Pub Date : 2013-05-06DOI: 10.1109/BSN.2013.6575521
Jason Biddle, D. Brigada, A. Lapadula, M. Buller, Stephen Mullen
Dismounted warfighters face a variety of environmental and physical challenges that can degrade performance and lead to serious injury. Real-time monitoring of physiological status can be a key component of reducing these risks. To these ends, a Real-Time Physiological Status Monitoring (RT-PSM) system named OBAN (Open Body Area Network) is being developed. This system utilizes an Open Systems Architecture approach which will allow for the inclusion of new sensor modalities and display form factors at low cost. A prototype has been built using both Commercial-Off-The-Shelf (COTS) and custom-designed sensors to demonstrate the feasibility of this approach. The current system accepts heart rate data from a commercial sensor to calculate the subject's Physiological Strain Index (PSI), which is an indication of susceptibility to heat injury, and data from custom, boot-mounted load sensors. COTS components were adapted to create the system's networking and computational modules. Limitations of the existing prototype are described and a path forward addressing the operational needs of warfighters is proposed.
{"title":"OBAN: An open architecture prototype for a tactical body sensor network","authors":"Jason Biddle, D. Brigada, A. Lapadula, M. Buller, Stephen Mullen","doi":"10.1109/BSN.2013.6575521","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575521","url":null,"abstract":"Dismounted warfighters face a variety of environmental and physical challenges that can degrade performance and lead to serious injury. Real-time monitoring of physiological status can be a key component of reducing these risks. To these ends, a Real-Time Physiological Status Monitoring (RT-PSM) system named OBAN (Open Body Area Network) is being developed. This system utilizes an Open Systems Architecture approach which will allow for the inclusion of new sensor modalities and display form factors at low cost. A prototype has been built using both Commercial-Off-The-Shelf (COTS) and custom-designed sensors to demonstrate the feasibility of this approach. The current system accepts heart rate data from a commercial sensor to calculate the subject's Physiological Strain Index (PSI), which is an indication of susceptibility to heat injury, and data from custom, boot-mounted load sensors. COTS components were adapted to create the system's networking and computational modules. Limitations of the existing prototype are described and a path forward addressing the operational needs of warfighters is proposed.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125218939","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575505
R. Brugarolas, R. Loftin, Pu Yang, D. Roberts, B. Sherman, A. Bozkurt
Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.
{"title":"Behavior recognition based on machine learning algorithms for a wireless canine machine interface","authors":"R. Brugarolas, R. Loftin, Pu Yang, D. Roberts, B. Sherman, A. Bozkurt","doi":"10.1109/BSN.2013.6575505","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575505","url":null,"abstract":"Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117341761","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575473
A. Arami, A. Barré, Roderik Berthelin, K. Aminian
In this work, we studied a combination of embedded magnetic measurement system in a knee prosthesis and wearable inertial sensors to estimate two knee joint rotations namely flexion-extension and internal-external rotations. The near optimal sensor configuration was designed for implantable measurement system, and linear estimators were used to estimate the mentioned angles. This system was separately evaluated in a mechanical knee simulator and the effect of the imposed Abduction-Adduction rotation was also studied on the angle estimations. To reduce the power consumption of the internal system, we reduced the sampling rate and duty cycled the implantable sensors. Then we compensated the lack of information via use of kinematic information from wearable sensors to provide accurate angle estimations. As long as this smart prosthesis is not implanted yet on a subject, the angles estimations from implantable sensors and wearable sensors are realistically simulated for four subjects. The simulated angle estimations were fed to the designed data fusion algorithms to boost the estimation performance. The results were considerably improved via use of Maximum Entropy Ordered Weighted Averaging (MEOWA) fusion for flexion angles, but not for internal-external angle estimations.
{"title":"Estimation of prosthetic knee angles via data fusion of implantable and wearable sensors","authors":"A. Arami, A. Barré, Roderik Berthelin, K. Aminian","doi":"10.1109/BSN.2013.6575473","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575473","url":null,"abstract":"In this work, we studied a combination of embedded magnetic measurement system in a knee prosthesis and wearable inertial sensors to estimate two knee joint rotations namely flexion-extension and internal-external rotations. The near optimal sensor configuration was designed for implantable measurement system, and linear estimators were used to estimate the mentioned angles. This system was separately evaluated in a mechanical knee simulator and the effect of the imposed Abduction-Adduction rotation was also studied on the angle estimations. To reduce the power consumption of the internal system, we reduced the sampling rate and duty cycled the implantable sensors. Then we compensated the lack of information via use of kinematic information from wearable sensors to provide accurate angle estimations. As long as this smart prosthesis is not implanted yet on a subject, the angles estimations from implantable sensors and wearable sensors are realistically simulated for four subjects. The simulated angle estimations were fed to the designed data fusion algorithms to boost the estimation performance. The results were considerably improved via use of Maximum Entropy Ordered Weighted Averaging (MEOWA) fusion for flexion angles, but not for internal-external angle estimations.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129724556","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575462
S. Thiemjarus, Apiwat Henpraserttae, S. Marukatat
This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the user's location can be displayed on both iPhone and the web application in real time.
{"title":"A study on instance-based learning with reduced training prototypes for device-context-independent activity recognition on a mobile phone","authors":"S. Thiemjarus, Apiwat Henpraserttae, S. Marukatat","doi":"10.1109/BSN.2013.6575462","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575462","url":null,"abstract":"This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the user's location can be displayed on both iPhone and the web application in real time.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115016852","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575478
Mladen Milošević, E. Jovanov, A. Milenković
Timed-Up-and-Go (TUG) is a simple, easy to administer, and frequently used test for assessing balance and mobility in elderly and people with Parkinson's disease. An instrumented version of the test (iTUG) has been recently introduced to better quantify subject's movements during the test. The subject is typically instrumented by a dedicated device designed to capture signals from inertial sensors that are later analyzed by healthcare professionals. In this paper we introduce a smartphone application called sTUG that completely automates the iTUG test so it can be performed at home. sTUG captures the subject's movements utilizing smartphone's built-in accelerometer and gyroscope sensors, determines the beginning and the end of the test and quantifies its individual phases, and optionally uploads test descriptors into a medical database. We describe the parameters used to quantify the iTUG test and algorithms to extract the parameters from signals captured by the smartphone sensors.
time - up -and- go (TUG)是一种简单、易于管理、经常用于评估老年人和帕金森病患者平衡和活动能力的测试。最近引入了一种仪器版本的测试(iTUG)，以更好地量化测试期间受试者的运动。受试者通常由专用设备进行仪器检测，该设备旨在捕获来自惯性传感器的信号，随后由医疗保健专业人员进行分析。在本文中，我们介绍了一个名为sTUG的智能手机应用程序，它可以完全自动化iTUG测试，因此可以在家中进行测试。sTUG利用智能手机内置的加速度计和陀螺仪传感器捕捉受试者的运动，确定测试的开始和结束，并量化其各个阶段，并可选择将测试描述符上传到医疗数据库。我们描述了用于量化iTUG测试的参数，以及从智能手机传感器捕获的信号中提取参数的算法。
{"title":"Quantifying Timed-Up-and-Go test: A smartphone implementation","authors":"Mladen Milošević, E. Jovanov, A. Milenković","doi":"10.1109/BSN.2013.6575478","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575478","url":null,"abstract":"Timed-Up-and-Go (TUG) is a simple, easy to administer, and frequently used test for assessing balance and mobility in elderly and people with Parkinson's disease. An instrumented version of the test (iTUG) has been recently introduced to better quantify subject's movements during the test. The subject is typically instrumented by a dedicated device designed to capture signals from inertial sensors that are later analyzed by healthcare professionals. In this paper we introduce a smartphone application called sTUG that completely automates the iTUG test so it can be performed at home. sTUG captures the subject's movements utilizing smartphone's built-in accelerometer and gyroscope sensors, determines the beginning and the end of the test and quantifies its individual phases, and optionally uploads test descriptors into a medical database. We describe the parameters used to quantify the iTUG test and algorithms to extract the parameters from signals captured by the smartphone sensors.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127884664","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575510
Haoshi Zhang, Lan Tian, Liangqing Zhang, Guanglin Li
Wearable systems based on continuously monitoring of vital physiological signals without interfering with user's daily life much are desired urgently in health care. Similarly, the limb amputees who need to wear their myoelectric prostheses for a long time daily expect a comfortable and reliable prosthetic system. It is inconvenient in clinical application of a myoelectric prosthesis to use the commonly used gel electrode for electromyography (EMG) recording over all day. Textile electrode with characteristics of ventilation, flexibility, and folding, may be an ideal selection of physiological signal monitoring in clinical applications. In this study, the textile electrodes made using screen printing technology were used for EMG recordings and the real-time performance of the textile-electrode EMG in myoelectric control of multifunctional prostheses was investigated in transradial amputees and able-bodied subjects for comparison purpose. The results over seven able-bodied subjects showed that the textile electrode could achieve similar performance as conventional metal electrodes for both the off-line classification accuracy and the real-time motion completion rate in operating a virtual hand. With the textile electrodes, the average off-line classification accuracy of 73.4% and the real-time motion completion rate of 81.9% within a 5 s time limit were achieved in three transradial amputees. These pilot results suggested that the textile electrodes might be feasible for EMG recordings in control of myoelectric prostheses.
{"title":"Using textile electrode EMG for prosthetic movement identification in transradial amputees","authors":"Haoshi Zhang, Lan Tian, Liangqing Zhang, Guanglin Li","doi":"10.1109/BSN.2013.6575510","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575510","url":null,"abstract":"Wearable systems based on continuously monitoring of vital physiological signals without interfering with user's daily life much are desired urgently in health care. Similarly, the limb amputees who need to wear their myoelectric prostheses for a long time daily expect a comfortable and reliable prosthetic system. It is inconvenient in clinical application of a myoelectric prosthesis to use the commonly used gel electrode for electromyography (EMG) recording over all day. Textile electrode with characteristics of ventilation, flexibility, and folding, may be an ideal selection of physiological signal monitoring in clinical applications. In this study, the textile electrodes made using screen printing technology were used for EMG recordings and the real-time performance of the textile-electrode EMG in myoelectric control of multifunctional prostheses was investigated in transradial amputees and able-bodied subjects for comparison purpose. The results over seven able-bodied subjects showed that the textile electrode could achieve similar performance as conventional metal electrodes for both the off-line classification accuracy and the real-time motion completion rate in operating a virtual hand. With the textile electrodes, the average off-line classification accuracy of 73.4% and the real-time motion completion rate of 81.9% within a 5 s time limit were achieved in three transradial amputees. These pilot results suggested that the textile electrodes might be feasible for EMG recordings in control of myoelectric prostheses.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131638699","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575459
Maggie K. Delano, C. Sodini
A low-power, wearable electrocardiogram (ECG) monitor was developed for long-term data acquisition and analysis. It was designed to maximize both comfort and ECG signal quality, and minimize obtrusiveness. The monitor consists of a central PCB that contains one electrode and most of the electronics. Two additional satellite PCBs house the remaining electrodes and buffer circuits and complete the system. It consumes 7.3 mW and can record single lead ECG for over one week under a variety of activity levels. A clinical test was performed to validate the monitor. Participants (N = 6) wore both the experimental cardiac monitor and a commercially available monitor while engaging in physical activities such as walking, stepping, and running. QRS sensitivity and QRS positive predictability were determined for each ECG waveform. The monitor performed as well or better than the commercial monitor in all interventions. It performed well even under high activity levels such as running, and may be a viable alternative to commercially available monitors.
{"title":"A long-term wearable electrocardiogram measurement system","authors":"Maggie K. Delano, C. Sodini","doi":"10.1109/BSN.2013.6575459","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575459","url":null,"abstract":"A low-power, wearable electrocardiogram (ECG) monitor was developed for long-term data acquisition and analysis. It was designed to maximize both comfort and ECG signal quality, and minimize obtrusiveness. The monitor consists of a central PCB that contains one electrode and most of the electronics. Two additional satellite PCBs house the remaining electrodes and buffer circuits and complete the system. It consumes 7.3 mW and can record single lead ECG for over one week under a variety of activity levels. A clinical test was performed to validate the monitor. Participants (N = 6) wore both the experimental cardiac monitor and a commercially available monitor while engaging in physical activities such as walking, stepping, and running. QRS sensitivity and QRS positive predictability were determined for each ECG waveform. The monitor performed as well or better than the commercial monitor in all interventions. It performed well even under high activity levels such as running, and may be a viable alternative to commercially available monitors.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852125","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575486
Christina Strohrmann, Shyamal Patel, C. Mancinelli, L. Deming, J. Chu, R. Greenwald, G. Tröster, P. Bonato
Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.
{"title":"Automated assessment of gait deviations in children with cerebral palsy using a sensorized shoe and Active Shape Models","authors":"Christina Strohrmann, Shyamal Patel, C. Mancinelli, L. Deming, J. Chu, R. Greenwald, G. Tröster, P. Bonato","doi":"10.1109/BSN.2013.6575486","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575486","url":null,"abstract":"Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123833969","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575511
P. Bagade, Ayan Banerjee, J. Milazzo, Sandeep K. S. Gupta
Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to a Body Sensor Network (BSN) and senses physiological signals through non-contact processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram (ECG) of one person gets coupled to the electroencephalogram (EEG) of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attacks on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN. Our results show that with a 30 second handshake we can break PSKA with an average probability of 0.3 (0.24 minimum and 0.5 maximum).
{"title":"Protect your BSN: No Handshakes, just Namaste!","authors":"P. Bagade, Ayan Banerjee, J. Milazzo, Sandeep K. S. Gupta","doi":"10.1109/BSN.2013.6575511","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575511","url":null,"abstract":"Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to a Body Sensor Network (BSN) and senses physiological signals through non-contact processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram (ECG) of one person gets coupled to the electroencephalogram (EEG) of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attacks on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN. Our results show that with a 30 second handshake we can break PSKA with an average probability of 0.3 (0.24 minimum and 0.5 maximum).","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134438845","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575503
Charence Wong, Zhiqiang Zhang, S. McKeague, Guang-Zhong Yang
Pervasive human motion capture in the workplace facilitates detailed analysis of the actions of individual subjects and team interaction. It is also important for ergonomic studies for assessing instrument design and workflow analysis. However, a busy, dynamic, team-based environment, such as the operating theatre poses a number of challenges for the currently used marker-based and sensor-based motion capture systems. Occlusions and sensor drift can affect the accuracy of the estimated motion. In this paper, we present a motion capture system that uses a vision-based head detection algorithm and a markerless inertial motion capture for estimating the motion of multiple people. The pose estimation obtained through inertial sensors is combined with location obtained through vision-based tracking to reconstruct the motion of each subject. A multi-target Kalman filter is used to track the movement of each subject. To handle the close proximity of the subjects, visual features associated with the body are used for data association. Experimental results demonstrate the accuracy of the proposed system.
{"title":"Multi-person vision-based head detector for markerless human motion capture","authors":"Charence Wong, Zhiqiang Zhang, S. McKeague, Guang-Zhong Yang","doi":"10.1109/BSN.2013.6575503","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575503","url":null,"abstract":"Pervasive human motion capture in the workplace facilitates detailed analysis of the actions of individual subjects and team interaction. It is also important for ergonomic studies for assessing instrument design and workflow analysis. However, a busy, dynamic, team-based environment, such as the operating theatre poses a number of challenges for the currently used marker-based and sensor-based motion capture systems. Occlusions and sensor drift can affect the accuracy of the estimated motion. In this paper, we present a motion capture system that uses a vision-based head detection algorithm and a markerless inertial motion capture for estimating the motion of multiple people. The pose estimation obtained through inertial sensors is combined with location obtained through vision-based tracking to reconstruct the motion of each subject. A multi-target Kalman filter is used to track the movement of each subject. To handle the close proximity of the subjects, visual features associated with the body are used for data association. Experimental results demonstrate the accuracy of the proposed system.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114597724","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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