Inter-user interference is the interference in communication when several Body Sensor Networks (BSNs) operate in the same vicinity. As BSN users congregate in an area, interference due to concurrently communicating BSNs will increase, resulting in poor performance. In this paper, we conduct a preliminary investigation of the impact of inter-user interference and investigate its behavior with respect to parameters such as number of networks and the rates at which these networks communicate. Inter-user interference, is seen to reduce Packet Delivery Ratio by almost 35% in cases of 8 or more high-rate networks operating in the same location. We also propose a system to mitigate the adverse effects of inter-user interference. Our solution uses a fixed network infrastructure to monitor and identify BSNs that are likely to interfere with each other. The network then recommends changes to the BSN protocol to lessen interference between them. We also implement an instance of our system using a Wireless Sensor Network (WSN) infrastructure to reduce interference. The system is shown to significantly decrease the impact of inter-user interference.
{"title":"Inter-User Interference in Body Sensor Networks: Preliminary Investigation and an Infrastructure-Based Solution","authors":"B. D. Silva, A. Natarajan, M. Motani","doi":"10.1109/BSN.2009.36","DOIUrl":"https://doi.org/10.1109/BSN.2009.36","url":null,"abstract":"Inter-user interference is the interference in communication when several Body Sensor Networks (BSNs) operate in the same vicinity. As BSN users congregate in an area, interference due to concurrently communicating BSNs will increase, resulting in poor performance. In this paper, we conduct a preliminary investigation of the impact of inter-user interference and investigate its behavior with respect to parameters such as number of networks and the rates at which these networks communicate. Inter-user interference, is seen to reduce Packet Delivery Ratio by almost 35% in cases of 8 or more high-rate networks operating in the same location. We also propose a system to mitigate the adverse effects of inter-user interference. Our solution uses a fixed network infrastructure to monitor and identify BSNs that are likely to interfere with each other. The network then recommends changes to the BSN protocol to lessen interference between them. We also implement an instance of our system using a Wireless Sensor Network (WSN) infrastructure to reduce interference. The system is shown to significantly decrease the impact of inter-user interference.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128938711","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}
Qiang Li, J. Stankovic, M. Hanson, Adam T. Barth, J. Lach, Gang Zhou
Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.
{"title":"Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information","authors":"Qiang Li, J. Stankovic, M. Hanson, Adam T. Barth, J. Lach, Gang Zhou","doi":"10.1109/BSN.2009.46","DOIUrl":"https://doi.org/10.1109/BSN.2009.46","url":null,"abstract":"Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130129170","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}
This paper describes the application of a fully wireless network of on-body inertial/magnetic sensors for the 3-D motion capture and real-time analysis of Tango dancers. Accomplished Tango dancers exhibit both individual flair and good co-ordination with their partners. Features have been identified which differentiate the better dancers, such as chest-bend angle, synchronisation between the chest and foot movements, and chest movement co-ordination, which reflect the performance of both the individual and of the partnership. These features have been analysed on live data for characterising the dancers’ performances. The aim in the future is to design a dance tutoring tool which will analyse the sensor data and provide feedback for improvement.
{"title":"Speckled Tango Dancers: Real-Time Motion Capture of Two-Body Interactions Using On-body Wireless Sensor Networks","authors":"D. Arvind, Aris Valtazanos","doi":"10.1109/BSN.2009.54","DOIUrl":"https://doi.org/10.1109/BSN.2009.54","url":null,"abstract":"This paper describes the application of a fully wireless network of on-body inertial/magnetic sensors for the 3-D motion capture and real-time analysis of Tango dancers. Accomplished Tango dancers exhibit both individual flair and good co-ordination with their partners. Features have been identified which differentiate the better dancers, such as chest-bend angle, synchronisation between the chest and foot movements, and chest movement co-ordination, which reflect the performance of both the individual and of the partnership. These features have been analysed on live data for characterising the dancers’ performances. The aim in the future is to design a dance tutoring tool which will analyse the sensor data and provide feedback for improvement.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131146990","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}
M. Hanson, H. Powell, Adam T. Barth, J. Lach, Maite Brandt-Pearce
Clinicians have determined that continuous ambulatory monitoring provides significant preventative and diagnostic benefit, especially to the aged population. In this paper we describe gait classification techniques based on data obtained using a new body area sensor network platform named TEMPO 3. The platform and its supporting infrastructure enable six-degrees-of-freedom inertial sensing, signal processing, and wireless transmission. The proposed signal processing includes data normalization to improve robustness, feature extraction optimized for classification, and wavelet pre-processing. The effectiveness of the platform is validated by implementing a binary classifier between shuffle and normal gait. Artificial neural networks and classifiers based on the Cerebellar Model Articulation Controller were tested and yielded classification accuracies (68%-98%) comparable to previous efforts that required more restrictive or intrusive apparatus. These results suggest a viable path to resource-constrained, on-body gait classification.
{"title":"Neural Network Gait Classification for On-Body Inertial Sensors","authors":"M. Hanson, H. Powell, Adam T. Barth, J. Lach, Maite Brandt-Pearce","doi":"10.1109/BSN.2009.48","DOIUrl":"https://doi.org/10.1109/BSN.2009.48","url":null,"abstract":"Clinicians have determined that continuous ambulatory monitoring provides significant preventative and diagnostic benefit, especially to the aged population. In this paper we describe gait classification techniques based on data obtained using a new body area sensor network platform named TEMPO 3. The platform and its supporting infrastructure enable six-degrees-of-freedom inertial sensing, signal processing, and wireless transmission. The proposed signal processing includes data normalization to improve robustness, feature extraction optimized for classification, and wavelet pre-processing. The effectiveness of the platform is validated by implementing a binary classifier between shuffle and normal gait. Artificial neural networks and classifiers based on the Cerebellar Model Articulation Controller were tested and yielded classification accuracies (68%-98%) comparable to previous efforts that required more restrictive or intrusive apparatus. These results suggest a viable path to resource-constrained, on-body gait classification.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115776731","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}
The objective of this study was to compare base-level and meta-level classifiers on the task of activity recognition. Five wireless kinematic sensors were attached to 25 subjects with each subject asked to complete a range of basic activities in a controlled laboratory setting. Subjects were then asked to carry out similar self-annotated activities in a random order and in an unsupervised environment. A combination of time-domain and frequency-domain features were calculated using a sliding window segmentation technique. A reduced feature set was generated using a wrapper subset evaluation technique with a linear forward search.The meta-level classifier AdaBoostM1 with C4.5 Graft as its base-level classifier achieved an overall accuracy of 95%. Equal sized datasets of subject independent data and subject dependent data were used to train this classifier and it was found that high recognition rates can be achieved without the need of user specific training.
{"title":"Identifying Activities of Daily Living Using Wireless Kinematic Sensors and Data Mining Algorithms","authors":"A. Dalton, G. Ó. Laighin","doi":"10.1109/BSN.2009.65","DOIUrl":"https://doi.org/10.1109/BSN.2009.65","url":null,"abstract":"The objective of this study was to compare base-level and meta-level classifiers on the task of activity recognition. Five wireless kinematic sensors were attached to 25 subjects with each subject asked to complete a range of basic activities in a controlled laboratory setting. Subjects were then asked to carry out similar self-annotated activities in a random order and in an unsupervised environment. A combination of time-domain and frequency-domain features were calculated using a sliding window segmentation technique. A reduced feature set was generated using a wrapper subset evaluation technique with a linear forward search.The meta-level classifier AdaBoostM1 with C4.5 Graft as its base-level classifier achieved an overall accuracy of 95%. Equal sized datasets of subject independent data and subject dependent data were used to train this classifier and it was found that high recognition rates can be achieved without the need of user specific training.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130634933","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}
S. Bouwstra, Wei Chen, L. Feijs, Sidarto Bambang-Oetomo
Critically ill new born babies admitted at the Neonatal Intensive Care Unit (NICU) are extremely tiny and vulnerable to external disturbance. Smart Jacket proposed in this paper is the vision of a wearable unobtrusive continuous monitoring system realized by body sensor networks (BSN) and wireless communication. The smart jacket aims for providing reliable health monitoring as well as a comfortable clinical environment for neonatal care and parent-child interaction. We present the first version of the neonatal jacket that enables ECG measurement by textile electrodes. We also explore a new solution for skin-contact challenges that textile electrodes pose. The jacket is expandable with new wearable technologies and has aesthetics that appeal to parents and medical staff. An iterative design process in close contact with the users and experts lead to a balanced integration of technology, user focus and aesthetics. We demonstrate the prototype and the experimental results obtained in clinical setting.
{"title":"Smart Jacket Design for Neonatal Monitoring with Wearable Sensors","authors":"S. Bouwstra, Wei Chen, L. Feijs, Sidarto Bambang-Oetomo","doi":"10.1109/BSN.2009.40","DOIUrl":"https://doi.org/10.1109/BSN.2009.40","url":null,"abstract":"Critically ill new born babies admitted at the Neonatal Intensive Care Unit (NICU) are extremely tiny and vulnerable to external disturbance. Smart Jacket proposed in this paper is the vision of a wearable unobtrusive continuous monitoring system realized by body sensor networks (BSN) and wireless communication. The smart jacket aims for providing reliable health monitoring as well as a comfortable clinical environment for neonatal care and parent-child interaction. We present the first version of the neonatal jacket that enables ECG measurement by textile electrodes. We also explore a new solution for skin-contact challenges that textile electrodes pose. The jacket is expandable with new wearable technologies and has aesthetics that appeal to parents and medical staff. An iterative design process in close contact with the users and experts lead to a balanced integration of technology, user focus and aesthetics. We demonstrate the prototype and the experimental results obtained in clinical setting.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117114630","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}
This paper presents an expert body sensor network (BSN) algorithm for autonomous control of mean arterial blood pressure using vasoactive drugs. A robust Proportional Integral Differential (PID) control algorithm based on a statistics model was designated. Extensive simulation results indicated that the light-weighted expert BSN system is robust enough against sensitivity variations and various artificial disturbances.
{"title":"A Robust Closed-Loop Control Algorithm for Mean Arterial Blood Pressure Regulation","authors":"Guan-Zheng Liu, Lei Wang, Yuan-Ting Zhang","doi":"10.1109/BSN.2009.61","DOIUrl":"https://doi.org/10.1109/BSN.2009.61","url":null,"abstract":"This paper presents an expert body sensor network (BSN) algorithm for autonomous control of mean arterial blood pressure using vasoactive drugs. A robust Proportional Integral Differential (PID) control algorithm based on a statistics model was designated. Extensive simulation results indicated that the light-weighted expert BSN system is robust enough against sensitivity variations and various artificial disturbances.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124795062","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}
Christine Ho, M. Mark, M. Koplow, L. Miller, A. Chen, E. Reilly, J. Rabaey, J. Evans, P. Wright
We present a design study highlighting our recent technological developments that will enable the implementation of autonomous wireless sensor networks for home healthcare monitoring systems. We outline the power requirements for a commercially available implantable glucose sensor which transmits measurements to an external wireless sensor node embedded in the home. A network of these sensor nodes will relay the data to a base station, such as a computer with internet connection, which will record and report this data to the user. We explore the feasibility of powering these sensors using energy scavenging from both body temperature gradients and vibrations in the home, and discuss our developments in energy storage and low power consuming hardware.
{"title":"Technologies for an Autonomous Wireless Home Healthcare System","authors":"Christine Ho, M. Mark, M. Koplow, L. Miller, A. Chen, E. Reilly, J. Rabaey, J. Evans, P. Wright","doi":"10.1109/BSN.2009.50","DOIUrl":"https://doi.org/10.1109/BSN.2009.50","url":null,"abstract":"We present a design study highlighting our recent technological developments that will enable the implementation of autonomous wireless sensor networks for home healthcare monitoring systems. We outline the power requirements for a commercially available implantable glucose sensor which transmits measurements to an external wireless sensor node embedded in the home. A network of these sensor nodes will relay the data to a base station, such as a computer with internet connection, which will record and report this data to the user. We explore the feasibility of powering these sensors using energy scavenging from both body temperature gradients and vibrations in the home, and discuss our developments in energy storage and low power consuming hardware.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114405893","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}
Shyamal Patel, C. Mancinelli, Jennifer Healey, M. Moy, P. Bonato
Chronic obstructive pulmonary disease (COPD) is a major public health problem. Early detection and treatment of an exacerbation in the outpatient setting are important to prevent worsening of clinical status and need for emergency room care or hospital admission. In this study we use accelerometers to capture motion data; and heart rate and respiration rate to capture physiological responses from patients with COPD as they perform a range of Activities of Daily Living (ADL) and physical exercises. We present a comparative analysis of classification performance of a set of different classification techniques and factors that affect classification performance for activity recognition based on accelerometer data. This is the first step towards building a wearable sensor monitoring system for tracking changes in physiological responses of patients with COPD with respect to their physical activity level.
{"title":"Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance","authors":"Shyamal Patel, C. Mancinelli, Jennifer Healey, M. Moy, P. Bonato","doi":"10.1109/BSN.2009.53","DOIUrl":"https://doi.org/10.1109/BSN.2009.53","url":null,"abstract":"Chronic obstructive pulmonary disease (COPD) is a major public health problem. Early detection and treatment of an exacerbation in the outpatient setting are important to prevent worsening of clinical status and need for emergency room care or hospital admission. In this study we use accelerometers to capture motion data; and heart rate and respiration rate to capture physiological responses from patients with COPD as they perform a range of Activities of Daily Living (ADL) and physical exercises. We present a comparative analysis of classification performance of a set of different classification techniques and factors that affect classification performance for activity recognition based on accelerometer data. This is the first step towards building a wearable sensor monitoring system for tracking changes in physiological responses of patients with COPD with respect to their physical activity level.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132959276","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}
A non-contact capacitive biopotential electrode with a common-mode noise suppression circuit is presented. The sensor network utilizes a single conductive sheet to establish a common body wide reference line, eliminating the need for an explicit signal ground connection. Each electrode senses the local biopotential with a differential gain of 46dB over a 1-100Hz bandwidth. Signals are digitized directly on board with a 16-bit ADC. The coin sized electrode consumes 285uA from a single 3.3V supply, and interfaces with a serial data bus for daisy-chain integration in body area sensor networks.
{"title":"Non-contact Low Power EEG/ECG Electrode for High Density Wearable Biopotential Sensor Networks","authors":"Y. Chi, S. Deiss, G. Cauwenberghs","doi":"10.1109/BSN.2009.52","DOIUrl":"https://doi.org/10.1109/BSN.2009.52","url":null,"abstract":"A non-contact capacitive biopotential electrode with a common-mode noise suppression circuit is presented. The sensor network utilizes a single conductive sheet to establish a common body wide reference line, eliminating the need for an explicit signal ground connection. Each electrode senses the local biopotential with a differential gain of 46dB over a 1-100Hz bandwidth. Signals are digitized directly on board with a 16-bit ADC. The coin sized electrode consumes 285uA from a single 3.3V supply, and interfaces with a serial data bus for daisy-chain integration in body area sensor networks.","PeriodicalId":269861,"journal":{"name":"2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116128394","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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