2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献

Investigating the feasibility of multiple UWB transmitters in brain computer interface (BCI) applications 研究多个超宽带发射机在脑机接口(BCI)应用中的可行性

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516266

Rutul Patel, Pragnesh V. Patel, Jinendar Lalwani, M. Sarkar, S. Nagaraj

引用次数: 8

Tracking body core temperature in military thermal environments: An extended Kalman filter approach 军用热环境下人体核心温度的跟踪:一种扩展的卡尔曼滤波方法

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516277

K. Seng, Ying Chen, K. M. A. Chai, Ting Wang, David Chiok Yuen Fun, Y. S. Teo, P. Tan, W. Ang, J. Lee

{"title":"Tracking body core temperature in military thermal environments: An extended Kalman filter approach","authors":"K. Seng, Ying Chen, K. M. A. Chai, Ting Wang, David Chiok Yuen Fun, Y. S. Teo, P. Tan, W. Ang, J. Lee","doi":"10.1109/BSN.2016.7516277","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516277","url":null,"abstract":"Military personnel operating in hot and humid environments are susceptible to heat-related illnesses. As heat-related illnesses are associated with a rise in body core temperature (Tc), a reliable system for real-time assessment of Tc is useful to minimize heat casualties. However, invasive measurement of Tc (such as rectal, intestinal and esophageal temperature) is impractical in the field settings. This paper describes the model construction and qualification results of tracking Tc using an extended Kalman filter (EKF) based on physiological data recorded from wearable sensors. Tc, surface skin temperature (Tsk) and heart rate (HR) data were collected from three studies with different experimental protocols, climatic conditions and soldier volunteers. The predictive performance of the model was evaluated by cross-validation and external validation. The final EKF model was implemented using a nonlinear (cubic) state-space model (Tsk versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR) as the time update model. Overall, when tested against an independent dataset, the model showed a prediction bias of 0.11°C, a root mean square deviation of 0.29°C, and 87% of all Tc predictions fell within ±0.3°C of the measured Tc values. The results from our study indicate that the derived EKF model is accurate enough to calculate subject-specific Tc for the minimization of heat injuries.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"75 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114005508","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}

引用次数: 9

Non-invasive biomedical patch sensor to measure intracranial pressure 无创生物医学贴片传感器测量颅内压

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516261

Jacob L. Griffith, A. Wakim, P. Moore-Jansen, K. Cluff

引用次数: 6

Towards a wearable system for continuous monitoring of sniffing and panting in dogs 研制一种可穿戴系统，用于连续监测狗的嗅探和喘气

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516276

R. Brugarolas, M. T. Agcayazi, S. Yuschak, D. Roberts, B. Sherman, A. Bozkurt

{"title":"Towards a wearable system for continuous monitoring of sniffing and panting in dogs","authors":"R. Brugarolas, M. T. Agcayazi, S. Yuschak, D. Roberts, B. Sherman, A. Bozkurt","doi":"10.1109/BSN.2016.7516276","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516276","url":null,"abstract":"Although numerous advances have been made in instrumental odor detection systems, these still cannot match the efficient sampling, odor discrimination, agile mobility and the olfactory acuity of odor detection dogs. A limiting step in using dogs to detect odors is the subjectivity of the translation of odor information processed by the dog to its handler. We present our preliminary efforts towards a wireless wearable system for continuous auscultation of respiratory behavior by recording internal sounds at the neck and chest by means of a commercially available electronic stethoscope to provide objective decision support for handlers. We have identified discrete features of sniffing and panting in the time domain and utilize event duration, event rate, event mean energy, and the number of consecutive events in a row to build a decision tree classifier. Since feature extraction requires segmentation of individual sniffing and panting events, we developed an adaptive method using short-time energy contour and an adaptive threshold. The performance of the system was evaluated on recordings from a Greyhound and a Labrador Retriever and achieved high classification accuracies.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"31 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":"130225729","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}

引用次数: 9

Determining physiological significance of inertial gait features in multiple sclerosis 确定多发性硬化症惯性步态特征的生理意义

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516271

Sriram Raju Dandu, M. Engelhard, M. Goldman, J. Lach

引用次数: 5

Tracking the behavior of UWB transmissions in invasive BCI applications 在侵入性脑机接口应用中追踪超宽带传输的行为

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516260

Pragnesh V. Patel, J. A. Kumar, M. Sarkar, S. Nagaraj

{"title":"Tracking the behavior of UWB transmissions in invasive BCI applications","authors":"Pragnesh V. Patel, J. A. Kumar, M. Sarkar, S. Nagaraj","doi":"10.1109/BSN.2016.7516260","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516260","url":null,"abstract":"Ultra wideband (UWB) radio technology has been shown to have tremendous potential to support certain Brain Computer Interface (BCI) applications. To implant UWB transmitters inside a human brain to transmit ECoG signals from the neurons in the brain (collected by bio-implantable electrodes) to stimulators residing on the spinal cord or on muscles at different parts of the body (non-invasive), almost sounds like a sci-fi story. In order to make this sci-fi dream a reality, many aspects of the problem needs to be addressed. In this paper, we attempt to study one such aspect. Specifically, we study the behavior of UWB transmissions through the human brain, primarily focusing on the behavior of the signal when it traverses through human blood in the brain before it can be received by the receiver planted on the surface of the human body (spinal cord, upper limbs, etc). We have performed in-depth numerical analysis through theoretical and experimental procedures which has helped us gain insight into the attenuation, delay and transmit power properties of the signal at different frequencies within the UWB spectrum. We present these results in this paper. So far, there are no channel models available for UWB transmissions inside the human body. Our work, is a step in that direction.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"218 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":"115634542","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}

引用次数: 5

Capture and analysis of biomechanical signals with inertial and magnetic sensors as support in physical rehabilitation processes 用惯性和磁传感器捕获和分析生物力学信号，作为物理康复过程的支持

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516244

M. C. Cuervo, J. C. Álvarez, D. Álvarez

引用次数: 13

Accurate gait analysis in post-stroke patients using a single inertial measurement unit 使用单一惯性测量单元对脑卒中后患者进行精确的步态分析

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516284

Federico Parisi, G. Ferrari, A. Baricich, M. D'Innocenzo, C. Cisari, A. Mauro

{"title":"Accurate gait analysis in post-stroke patients using a single inertial measurement unit","authors":"Federico Parisi, G. Ferrari, A. Baricich, M. D'Innocenzo, C. Cisari, A. Mauro","doi":"10.1109/BSN.2016.7516284","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516284","url":null,"abstract":"Improving independent mobility in post-stroke patients is one of the main goals of most rehabilitation strategies. While quantitative gait assessment is crucial to provide a meaningful feedback on the recovery progress, the irregularity of hemiparetic walking prevents the use of classical Inertial Measurement Unit (IMU)-based gait analysis algorithms. In this paper, we propose a novel low-cost system, which relies on a single wearable IMU attached to the lower trunk, to estimate spatio-temporal gait parameters of both hemiparetic and healthy subjects. A new procedure for temporal features' computation and two modified versions of well-known step length (i.e., spatial features) estimators are derived. In both cases, we exploit dynamic calibration constants, related to the “power” of an individual gait pattern, to deal with the typical asymmetry and inter-subject variability of hemiparetic gait. The spatio-temporal features estimated with the proposed methods are compared with ground-truth parameters extracted by an optoelectronic system. The obtained results show very high correlations between estimated and reference values.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"44 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":"116969621","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}

引用次数: 9

Identification of isolated biomechanical parameters with a wireless body sensor network 用无线身体传感器网络识别孤立生物力学参数

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516229

B. Misgeld, Markus J. Lüken, S. Leonhardt

{"title":"Identification of isolated biomechanical parameters with a wireless body sensor network","authors":"B. Misgeld, Markus J. Lüken, S. Leonhardt","doi":"10.1109/BSN.2016.7516229","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516229","url":null,"abstract":"The accurate, real-time estimation of biomechanical joint parameters bears a potential benefit for many applications. Examples include the assessment of training success in movement therapy, the use as a quantitative clinical scale for joint rigidity or the use in the derivation of control parameters for active, intelligent orthotic or prosthetic devices. Such a realtime assessment system should be as unobtrusive as possible, minimising instrumentation effort for the user or the clinical staff. Towards this goal we have build a body sensor network (BSN) that is able to measure surface electromyogram and 9-degrees of freedom inertial/magnetic data at high sample rates. The measured data is preprocessed and subsequently used in an Unscented Kalman Filter in a model-based approach employing the nonlinear dynamics of the human knee kinematics. The derivation of biomechanical joint parameters, in our case the knee stiffness, can then be readily obtained from the nonlinear model. To validate BSN measurements, we present a novel test-bench and its corresponding nonlinear model. The biomechanical parameter estimator is validated in pendulum like motions on the test-bench and in experiments where the test subject is undergoing co-activation of extensor and flexor muscles acting on the knee.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"68 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":"128653487","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}

引用次数: 1

Brain-controlled driving aid for electric wheelchairs 电动轮椅脑控驾驶辅助装置

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2016-06-14 DOI: 10.1109/BSN.2016.7516243

Nikhil Shinde, K. George

引用次数: 18