2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献

Detecting and tracking gait asymmetries with wearable accelerometers 基于可穿戴加速度计的步态不对称检测与跟踪

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299355

J. Williamson, Andrew Dumas, A. Hess, Tejash Patel, B. Telfer, M. Buller

引用次数: 4

Wearable motion capture unit for shoulder injury prevention 防止肩部受伤的可穿戴运动捕捉装置

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299417

S. Rawashdeh, Derek A. Rafeldt, T. Uhl, J. Lumpp

引用次数: 11

An unsupervised approach for gait-based authentication 基于步态的无监督认证方法

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299423

Guglielmo Cola, M. Avvenuti, Alessio Vecchio, Guang-Zhong Yang, Benny P. L. Lo

引用次数: 23

Assessment of the e-AR sensor for gait analysis of Parkinson;s Disease patients e-AR传感器在帕金森病患者步态分析中的应用评估

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299396

D. Jarchi, Amy Peters, Benny P. L. Lo, E. Kalliolia, I. D. Giulio, P. Limousin, B. Day, Guang-Zhong Yang

引用次数: 7

A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities A-Wristocracy:基于腕带传感的深度学习，用于识别用户复杂活动

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299406

Praneeth Vepakomma, Debraj De, Sajal K. Das, S. Bhansali

{"title":"A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities","authors":"Praneeth Vepakomma, Debraj De, Sajal K. Das, S. Bhansali","doi":"10.1109/BSN.2015.7299406","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299406","url":null,"abstract":"In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6-12 activities detected by state-of-the-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129522642","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: 111

EMI Spy: Harnessing electromagnetic interference for low-cost, rapid prototyping of proxemic interaction 电磁干扰间谍:利用电磁干扰低成本，快速原型的近距离相互作用

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299402

Nan Zhao, G. Dublon, N. Gillian, A. Dementyev, J. Paradiso

{"title":"EMI Spy: Harnessing electromagnetic interference for low-cost, rapid prototyping of proxemic interaction","authors":"Nan Zhao, G. Dublon, N. Gillian, A. Dementyev, J. Paradiso","doi":"10.1109/BSN.2015.7299402","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299402","url":null,"abstract":"We present a wearable system that uses ambient electromagnetic interference (EMI) as a signature to identify electronic devices and support proxemic interaction. We designed a low cost tool, called EMI Spy, and a software environment for rapid deployment and evaluation of ambient EMI-based interactive infrastructure. EMI Spy captures electromagnetic interference and delivers the signal to a user's mobile device or PC through either the device's wired audio input or wirelessly using Bluetooth. The wireless version can be worn on the wrist, communicating with the user;s mobile device in their pocket. Users are able to train the system in less than 1 second to uniquely identify displays in a 2-m radius around them, as well as to detect pointing at a distance and touching gestures on the displays in real-time. The combination of a low cost EMI logger and an open source machine learning tool kit allows developers to quickly prototype proxemic, touch-to-connect, and gestural interaction. We demonstrate the feasibility of mobile, EMI-based device and gesture recognition with preliminary user studies in 3 scenarios, achieving 96% classification accuracy at close range for 6 digital signage displays distributed throughout a building, and 90% accuracy in classifying pointing gestures at neighboring desktop LCD displays. We were able to distinguish 1- and 2-finger touching with perfect accuracy and show indications of a way to determine power consumption of a device via touch. Our system is particularly well-suited to temporary use in a public space, where the sensors could be distributed to support a popup interactive environment anywhere with electronic devices. By designing for low cost, mobile, flexible, and infrastructure-free deployment, we aim to enable a host of new proxemic interfaces to existing appliances and displays","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128702144","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: 13

On constructing interference free schedule for coexisting wireless body area networks using distributed coloring algorithm 利用分布式着色算法构建共存无线体域网络的无干扰调度

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299422

Wen-Tien Huang, Tony Q. S. Quek

引用次数: 11

A restricted Boltzmann machine based two-lead electrocardiography classification 基于受限玻尔兹曼机的双导联心电图分类

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299399

Yan Yan, Xin Qin, Yige Wu, Nannan Zhang, Jianping Fan, Lei Wang

引用次数: 49

A method for automatic, objective and continuous scoring of bradykinesia 一种运动迟缓的自动、客观、连续评分方法

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299358

O. M. Manzanera, E. Roosma, M. Beudel, R. Borgemeester, T. Laar, N. Maurits

{"title":"A method for automatic, objective and continuous scoring of bradykinesia","authors":"O. M. Manzanera, E. Roosma, M. Beudel, R. Borgemeester, T. Laar, N. Maurits","doi":"10.1109/BSN.2015.7299358","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299358","url":null,"abstract":"The assessment of bradykinesia is a key element in the diagnosis of Parkinson's disease. It is typically performed using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, despite its importance, the bradykinesia-related items of this scale show very low inter-rater agreement. Therefore, in this study a method for automatic, objective and continuous scoring of three of the bradykinesia-related items of the MDS-UPDRS is proposed. Four clinicians scored these items for 25 patients diagnosed with Parkinson's disease, within a range of 0-4. Orientation sensors were used to record movement during performance of each item. From the recorded data a set of features was derived to represent the movement characteristics that evaluators assess for scoring bradykinesia according to the MDS-UPDRS. These features and the averaged scores of the evaluators were used to create a model for the score on each item using backward linear regression. The estimated generalization errors indicate that the continuous objective scale can obtain an automatic score with an average error of 0.50 compared to the evaluators' averaged scores.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133843944","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: 7

Application of a wireless BSN for gait and balance assessment in the elderly 无线BSN在老年人步态和平衡评估中的应用

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299386

M. Caldara, P. Locatelli, D. Comotti, M. Galizzi, V. Re, N. Dellerma, A. Corenzi, M. Pessione

引用次数: 6