2014 11th International Conference on Wearable and Implantable Body Sensor Networks最新文献

Validation of the e-AR Sensor for Gait Event Detection Using the Parotec Foot Insole with Application to Post-Operative Recovery Monitoring e-AR传感器用于步态事件检测的验证，并应用于术后康复监测

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.16

D. Jarchi, Benny P. L. Lo, E. Ieong, D. Nathwani, Guang-Zhong Yang

{"title":"Validation of the e-AR Sensor for Gait Event Detection Using the Parotec Foot Insole with Application to Post-Operative Recovery Monitoring","authors":"D. Jarchi, Benny P. L. Lo, E. Ieong, D. Nathwani, Guang-Zhong Yang","doi":"10.1109/BSN.2014.16","DOIUrl":"https://doi.org/10.1109/BSN.2014.16","url":null,"abstract":"The use of e-AR (ear-worn activity recognition) sensor for gait pattern estimation has shown promise for a range of health and wellbeing applications. To establish its more detailed quantitative accuracy, an in-shoe pressure measurement system (Parotec) has been used to validate the estimated gait events from the e-AR sensor. Ten healthy adults equipped with Parotec and e-AR systems walked in a corridor of about 15m. The sampling frequency of both systems was set at 100Hz and a manual synchronisation has been performed for subsequent error measurements. The gait events from the e-AR sensor are estimated by using a recently developed method based on singular spectrum analysis and longest common subsequence algorithms [1]. The corresponding gait events from the Parotec system are estimated using the ground reaction forces. The upper and lower limits of absolute errors using 95% confidence intervals for heel contact and toe off events obtained as 35.38±3.22ms and 73.05±7.24ms respectively. We further provide a preliminary patient study to demonstrate how the estimated gait events and the gait analysis platform can be used for assessing patients recovering after orthopaedic surgery inside the clinic.","PeriodicalId":270751,"journal":{"name":"2014 11th International Conference on Wearable and Implantable Body Sensor Networks","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133705775","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: 14

Real-Time ECG and EMG Analysis for Biking Using Android-Based Mobile Devices 基于android的移动设备骑车的实时心电和肌电分析

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.20

R. Richer, Peter Blank, D. Schuldhaus, B. Eskofier

引用次数: 22

The Use of BSN for Whole Body Motion Training for a Humanoid Robot BSN在仿人机器人全身运动训练中的应用

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.12

K. Teachasrisaksakul, Zhiqiang Zhang, Guang-Zhong Yang

{"title":"The Use of BSN for Whole Body Motion Training for a Humanoid Robot","authors":"K. Teachasrisaksakul, Zhiqiang Zhang, Guang-Zhong Yang","doi":"10.1109/BSN.2014.12","DOIUrl":"https://doi.org/10.1109/BSN.2014.12","url":null,"abstract":"Sensor based motion capture system enables motion analysis with applications ranging from entertainment, healthcare and robotics. It can provide an intuitive interface for human to provide motion training and control a humanoid robot. In this paper, we propose a novel framework for the imitation of human motion for a humanoid robot. In the proposed framework, human motion data is directly captured from a wireless, wearable motion capture platform (Biomotion+). The reconstructed posture is then converted into joint angle trajectories. Due to the structural differences between the joints of the robot and those of the human, the trajectories are then optimized to satisfy the mechanical constraints of the robot and to maintain appropriate balance. To validate the proposed framework, different motion trajectories were verified. The results demonstrate the stability and effectiveness of the proposed framework to reproduce realistic human motion for a humanoid robot and the potential for a tele-rehabilitation application. The proposed framework offers a new way of imitating human motion for a humanoid robot.","PeriodicalId":270751,"journal":{"name":"2014 11th International Conference on Wearable and Implantable Body Sensor Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121345664","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: 3

Unsupervised Time Series Segmentation for High-Dimensional Body Sensor Network Data Streams 高维身体传感器网络数据流的无监督时间序列分割

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.34

D. Haber, Andreas A. C. Thomik, A. Faisal

{"title":"Unsupervised Time Series Segmentation for High-Dimensional Body Sensor Network Data Streams","authors":"D. Haber, Andreas A. C. Thomik, A. Faisal","doi":"10.1109/BSN.2014.34","DOIUrl":"https://doi.org/10.1109/BSN.2014.34","url":null,"abstract":"The vast amounts of data which can be collected using body-sensor networks with high temporal and spatial resolution require a novel analysis approach. In this context, state-of-the-art Bayesian approaches based on variational, non-parametric or MCMC derived methods often become computationally intractable when faced with several million data points. Here, we present how the simple combination of PCA, approximate Bayesian segmentation and temporal correlation processing can achieve reliable time series segmentation. We use our method, which relies on simple iterative covariance, correlation and maximum likelihood operations, to perform complex behavioural time series segmentation over millions of samples in 18 dimensions in linear time and space. Our approach is suitable for even higher dimensional data streams as performance scales near constantly with the dimensionality of the time series samples. We validate this novel approach on an artificially-generated time series and demonstrate that our method is very robust to noise and achieves a segmentation accuracy of over 86% of matching segments against ground-truth. We conclude that our approach makes Big Data driven approaches to stream processing Body Sensor Network (BSN) data tractable, and is required for BSN-driven Neurotechnology applications in Brain-Machine Interfacing and Neuroprosthetics.","PeriodicalId":270751,"journal":{"name":"2014 11th International Conference on Wearable and Implantable Body Sensor Networks","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131775503","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: 19

Experimental Validation of a Piezoelectric Frequency Up-Converting Rotational Harvester 压电变频旋转采集器的实验验证

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.30

P. Pillatsch, E. Yeatman, A. Holmes

引用次数: 5

A Comparison of Day-Long Recording Stability and Muscle Force Prediction between BSN-Based Mechanomyography and Electromyography 基于bsn的肌力图和肌电图全天记录稳定性和肌力预测的比较

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.23

Constantinos Gavriel, A. Faisal

{"title":"A Comparison of Day-Long Recording Stability and Muscle Force Prediction between BSN-Based Mechanomyography and Electromyography","authors":"Constantinos Gavriel, A. Faisal","doi":"10.1109/BSN.2014.23","DOIUrl":"https://doi.org/10.1109/BSN.2014.23","url":null,"abstract":"Day-long continuous monitoring requires stable sensors that can minimise the effects of drift and maintain high accuracy and precision over time. We have recently shown that our inertial motion tracking system can capture stable kinematic data, calibrated against ground-truth over a long period of time. However, for many clinical and daily life activities, it is also essential to monitor the muscle-activity. In this study, we evaluate the long-term recording stability of our prototype mechanomyography (MMG) sensors as an extension to our existing ETHO1 body sensor network platform. We attached the MMG sensors along with commercial high-accuracy EMG electrodes on the arm muscles of 5 subjects throughout a working day of 9 hours. The subjects followed their daily routine but they had to perform a multi-level force-matching task through flexion and extension of their arm during four short sessions of the day, as measures of practical signal quality. We designed a force predictor that used either EMG or MMG signals to predict the forces generated by subjects. Our prototype low-cost MMG channels have shown comparable results (RMSE: 23N and R2: 0.91) in predicting the force levels applied when compared against the commercial high-accuracy EMG sensor (RMSE: 19N and R2: 0.95).","PeriodicalId":270751,"journal":{"name":"2014 11th International Conference on Wearable and Implantable Body Sensor Networks","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125537790","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: 17

Mobile Health: Design of Flexible and Stretchable Electrophysiological Sensors for Wearable Healthcare Systems 移动医疗:可穿戴医疗系统的柔性和可拉伸电生理传感器的设计

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.25

Ningqi Luo, Jun Ding, Ni Zhao, Billy Leung, Carmen C. Y. Poon

{"title":"Mobile Health: Design of Flexible and Stretchable Electrophysiological Sensors for Wearable Healthcare Systems","authors":"Ningqi Luo, Jun Ding, Ni Zhao, Billy Leung, Carmen C. Y. Poon","doi":"10.1109/BSN.2014.25","DOIUrl":"https://doi.org/10.1109/BSN.2014.25","url":null,"abstract":"Flexible and stretchable sensors have recently emerged as an active area of research for designing wearable, implantable and ingestible systems that allow pervasive and unobtrusive health monitoring in mobile health (m-Health) applications. Nevertheless, there lacks systematical study that compares the performances of these new types of sensors with their conventional counterparts. In this study, we reported the fabrication of serpentine-shape, flexible and stretchable electrodes that can be conformally attached to the skin via a printing-transfer process, and we introduced Area Density (AD) as a key parameter to guide the future design of these sensors. Sensors of AD 10, 20 and 40 percentages were designed and fabricated. The performances of these sensors for capturing one type of electrophysiological signals, i.e. electrocardiogram (ECG), were compared against ECG obtained by conventional gold and stainless steel metal clips. The results of this study found that flexible sensors designed with larger AD resulted in ECG of higher signal-to-noise ratio (SNR). In particular, ECG with SNR comparable to those measured by conventional metal clips (SNR>25dB) can be achieved when this new type of flexible sensors were designed with AD=40%. The results of this study shed light on the design of novel wearable and flexible electrodes, which can be used not only for on-body sensing but also in-body measurement inside the gastrointestinal tract.","PeriodicalId":270751,"journal":{"name":"2014 11th International Conference on Wearable and Implantable Body Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130786916","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: 22

A Wearable Nutrition Monitoring System 可穿戴营养监测系统

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.26

H. Kalantarian, N. Alshurafa, M. Sarrafzadeh

引用次数: 77

Linking UPDRS Scores and Kinematic Variables in the Leg Agility Task of Parkinsonians 链接UPDRS得分和运动变量在帕金森患者的腿敏捷性任务

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.18

M. Giuberti, G. Ferrari, L. Contin, V. Cimolin, C. Azzaro, G. Albani, A. Mauro

引用次数: 16

Motion Based Acceleration Correction for Improved Sensor Orientation Estimates 基于运动的加速度校正改进传感器方向估计

2014 11th International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2014-06-16 DOI: 10.1109/BSN.2014.17

Terrell R. Bennett, R. Jafari, N. Gans

引用次数: 6