Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299416
Ching-Mei Chen, Kosy Onyenso, Guang-Zhong Yang, Benny P. L. Lo
This paper proposes a novel concept of using a multiple PPG and ECG based sensing platform aimed for monitoring the progress of diabetic peripheral neuropathy (DPN). It explores the use of PPG sensor to capture pulse arrival time (PAT). Based on the same principal of using Brachial-ankle pulse wave velocity (baPWV) to assess DPN, this paper proposes a platform which integrated two PPG sensors and one 2-lead ECG sensor to detect the difference in PAT (pulse arrive time on the finger compare to the time when the pulse reaches the ankle) as a surrogate measure for evaluating the progression of DPN. Preliminary results show that PAT increases when a pressure was applied onto upper leg using a blood pressure cuff simulating arterial stiffness/DPN. It shows that PDN can potentially be quantified by measuring PAT by using the proposed platform.
{"title":"A multi-sensor platform for monitoring diabetic peripheral neuropathy","authors":"Ching-Mei Chen, Kosy Onyenso, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2015.7299416","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299416","url":null,"abstract":"This paper proposes a novel concept of using a multiple PPG and ECG based sensing platform aimed for monitoring the progress of diabetic peripheral neuropathy (DPN). It explores the use of PPG sensor to capture pulse arrival time (PAT). Based on the same principal of using Brachial-ankle pulse wave velocity (baPWV) to assess DPN, this paper proposes a platform which integrated two PPG sensors and one 2-lead ECG sensor to detect the difference in PAT (pulse arrive time on the finger compare to the time when the pulse reaches the ankle) as a surrogate measure for evaluating the progression of DPN. Preliminary results show that PAT increases when a pressure was applied onto upper leg using a blood pressure cuff simulating arterial stiffness/DPN. It shows that PDN can potentially be quantified by measuring PAT by using the proposed platform.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787274","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299411
J. Dieffenderfer, Henry Goodell, Brinnae Bent, Eric C. Beppler, R. Jayakumar, Murat A. Yokus, J. Jur, A. Bozkurt, D. Peden
We present a wearable sensor system consisting of a wristband and chest patch to enable the correlation of individual environmental exposure to health response for understanding impacts of ozone on chronic asthma conditions. The wrist worn device measures ambient ozone concentration, heart rate via plethysmography (PPG), three-axis acceleration, ambient temperature, and ambient relative humidity. The chest patch measures heart rate via electrocardiography (ECG) and PPG, respiratory rate via PPG, wheezing via a microphone, and three-axis acceleration. The data from each sensor is continually streamed to a peripheral data aggregation device, and is subsequently transferred to a dedicated server for cloud storage. The current generation of the system uses only commercially-off-the-shelf (COTS) components where the entire electronic structure of the wristband has dimensions of 3.1×4.1×1.2 cm3 while the chest patch electronics has a dimensions of 3.3×4.4×1.2 cm3. The power consumptions of the wristband and chest patch are 78 mW and 33 mW respectively where using a 400 mAh lithium polymer battery would operate the wristband for around 15 hours and the chest patch for around 36 hours.
{"title":"Wearable wireless sensors for chronic respiratory disease monitoring","authors":"J. Dieffenderfer, Henry Goodell, Brinnae Bent, Eric C. Beppler, R. Jayakumar, Murat A. Yokus, J. Jur, A. Bozkurt, D. Peden","doi":"10.1109/BSN.2015.7299411","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299411","url":null,"abstract":"We present a wearable sensor system consisting of a wristband and chest patch to enable the correlation of individual environmental exposure to health response for understanding impacts of ozone on chronic asthma conditions. The wrist worn device measures ambient ozone concentration, heart rate via plethysmography (PPG), three-axis acceleration, ambient temperature, and ambient relative humidity. The chest patch measures heart rate via electrocardiography (ECG) and PPG, respiratory rate via PPG, wheezing via a microphone, and three-axis acceleration. The data from each sensor is continually streamed to a peripheral data aggregation device, and is subsequently transferred to a dedicated server for cloud storage. The current generation of the system uses only commercially-off-the-shelf (COTS) components where the entire electronic structure of the wristband has dimensions of 3.1×4.1×1.2 cm3 while the chest patch electronics has a dimensions of 3.3×4.4×1.2 cm3. The power consumptions of the wristband and chest patch are 78 mW and 33 mW respectively where using a 400 mAh lithium polymer battery would operate the wristband for around 15 hours and the chest patch for around 36 hours.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116671558","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299351
Lindsay W. Ludlow, P. Weyand
Sensor-based predictions for walking energy expenditure require sufficiently versatile algorithms to generalize to a variety of conditions. Here we test whether our height-weight-speed (HWS) model validated across speed under level conditions is similarly accurate for loaded walking. We hypothesized that increases in walking energy expenditure would be proportional to added load when resting metabolism was subtracted from gross walking metabolism. After subtracting resting metabolic rate, walking energy expenditure was found to increase in direct proportion to load at walking speeds of 0.6, 1.0, and 1.4 m·s-1. With load carriage treated as body weight, the predictive algorithms derived using the HWS model were similar for loaded and unloaded conditions. Determination of the direct relationship between load and energy expenditure for level walking provides insight which may be used to refine algorithms, such as the HWS model, for use in body sensors to monitor physiological status in the field.
{"title":"Walking energy expenditure: A loaded approach to algorithm development","authors":"Lindsay W. Ludlow, P. Weyand","doi":"10.1109/BSN.2015.7299351","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299351","url":null,"abstract":"Sensor-based predictions for walking energy expenditure require sufficiently versatile algorithms to generalize to a variety of conditions. Here we test whether our height-weight-speed (HWS) model validated across speed under level conditions is similarly accurate for loaded walking. We hypothesized that increases in walking energy expenditure would be proportional to added load when resting metabolism was subtracted from gross walking metabolism. After subtracting resting metabolic rate, walking energy expenditure was found to increase in direct proportion to load at walking speeds of 0.6, 1.0, and 1.4 m·s-1. With load carriage treated as body weight, the predictive algorithms derived using the HWS model were similar for loaded and unloaded conditions. Determination of the direct relationship between load and energy expenditure for level walking provides insight which may be used to refine algorithms, such as the HWS model, for use in body sensors to monitor physiological status in the field.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127672223","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}
Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.
{"title":"SleepSense: Non-invasive sleep event recognition using an electromagnetic probe","authors":"Yan Zhuang, Chen Song, Aosen Wang, Feng Lin, Yiran Li, Changzhan Gu, Changzhi Li, Wenyao Xu","doi":"10.1109/BSN.2015.7299364","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299364","url":null,"abstract":"Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127680845","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299367
B. Venema, V. Blazek, S. Leonhardt
In this work, we report on human trials with the MedIT in-ear photoplethysmography (PPG) measurement system. The system is evaluated with healthy subjects and people suffering from heart insufficiency, respectively. Physiological heart activity can be measured with a minimal error of 1.2 heartbeats per minute and a regression coefficient of 0.9975 compared with standard ECG. Respiration related information was extracted by combining PPG amplitude analysis and car-diorespirational coupling (cardiorespiratory sinus arrhythmia). The moments of inspiration and expiration were estimated with a Naive Bayes' classifier with high sensitivity and specificity of 81,4% and 86%, respectively. For automatic cardiological alarming, a feature space is defined that clearly demonstrates the separability of normal heart rhythm and heart insufficiency. The results demonstrate a promising perspective for a mobile and long-term cardiorespiratory monitoring and alarming with an unobtrusive and inexspensive PPG measurement technique that is fully compatible to modern communication devices.
{"title":"In-ear photoplethysmography for mobile cardiorespiratory monitoring and alarming","authors":"B. Venema, V. Blazek, S. Leonhardt","doi":"10.1109/BSN.2015.7299367","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299367","url":null,"abstract":"In this work, we report on human trials with the MedIT in-ear photoplethysmography (PPG) measurement system. The system is evaluated with healthy subjects and people suffering from heart insufficiency, respectively. Physiological heart activity can be measured with a minimal error of 1.2 heartbeats per minute and a regression coefficient of 0.9975 compared with standard ECG. Respiration related information was extracted by combining PPG amplitude analysis and car-diorespirational coupling (cardiorespiratory sinus arrhythmia). The moments of inspiration and expiration were estimated with a Naive Bayes' classifier with high sensitivity and specificity of 81,4% and 86%, respectively. For automatic cardiological alarming, a feature space is defined that clearly demonstrates the separability of normal heart rhythm and heart insufficiency. The results demonstrate a promising perspective for a mobile and long-term cardiorespiratory monitoring and alarming with an unobtrusive and inexspensive PPG measurement technique that is fully compatible to modern communication devices.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133262919","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299395
P. Pillatsch, P. Wright, E. Yeatman, A. Holmes
Motion energy harvesting is a sought after alternative to battery powering for implanted and body worn devices. However, the lack of electricity generation at rest is a major concern. This paper describes a previously presented piezoelectric rotational motion harvester, and presents a mechanism for wireless and external actuation of the main rotor of the device through a magnetic reluctance coupling. With this approach, an internal battery or super-capacitor could be recharged during prolonged periods of inactivity. An improved experimental setup uses a stepper motor to accurately prescribe even high actuation frequencies. A single stack and diametrically opposed dual stacks of driving magnets are investigated. It is demonstrated that adding the additional magnet stack is detrimental to the system performance. Furthermore, the system was tested in a horizontal and a gravity-independent vertical arrangement. Power can successfully be generated regardless of orientation. The maximal separation between driving magnets and harvester reached 20 millimeters. Lastly, the device can operate even under misalignment, and the optimal driving frequency is 25 Hertz, at which over 100 microwatts of power were generated for a device with a functional volume of 1.85 cubic centimeters.
{"title":"A wireless charging mechanism for a rotational human motion energy harvester","authors":"P. Pillatsch, P. Wright, E. Yeatman, A. Holmes","doi":"10.1109/BSN.2015.7299395","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299395","url":null,"abstract":"Motion energy harvesting is a sought after alternative to battery powering for implanted and body worn devices. However, the lack of electricity generation at rest is a major concern. This paper describes a previously presented piezoelectric rotational motion harvester, and presents a mechanism for wireless and external actuation of the main rotor of the device through a magnetic reluctance coupling. With this approach, an internal battery or super-capacitor could be recharged during prolonged periods of inactivity. An improved experimental setup uses a stepper motor to accurately prescribe even high actuation frequencies. A single stack and diametrically opposed dual stacks of driving magnets are investigated. It is demonstrated that adding the additional magnet stack is detrimental to the system performance. Furthermore, the system was tested in a horizontal and a gravity-independent vertical arrangement. Power can successfully be generated regardless of orientation. The maximal separation between driving magnets and harvester reached 20 millimeters. Lastly, the device can operate even under misalignment, and the optimal driving frequency is 25 Hertz, at which over 100 microwatts of power were generated for a device with a functional volume of 1.85 cubic centimeters.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044499","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299412
K. Teachasrisaksakul, Zhiqiang Zhang, Guang-Zhong Yang
Sensor-to-segment calibration is a critical step for motion reconstruction from inertial and magnetic measurement units (IMMUs). In this paper, a novel sensor-to-segment calibration protocol is proposed. The protocol consists of three stages that allow for in situ calibration. After the sensor units are attached to the body, predefined postures and movements are used for sensor calibration. Acceleration and angular velocity measurements are used to estimate axes of functional frame (FF) by Principal Component Analysis (PCA). Finally, Levenberg-Marquardt optimization is used to identify rotation matrices between the expected FF and their estimations with respect to the sensor frame. Validation of the method demonstrates its practical value and how the proposed protocol reduces the extent of cross-talk for evaluating joint kinematics.
{"title":"In situ sensor-to-segment calibration for whole body motion capture","authors":"K. Teachasrisaksakul, Zhiqiang Zhang, Guang-Zhong Yang","doi":"10.1109/BSN.2015.7299412","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299412","url":null,"abstract":"Sensor-to-segment calibration is a critical step for motion reconstruction from inertial and magnetic measurement units (IMMUs). In this paper, a novel sensor-to-segment calibration protocol is proposed. The protocol consists of three stages that allow for in situ calibration. After the sensor units are attached to the body, predefined postures and movements are used for sensor calibration. Acceleration and angular velocity measurements are used to estimate axes of functional frame (FF) by Principal Component Analysis (PCA). Finally, Levenberg-Marquardt optimization is used to identify rotation matrices between the expected FF and their estimations with respect to the sensor frame. Validation of the method demonstrates its practical value and how the proposed protocol reduces the extent of cross-talk for evaluating joint kinematics.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121787033","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299369
Kemeng Chen, W. Fink, Janet Roveda, R. Lane, John J. B. Allen, J. Vanuk
Wearable technology and mobile platforms are becoming more and more popular in health care. This paper introduces a real time stress management system using wearable sensors and Smartphone mobile platform. The new system estimates stress level in real time using heart rate variability and patient activity cycles, and provides relaxation exercises instantaneously to help manage stress. The system relies on a wearable sensor to collect data (i.e., heart rate and respiration rate) and transmits data to Smartphones using Bluetooth to further process data. We also introduce a new breathing template matching algorithm to identify the best breathing exercise for users. A 2D visualization display shows that stress can be effectively relieved by the proposed stress management system.
{"title":"Wearable sensor based stress management using integrated respiratory and ECG waveforms","authors":"Kemeng Chen, W. Fink, Janet Roveda, R. Lane, John J. B. Allen, J. Vanuk","doi":"10.1109/BSN.2015.7299369","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299369","url":null,"abstract":"Wearable technology and mobile platforms are becoming more and more popular in health care. This paper introduces a real time stress management system using wearable sensors and Smartphone mobile platform. The new system estimates stress level in real time using heart rate variability and patient activity cycles, and provides relaxation exercises instantaneously to help manage stress. The system relies on a wearable sensor to collect data (i.e., heart rate and respiration rate) and transmits data to Smartphones using Bluetooth to further process data. We also introduce a new breathing template matching algorithm to identify the best breathing exercise for users. A 2D visualization display shows that stress can be effectively relieved by the proposed stress management system.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121301637","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299379
Kai-Chun Liu, Chia-Tai Chan, S. J. Hsu
According to the WHO report in 2013, the world population aging over 60 years is predicted to increase to 20 million in 2050. Aging comes about many challenges to elders due to their cognitive decline, chronic age-related diseases, as well as limitations in physical activity, vision, and hearing. Recent advances in wearable computing and mobile health technology create new opportunity for ambient assisted living system to help the person perform the activities safely and independently. The activity monitoring of daily living is the core technique of the ambient assisted living system. Several well-known approaches have utilized various sensors for activity recognition such as camera, RFID, infrared detector and inertial sensor. Since the activities are well characterized by the objects, location or hand gesture that are manipulated during their performance on activities of daily living. However, some applications included, e.g. the monitoring of specific tasks and/or movements in a rehabilitation scenario or the classification of dietary intake gestures for an automated nutrition monitoring system, where reliable activity recognition on a more fine-grained level is needed. To fulfill the requirement, we design a hierarchical window approach based on the dynamic time warping algorithm to achieve fine-grained activity recognition, where the template selection and threshold configuration is developed to cope with the ambiguity with similar features. Furthermore, a confidence estimation for the pattern matching is also proposed. The recognition procedure was successfully adapted to the investigated cleaning tasks. The overall performance in precision, recall, and F1-socre is 89.0%, 88.6%, and 88.1% respectively. The results of the experiment demonstrate that the proposed mechanism is reliable and fulfills the requirements of the ambient assisted living.
{"title":"A confidence-based approach to hand movements recognition for cleaning tasks using dynamic time warping","authors":"Kai-Chun Liu, Chia-Tai Chan, S. J. Hsu","doi":"10.1109/BSN.2015.7299379","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299379","url":null,"abstract":"According to the WHO report in 2013, the world population aging over 60 years is predicted to increase to 20 million in 2050. Aging comes about many challenges to elders due to their cognitive decline, chronic age-related diseases, as well as limitations in physical activity, vision, and hearing. Recent advances in wearable computing and mobile health technology create new opportunity for ambient assisted living system to help the person perform the activities safely and independently. The activity monitoring of daily living is the core technique of the ambient assisted living system. Several well-known approaches have utilized various sensors for activity recognition such as camera, RFID, infrared detector and inertial sensor. Since the activities are well characterized by the objects, location or hand gesture that are manipulated during their performance on activities of daily living. However, some applications included, e.g. the monitoring of specific tasks and/or movements in a rehabilitation scenario or the classification of dietary intake gestures for an automated nutrition monitoring system, where reliable activity recognition on a more fine-grained level is needed. To fulfill the requirement, we design a hierarchical window approach based on the dynamic time warping algorithm to achieve fine-grained activity recognition, where the template selection and threshold configuration is developed to cope with the ambiguity with similar features. Furthermore, a confidence estimation for the pattern matching is also proposed. The recognition procedure was successfully adapted to the investigated cleaning tasks. The overall performance in precision, recall, and F1-socre is 89.0%, 88.6%, and 88.1% respectively. The results of the experiment demonstrate that the proposed mechanism is reliable and fulfills the requirements of the ambient assisted living.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125425096","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299419
Vahid Behravan, Neil E. Glover, Rutger Farry, Patrick Chiang, M. Shoaib
Biomedical signals exhibit substantial variance in their sparsity, preventing conventional a-priori open-loop setting of the compressed sensing (CS) compression factor. In this work, we propose, analyze, and experimentally verify a rate-adaptive compressed-sensing system where the compression factor is modified automatically, based upon the sparsity of the input signal. Experimental results based on an embedded sensor platform exhibit a 16.2% improvement in power consumption for the proposed rate-adaptive CS versus traditional CS with a fixed compression factor. We also demonstrate the potential to improve this number to 24% through the use of an ultra low power processor in our embedded system.
{"title":"Rate-adaptive compressed-sensing and sparsity variance of biomedical signals","authors":"Vahid Behravan, Neil E. Glover, Rutger Farry, Patrick Chiang, M. Shoaib","doi":"10.1109/BSN.2015.7299419","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299419","url":null,"abstract":"Biomedical signals exhibit substantial variance in their sparsity, preventing conventional a-priori open-loop setting of the compressed sensing (CS) compression factor. In this work, we propose, analyze, and experimentally verify a rate-adaptive compressed-sensing system where the compression factor is modified automatically, based upon the sparsity of the input signal. Experimental results based on an embedded sensor platform exhibit a 16.2% improvement in power consumption for the proposed rate-adaptive CS versus traditional CS with a fixed compression factor. We also demonstrate the potential to improve this number to 24% through the use of an ultra low power processor in our embedded system.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132076747","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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