Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516292
V. Ramachandran, P. Havinga, N. Meratnia
Chronic care is an eminent application of implantable body sensor networks (IBSN). Performing physical activities such as walking, running, and sitting is unavoidable during the long-term monitoring of chronic-care patients. These physical activities cripple the radio frequency (RF) signal between the implanted sensor nodes. This is because various body postures shadow the RF signal. Although shadowing itself may be short, a prolonged activity will significantly increase the effect of the RF-shadowing. This effect dampens the communication between implantable sensor nodes and hence increases the chance of missing life-critical data. To overcome this problem, in this paper we propose a link quality-aware medium access control (MAC) protocol called HACMAC, which adapts the access mechanism during different human activities based on the wireless link-quality. Our simulation results show that compared with the access mechanism suggested by the IEEE 802.15.6 standard, the reliability of the wireless communication is increased using HACMAC even while transmitting at a strongly low transmission power of 25μW effective isotropic radiated power (EIRP) set by the IEEE 802.15.6 standard.
{"title":"HACMAC: A reliable human activity-based medium access control for implantable body sensor networks","authors":"V. Ramachandran, P. Havinga, N. Meratnia","doi":"10.1109/BSN.2016.7516292","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516292","url":null,"abstract":"Chronic care is an eminent application of implantable body sensor networks (IBSN). Performing physical activities such as walking, running, and sitting is unavoidable during the long-term monitoring of chronic-care patients. These physical activities cripple the radio frequency (RF) signal between the implanted sensor nodes. This is because various body postures shadow the RF signal. Although shadowing itself may be short, a prolonged activity will significantly increase the effect of the RF-shadowing. This effect dampens the communication between implantable sensor nodes and hence increases the chance of missing life-critical data. To overcome this problem, in this paper we propose a link quality-aware medium access control (MAC) protocol called HACMAC, which adapts the access mechanism during different human activities based on the wireless link-quality. Our simulation results show that compared with the access mechanism suggested by the IEEE 802.15.6 standard, the reliability of the wireless communication is increased using HACMAC even while transmitting at a strongly low transmission power of 25μW effective isotropic radiated power (EIRP) set by the IEEE 802.15.6 standard.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348438","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516253
Ju Gao, Diyan Teng, Emre Ertin
Wireless biosensors enable continuous monitoring of physiology and can provide early signs of imminent problems allowing for quick intervention and improved outcomes. Wireless communication of the sensor data for remote storage and analysis dominates the device power budget and puts severe constraints on lifetime and size of these sensors. Traditionally, to minimize the wireless communication bandwidth, data compression at the sensor node and signal reconstruction at the remote terminal is utilized. Here we consider an alternative strategy of feature detection with compressed samples without the intermediate step of signal reconstruction. Specifically, we present a compressed matched subspace detection algorithm to detect fiducial points of ECG waveform from streaming random projections of the data. We provide a theoretical analysis to compare the performance of the compressed matched detector performance to that of a matched detector operating with uncompressed data. We present extensive experimental results with ECG data collected in the field illustrating that the proposed system can provide high quality heart rate variability indices and achieve an order of magnitude better RMSE in beat-to-beat heart rate estimation than the traditional filter/downsample solutions at low data rates.
{"title":"ECG feature detection using randomly compressed samples for stable HRV analysis over low rate links","authors":"Ju Gao, Diyan Teng, Emre Ertin","doi":"10.1109/BSN.2016.7516253","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516253","url":null,"abstract":"Wireless biosensors enable continuous monitoring of physiology and can provide early signs of imminent problems allowing for quick intervention and improved outcomes. Wireless communication of the sensor data for remote storage and analysis dominates the device power budget and puts severe constraints on lifetime and size of these sensors. Traditionally, to minimize the wireless communication bandwidth, data compression at the sensor node and signal reconstruction at the remote terminal is utilized. Here we consider an alternative strategy of feature detection with compressed samples without the intermediate step of signal reconstruction. Specifically, we present a compressed matched subspace detection algorithm to detect fiducial points of ECG waveform from streaming random projections of the data. We provide a theoretical analysis to compare the performance of the compressed matched detector performance to that of a matched detector operating with uncompressed data. We present extensive experimental results with ECG data collected in the field illustrating that the proposed system can provide high quality heart rate variability indices and achieve an order of magnitude better RMSE in beat-to-beat heart rate estimation than the traditional filter/downsample solutions at low data rates.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133177683","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516235
D. Ravì, Charence Wong, Benny P. L. Lo, Guang-Zhong Yang
Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.
{"title":"Deep learning for human activity recognition: A resource efficient implementation on low-power devices","authors":"D. Ravì, Charence Wong, Benny P. L. Lo, Guang-Zhong Yang","doi":"10.1109/BSN.2016.7516235","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516235","url":null,"abstract":"Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010962","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516273
Liang Liu, S. Mehrotra
This paper will focus on bed angle detection in hospital room automatically using the latest Kinect sensor. The developed system is an ideal application for nursing staff to monitoring the bed status for patient, especially under the situation that the patient is alone. The patient bed is reconstructed from point cloud data using polynomial plane fitting. The analysis to the detected bed angle could help the nursing staff to understand the potential developed hospital acquired infection (HAI) and the health situation of the patient, and acquire informative knowledge of the relation between bed angle and disease recovery to decide appropriate treatment strategy.
{"title":"Bed angle detection in hospital room using Microsoft Kinect V2","authors":"Liang Liu, S. Mehrotra","doi":"10.1109/BSN.2016.7516273","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516273","url":null,"abstract":"This paper will focus on bed angle detection in hospital room automatically using the latest Kinect sensor. The developed system is an ideal application for nursing staff to monitoring the bed status for patient, especially under the situation that the patient is alone. The patient bed is reconstructed from point cloud data using polynomial plane fitting. The analysis to the detected bed angle could help the nursing staff to understand the potential developed hospital acquired infection (HAI) and the health situation of the patient, and acquire informative knowledge of the relation between bed angle and disease recovery to decide appropriate treatment strategy.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633733","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516259
A. Tobola, Heike Leutheuser, Björn Schmitz, Christian Hofmann, M. Struck, C. Weigand, B. Eskofier, Georg Fischer
Battery runtime is a critical concern for practical usage of wearable biomedical sensor systems. A long runtime requires an interdisciplinary low-power knowledge and appropriate design tools. We addressed this issue designing a toolbox in three parts: (1) Modular evaluation kit for development of wearable ultra-low-power biomedical sensors; (2) Miniaturized, wearable, and code compatible sensor system with the same properties as the development kit; (3) Web-based battery runtime calculator for our sensor systems. The purpose of the development kit is optimization of the power consumption. Once optimization is finished, the same embedded software can be transferred to the miniaturized body worn sensor. The web-based application supports development quantifying the effects of use case and design decisions on battery runtime. A sensor developer can select sensor modules, configure sensor parameters, enter use case specific requirements, and select a battery to predict the battery runtime for a specific application. Our concept adds value to development of ultra-low-power biomedical wearable sensors. The concept is effective for professional work and educational purposes.
{"title":"Battery runtime optimization toolbox for wearable biomedical sensors","authors":"A. Tobola, Heike Leutheuser, Björn Schmitz, Christian Hofmann, M. Struck, C. Weigand, B. Eskofier, Georg Fischer","doi":"10.1109/BSN.2016.7516259","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516259","url":null,"abstract":"Battery runtime is a critical concern for practical usage of wearable biomedical sensor systems. A long runtime requires an interdisciplinary low-power knowledge and appropriate design tools. We addressed this issue designing a toolbox in three parts: (1) Modular evaluation kit for development of wearable ultra-low-power biomedical sensors; (2) Miniaturized, wearable, and code compatible sensor system with the same properties as the development kit; (3) Web-based battery runtime calculator for our sensor systems. The purpose of the development kit is optimization of the power consumption. Once optimization is finished, the same embedded software can be transferred to the miniaturized body worn sensor. The web-based application supports development quantifying the effects of use case and design decisions on battery runtime. A sensor developer can select sensor modules, configure sensor parameters, enter use case specific requirements, and select a battery to predict the battery runtime for a specific application. Our concept adds value to development of ultra-low-power biomedical wearable sensors. The concept is effective for professional work and educational purposes.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134081380","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516285
Zhiqiang Liu, B. Liu, C. Chen, C. Chen
Wireless Body Area Networks (WBANs) represent one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, we propose an Energy-efficient and QoS-effective resource allocation that considers a mix-cost parameter characterizing both energy cost and QoS cost between attainable QoS support and QoS requirements. Based on the mix-cost parameter, we first formulate the resource allocation problem as a mixed integer nonlinear programming (MINP) for optimizing the transmission power, the transmission rate and allocated time slots for each sensor to minimize total mix-cost of the system. Then we propose a sub-optimal greedy resource allocation algorithm, which has a much lower complexity compared to exhaustive search. Simulation results demonstrate the advantage of the mix-cost parameter to evaluate energy efficiency and attainable QoS support, as well as verifying the effectiveness of the proposed resource allocation algorithm.
{"title":"An energy-efficient and QoS-effective resource allocation scheme in WBANs","authors":"Zhiqiang Liu, B. Liu, C. Chen, C. Chen","doi":"10.1109/BSN.2016.7516285","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516285","url":null,"abstract":"Wireless Body Area Networks (WBANs) represent one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, we propose an Energy-efficient and QoS-effective resource allocation that considers a mix-cost parameter characterizing both energy cost and QoS cost between attainable QoS support and QoS requirements. Based on the mix-cost parameter, we first formulate the resource allocation problem as a mixed integer nonlinear programming (MINP) for optimizing the transmission power, the transmission rate and allocated time slots for each sensor to minimize total mix-cost of the system. Then we propose a sub-optimal greedy resource allocation algorithm, which has a much lower complexity compared to exhaustive search. Simulation results demonstrate the advantage of the mix-cost parameter to evaluate energy efficiency and attainable QoS support, as well as verifying the effectiveness of the proposed resource allocation algorithm.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125761657","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516267
Hossein Mohamadipanah, C. Parthiban, K. Law, Jay N. Nathwani, Lakita Maulson, Shannon DiMarco, C. Pugh
The main purpose of this study is to find possible relationships between the smoothness of hand function during laparoscopic ventral hernia (LVH) repair and psychomotor skills in a defined virtual reality (VR) environment. Thirty four surgical residents N = 34 performed two scenarios. First, participants were asked to perform a simulated LVH repair during which their hand movement was tracked using electromagnetic sensors. Subsequently, the smoothness of hand function was calculated for each participant's dominant and non-dominate hand. Then participants performed two modules in a defined VR environment, which assessed their force matching and target tracking capabilities. More smooth hand function during the LVH repair correlated positively with higher performance in VR modules. Also, translational smoothness of dominant hand is found as the most informative smoothness metric in the LVH repair scenario. Therefore, defined force matching and target tracking assessments in VR can potentially be used as an indirect assessment of fine motor skills in the LVH repair.
{"title":"Hand smoothness in laparoscopic surgery correlates to psychomotor skills in virtual reality","authors":"Hossein Mohamadipanah, C. Parthiban, K. Law, Jay N. Nathwani, Lakita Maulson, Shannon DiMarco, C. Pugh","doi":"10.1109/BSN.2016.7516267","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516267","url":null,"abstract":"The main purpose of this study is to find possible relationships between the smoothness of hand function during laparoscopic ventral hernia (LVH) repair and psychomotor skills in a defined virtual reality (VR) environment. Thirty four surgical residents N = 34 performed two scenarios. First, participants were asked to perform a simulated LVH repair during which their hand movement was tracked using electromagnetic sensors. Subsequently, the smoothness of hand function was calculated for each participant's dominant and non-dominate hand. Then participants performed two modules in a defined VR environment, which assessed their force matching and target tracking capabilities. More smooth hand function during the LVH repair correlated positively with higher performance in VR modules. Also, translational smoothness of dominant hand is found as the most informative smoothness metric in the LVH repair scenario. Therefore, defined force matching and target tracking assessments in VR can potentially be used as an indirect assessment of fine motor skills in the LVH repair.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125909210","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516270
Carlos A. Mugruza-Vassallo
The use of hierarchical linear modelling has been increasing in the last 5 years to analyze EEG data. Until now, no clear comparison on linear modelling in different modalities has been done. Therefore, specific differences observed in both visual and auditory paradigms were computed with linear modelling. The Coefficient of Determination through the explained variance (R2) in Linear Modelling was sought in visual and auditory modalities. ERP scalp series of time from 100 to 300 ms for the visual task and around 150 ms to 400 for the auditory task were also plotted. Although these paradigms use different regressors, both paradigms showed reliable R2 signatures across the participants and reliable ERP scalp maps. Results accounted for different magnitudes in greater R2 values for visual modality. Auditory R2 results appeared with a reliable linear modelling when compared with R2 studies in other subjects.
{"title":"Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks","authors":"Carlos A. Mugruza-Vassallo","doi":"10.1109/BSN.2016.7516270","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516270","url":null,"abstract":"The use of hierarchical linear modelling has been increasing in the last 5 years to analyze EEG data. Until now, no clear comparison on linear modelling in different modalities has been done. Therefore, specific differences observed in both visual and auditory paradigms were computed with linear modelling. The Coefficient of Determination through the explained variance (R2) in Linear Modelling was sought in visual and auditory modalities. ERP scalp series of time from 100 to 300 ms for the visual task and around 150 ms to 400 for the auditory task were also plotted. Although these paradigms use different regressors, both paradigms showed reliable R2 signatures across the participants and reliable ERP scalp maps. Results accounted for different magnitudes in greater R2 values for visual modality. Auditory R2 results appeared with a reliable linear modelling when compared with R2 studies in other subjects.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131885080","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 : 2016-06-14DOI: 10.1109/BSN.2016.7516237
Markus J. Lüken, B. Penzlin, S. Leonhardt, B. Misgeld
In clinical practice the determination of the heart rate variability (HRV) has become a common measure to investigate the parasympathetic cardiac control. Especially the measurement of the respiratory sinus arrhythmia (RSA) has gained importance to asses the HRV. The RSA can be seen as an indirect parameter for the physiological or psychological stress the patient is currently exposed to. Thus, this parameter is used to identify specific characteristics of disease in a broad field of clinical disciplines. In this contribution, we present a BSN-based approach of assessing the RSA in a long-term evaluation. For this purpose, we use two sensor types: A three channel ECG sensor node which was introduced before and a recently developed respiratory sensor based on conductive yarn. We further implemented an oscillatory model-based Unscented Kalman filter (UKF) to estimate the heart rate as well as the breathing rate and, thus, to calculate the RSA. The algorithm is finally validated by performing deep breathing tests (DBT) on a healthy test subject in order to force an increased occurrence of the RSA. The results of the developed system and proposed algorithm are finally discussed with respect to its applicability in different every days situations.
{"title":"Quantification of respiratory sinus arrhythmia using the IPANEMA body sensor network","authors":"Markus J. Lüken, B. Penzlin, S. Leonhardt, B. Misgeld","doi":"10.1109/BSN.2016.7516237","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516237","url":null,"abstract":"In clinical practice the determination of the heart rate variability (HRV) has become a common measure to investigate the parasympathetic cardiac control. Especially the measurement of the respiratory sinus arrhythmia (RSA) has gained importance to asses the HRV. The RSA can be seen as an indirect parameter for the physiological or psychological stress the patient is currently exposed to. Thus, this parameter is used to identify specific characteristics of disease in a broad field of clinical disciplines. In this contribution, we present a BSN-based approach of assessing the RSA in a long-term evaluation. For this purpose, we use two sensor types: A three channel ECG sensor node which was introduced before and a recently developed respiratory sensor based on conductive yarn. We further implemented an oscillatory model-based Unscented Kalman filter (UKF) to estimate the heart rate as well as the breathing rate and, thus, to calculate the RSA. The algorithm is finally validated by performing deep breathing tests (DBT) on a healthy test subject in order to force an increased occurrence of the RSA. The results of the developed system and proposed algorithm are finally discussed with respect to its applicability in different every days situations.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133461742","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}
Rehabilitation exercise is one of the most important parts in knee osteoarthritis therapy. A good rehabilitation monitoring method provides physiotherapists with performance metrics that are greatly helpful in recovery progress. One of the main difficulties of monitoring and analysis is performing accurate online segmentation of motion sections due to the high degree of freedom (DoF) of human motion. This paper proposes an approach for initial posture classification and online segmentation of rehabilitation exercise data acquired with body-worn inertial sensors. Specifically, we introduce a threshold-based algorithm for initial posture classification and a multi-layer Support Vector Machine (SVM) model for online segmentation. The proposed approach is capable of accurate online segmentation and classification of exercise data. The approach is verified on 10 subjects performing common rehabilitation exercises for knee osteoarthritis, giving initial posture classification accuracy of 97.9% and segmentation accuracy of 90.6% on layer-1 SVM and 92.7% on layer-2 SVM.
{"title":"Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring","authors":"Hsieh-Ping Chen, Hsieh-Chung Chen, Kai-Chun Liu, Chia-Tai Chan","doi":"10.1109/BSN.2016.7516232","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516232","url":null,"abstract":"Rehabilitation exercise is one of the most important parts in knee osteoarthritis therapy. A good rehabilitation monitoring method provides physiotherapists with performance metrics that are greatly helpful in recovery progress. One of the main difficulties of monitoring and analysis is performing accurate online segmentation of motion sections due to the high degree of freedom (DoF) of human motion. This paper proposes an approach for initial posture classification and online segmentation of rehabilitation exercise data acquired with body-worn inertial sensors. Specifically, we introduce a threshold-based algorithm for initial posture classification and a multi-layer Support Vector Machine (SVM) model for online segmentation. The proposed approach is capable of accurate online segmentation and classification of exercise data. The approach is verified on 10 subjects performing common rehabilitation exercises for knee osteoarthritis, giving initial posture classification accuracy of 97.9% and segmentation accuracy of 90.6% on layer-1 SVM and 92.7% on layer-2 SVM.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122662343","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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