parameters. In addition, an automated approach is introduced to estimate the variance of the noise of the sensors during the operation. Considering motion context and automatic noise detection, the robustness of monitoring is enhanced against errors related to motion context (i.e., high acceleration and long-term motions); furthermore, offline calibration is no longer required to set the parameters of the filter. The system was tested on leg and arm motions. The root mean square error of our fusion method was 6.08° lower than using only gyroscope, 16.98° lower than using only accelerometer, 2.49° lower than using only the Kinect and 8.99° lower than using simple EKF fusion method, which does not consider motion context and automatic noise estimation.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127839497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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