2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献_第3页

Measuring muscle stiffness by linear mechanical perturbation 用线性机械扰动测量肌肉僵硬度

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299407

Luke M. Mooney, S. L. Ku, Madeleine Abromowitz, Jacob A. Mooney, Xu Sun, Qifang Bao

引用次数: 1

Evaluating squat performance with a single inertial measurement unit 用单一惯性测量单元评估深蹲性能

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299380

M. O'Reilly, D. Whelan, Charalampos Chanialidis, N. Friel, E. Delahunt, T. Ward, B. Caulfield

{"title":"Evaluating squat performance with a single inertial measurement unit","authors":"M. O'Reilly, D. Whelan, Charalampos Chanialidis, N. Friel, E. Delahunt, T. Ward, B. Caulfield","doi":"10.1109/BSN.2015.7299380","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299380","url":null,"abstract":"Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate whether a single lumbar-worn IMU is capable of identifying seven commonly observed squatting deviations. Twenty-two volunteers (18 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2 kg) performed the squat exercise correctly and with 7 induced deviations. IMU signal features were extracted for each condition. Statistical analysis and leave one subject out classifier evaluation were used to assess the ability of a single sensor to evaluate performance. Binary level classification was able to distinguish between correct and incorrect squatting performance with a sensitivity of 64.41%, specificity of 88.01% and accuracy of 80.45%. Multi-label classification was able to distinguish between specific squat deviations with a sensitivity of 59.65%, specificity of 94.84% and accuracy of 56.55%. These results indicate that a single IMU can successfully discriminate between squatting deviations. A larger data set must be collected and more complex classification techniques developed in order to create a more robust exercise analysis IMU-based system.","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":"128159985","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: 37

FLogFS: A lightweight flash log file system FLogFS:轻量级的flash日志文件系统

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299353

Benjamin Nahill, Z. Zilic

引用次数: 3

Classification of spasticity affected EMG-signals 痉挛的分类影响肌电信号

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299365

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

{"title":"Classification of spasticity affected EMG-signals","authors":"Markus J. Lüken, B. Misgeld, S. Leonhardt","doi":"10.1109/BSN.2015.7299365","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299365","url":null,"abstract":"Electromyography (EMG) is used as medical tool to display muscle activity and gain information about the health status of the patients muscle function, which may be affected by many kind of diseases. Spasticity is caused by injuries of the central nervous system, which may occur in consequence of stroke or as concomitant of multiple sclerosis. If the muscle function is influenced by spasticity, there are different types of therapy to regain muscle control. For robotic supported rehabilitation, such as provided by diverse exoskeleton applications, it is important to identify spastic muscle activity patterns, in order to protect patients against mechanical injury. Therefore the EMG data of a hemiplegic patient was analysed, in order to find characteristic features of affected muscle activity and combine them to a characteristic feature vector. To classify the different states of muscle activity a Support Vector Machine (SVM) is used, trained with the feature vector space, which was created from the given EMG data. After that, the developed SVM was applied to data sets of patients also affected by spasticity in order to compare the obtained results to those estimated by a previously used algorithm for spasticity detection. Subsequently, the recognition capability of the implemented SVM was validated by a newly developed EMG sensor node for the IPANEMA Body Sensor Network (BSN).","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"67 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":"123420966","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

Low-complexity energy proportional posture/gesture recognition based on WBSN 基于WBSN的低复杂度能量比例姿态/手势识别

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299414

A. Aulery, J. Diguet, C. Roland, O. Sentieys

引用次数: 3

On the correlation between UPDRS scoring in the leg agility, sit-to-stand, and gait tasks for parkinsonians 关于UPDRS评分在帕金森患者腿敏捷性、坐立和步态任务中的相关性

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299401

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

引用次数: 1

A flexible tonoarteriography-based body sensor network for cuffless measurement of arterial blood pressure 一种灵活的基于张力动脉造影的身体传感器网络，用于动脉血压的无箍测量

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299405

Xiaorong Ding, Wenxuan Dai, Ningqi Luo, Jing Liu, Ni Zhao, Yuan-ting Zhang

{"title":"A flexible tonoarteriography-based body sensor network for cuffless measurement of arterial blood pressure","authors":"Xiaorong Ding, Wenxuan Dai, Ningqi Luo, Jing Liu, Ni Zhao, Yuan-ting Zhang","doi":"10.1109/BSN.2015.7299405","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299405","url":null,"abstract":"Recent advances in unobtrusive sensing technology, especially those in flexible, stretchable, and printable sensing, have given rise to various novel signal acquisition modalities, such as stretchable epidermal electrocardiography (ECG), organic photoplethysmography (PPG), and flexible tonoarteriography (TAG) which is the cuffless and continuous recording of arterial blood pressure (BP). With the fast development of wearable computing and wireless communication technologies, all these modalities can be integrated into a body sensor network (BSN) for remote physiological multi-parameter monitoring. In this paper, we propose a TAG-based BSN for unobtrusive BP measurement with possible automatic cuffless BP calibration, and our efforts focus on the effect of posture change on the various pulse transit time (PTT) calculated from different BSN nodes consisting of TAG, ECG, and PPG sensors. Specifically, correlations of different PTTs with reference continuous BP at different postures are examined. The results of this study demonstrate that the PTT from ECG and TAG sensors has higher correlation with the reference BP as compared to that from ECG and PPG sensors, which suggests that flexible TAG sensor may potentially be utilized not only for cuffless calibration, but also as an alternative node in the BSN for continuous, cuffless BP measurement with better accuracy.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"17 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":"133532229","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

A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication 一种基于MEMS惯性传感器和GPRS通信的可穿戴式碰撞坠落预警与保护系统

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299397

Mian Yao, Qi Zhang, Menghua Li, Huiqi Li, Yunkun Ning, Gaosheng Xie, Guoru Zhao, Yingnan Ma, Xing Gao, Zongzhen Jin

{"title":"A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication","authors":"Mian Yao, Qi Zhang, Menghua Li, Huiqi Li, Yunkun Ning, Gaosheng Xie, Guoru Zhao, Yingnan Ma, Xing Gao, Zongzhen Jin","doi":"10.1109/BSN.2015.7299397","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299397","url":null,"abstract":"Fall is one of great threats affecting people with old age. Aimed at the falling issue of aged, the paper explored a pre-impact fall early warning and protection system. This system consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardian's cellphone app. The early fall alarm and airbags are integrated in a belt, convenient for wearing and hip-protection. The inner of early fall alarm has a MEMS sensor, which collects 3-axis accelerated velocity and 3-axis angular velocity. A fall detection algorithm is applied to recognize falls from activities of daily living (ADL). When there is a dangerous movement approaching fall, the early fall alarm will warn the aged to stop the movement. When the fall happens, the early fall alarm will trigger the airbag system, then the airbags in the belt will inflate as soon as possible to reduce the damage to the aged. In addition, the early fall alarm will ring and send message to the guardian's cellphone for help. Meanwhile, the kinematics of the human body during falling time will be stored in TF card and sent to remote monitoring platform for storage. Then the monitoring platform can show the fall location where the fall incident happens in the electronic map. In order to test the reliability of this early fall alarm and protection system, a series of experiments have been designed. The results show that this system can be relatively accurate to detect falls, accomplishing functions including early fall warning and alarming, airbag inflation, statics transferring and storage, real-time location, which has significant benefit for reducing the direct damage and shortening the aiding time.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"33 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":"133142448","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

Design of a smart insole for ambulatory assessment of gait 用于步态动态评估的智能鞋垫设计

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299383

Y. S. A. Mustufa, J. Barton, B. O’flynn, R. Davies, P. Mccullagh, Huiru Zheng

引用次数: 27

A wearable wireless sensor for real time validation of bowling action in cricket 一种用于实时验证板球保龄球动作的可穿戴无线传感器

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299370

Abdullah Ahmed, Muhammad Asawal, M. Khan, H. M. Cheema

引用次数: 5