2013 IEEE International Conference on Body Sensor Networks最新文献_第4页

Measurement, instrumentation, control and analysis (MICA): A modular system of wireless sensors 测量、仪表、控制和分析(MICA):无线传感器的模块化系统

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575530

Adam Spanbauer, A. Wahab, Brian D. Hemond, I. Hunter, L. Jones

引用次数: 8

Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals 使用心肺和运动信号预测早产儿个体化呼吸暂停

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575523

J. Williamson, D. Bliss, David W. Browne, P. Indic, E. Bloch-Salisbury, D. Paydarfar

引用次数: 20

An efficient fault-tolerant sensor fusion algorithm for accelerometers 一种有效的加速度计容错传感器融合算法

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575513

O. Sarbishei, Benjamin Nahill, Atena Roshan Fekr, Majid Janidarmian, K. Radecka, Z. Zilic, B. Karajica

引用次数: 1

Unsupervised routine profiling in free-living conditions — Can smartphone apps provide insights? 在自由生活条件下无监督的日常分析——智能手机应用程序能提供洞察力吗?

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575506

R. Ali, Benny P. L. Lo, Guang-Zhong Yang

{"title":"Unsupervised routine profiling in free-living conditions — Can smartphone apps provide insights?","authors":"R. Ali, Benny P. L. Lo, Guang-Zhong Yang","doi":"10.1109/BSN.2013.6575506","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575506","url":null,"abstract":"In activity recognition and behaviour profiling studies, wearable inertial sensors are commonly used to monitor the subjects' daily activities. However, the need of carrying the sensing devices in addition to personal belongings may prohibit the widespread use of the technologies. On the other hand, smartphones have become ubiquitous and most smartphones are already equipped with similar inertial sensors. Recent studies have proposed the use of smartphone for quantifying the activity and behaviour of the users. A smartphone based long-term routine profiling system is proposed. To simplify the user interface and facilitate the ubiquitous use of the system, unsupervised and optimized techniques have been developed and integrated into a mobile phone application. By running the application continuously in the background of the phone, the system captures and processes the sensing information to infer the activities of the users, and the results are forwarded to the server for profiling the routines using pattern mining techniques. The proposed system is validated through a study of six users over two weeks. The ability of the proposed system in capturing routine behavior is demonstrated in the results of the study.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"3 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":"133394963","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

Multi-modal in-person interaction monitoring using smartphone and on-body sensors 使用智能手机和身体传感器的多模态面对面互动监测

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575509

Qiang Li, Shanshan Chen, J. Stankovic

引用次数: 16

Pattern classification of foot strike type using body worn accelerometers 用穿戴式加速度计对足击类型进行模式分类

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575457

B. Eskofier, Ed Musho, H. Schlarb

{"title":"Pattern classification of foot strike type using body worn accelerometers","authors":"B. Eskofier, Ed Musho, H. Schlarb","doi":"10.1109/BSN.2013.6575457","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575457","url":null,"abstract":"The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"3 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":"129150613","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: 12

Allowing early inspection of activity data from a highly distributed bodynet with a hierarchical-clustering-of-segments approach 允许使用分段分层聚类方法对高度分布的车身网络的活动数据进行早期检查

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575519

M. Kreil, Kristof Van Laerhoven, P. Lukowicz

引用次数: 1

Application of a pedometer in a clinical setting: Is the number of walking steps predictive of changes in blood pressure?: Prediction of blood pressure changes in blod presure by a peadmeter 计步器在临床中的应用:步行步数是否能预测血压的变化?用计步器预测血压的变化

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575471

T. Tamura, Yutaka Kimira, Yuichi Kimura, Soichi Maeno, T. Hattori, K. Minato

引用次数: 2

WristQue: A personal sensor wristband 腕带:个人感应腕带

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575483

Brian D. Mayton, Nan Zhao, M. Aldrich, N. Gillian, J. Paradiso

引用次数: 20

Functional regression for data fusion and indirect measurements of physiological variables collected by wearable sensor systems and indirect calorimetry 可穿戴传感器系统和间接量热法收集的生理变量的数据融合和间接测量的功能回归

2013 IEEE International Conference on Body Sensor Networks Pub Date : 2013-05-06 DOI: 10.1109/BSN.2013.6575464

A. Gribok, W. Rumpler, R. Hoyt, M. Buller

{"title":"Functional regression for data fusion and indirect measurements of physiological variables collected by wearable sensor systems and indirect calorimetry","authors":"A. Gribok, W. Rumpler, R. Hoyt, M. Buller","doi":"10.1109/BSN.2013.6575464","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575464","url":null,"abstract":"The paper describes application of different types of functional regression for analysis and modeling of the data collected by wearable sensor systems. The data have been recorded from human subjects while they were staying in whole room calorimeter chamber for 48 hours. This allowed very accurate measurements of their oxygen consumption, energy expenditure and substrate oxidation. These physiological parameters are notorious for their inaccuracy when measured in field conditions. The subjects wore two types of body sensors: the Hidalgo Equivital™ (Cambridge, UK) physiological monitors with a telemetry thermometer pill and iPro Professional Continuous Glucose Monitoring System (CGMS) (Medtronic MiniMed, Inc, Northridge, CA). The data collected by these two systems and by the calorimeter chamber were subsequently analyzed off-line using the functional regression techniques. The energy expenditure, substrate oxidation, and body core temperature were used as response variables, while heart rate, respiratory rate, subcutaneous glucose concentration, and skin temperature were used as predictors. The results show that the 24-hours and instantaneous energy expenditure values can be inferred from instantaneous measurements of heart rate, respiratory rate and glucose concentrations. Also, the body core temperature can be inferred from heart rate, respiratory rate, glucose concentration, and skin temperature. The substrate oxidation was the most difficult parameter to infer and it can only be accomplished during the exercise activity.","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":"132251222","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