2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献_第7页

Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis 惯性体传感器在多发性硬化症诊断中增强步态评估可分离性的原因分析

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299400

Jiaqi Gong, J. Lach, Yanjun Qi, M. Goldman

{"title":"Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis","authors":"Jiaqi Gong, J. Lach, Yanjun Qi, M. Goldman","doi":"10.1109/BSN.2015.7299400","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299400","url":null,"abstract":"Gait assessment is a common method for diagnosing various diseases, disorders, and injuries, studying their impact on mobility, and evaluating the efficacy of various therapeutic interventions. The recent emergence of inertial body sensors for gait assessment addresses the limitations of visual observation and subjective clinical evaluation by providing more precise and objective measures. Inertial sensors have been included in an ongoing study at the University of Virginia Medical Center on Multiple Sclerosis (MS), a chronic autoimmune disorder of the central nervous system (CNS) that produces neurologic impairment and functional disability over time, with the goal of improving the ability to assess MS-affected gait and to distinguish between subjects with MS and those without MS. This work presents a gait assessment technique based on causal modeling to distinguish MS-affected gait and healthy gait. The approach in this work is based on the hypothesis that the strength of interaction between body parts during walking is greater in healthy controls that in MS subjects. The strength of interaction was quantified using a causality index based on the pairwise causal relationships between body parts as characterized by the Phase Slope Index (PSI) of inertial signals from pairs of body parts. In a pilot study with 41 subjects (28 MS subjects and 13 healthy controls), the approach developed in this paper provided better separability (p <; 0.0001) compared with existing methods.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"134 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":"121530816","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

Adaptive CSMA/CA MAC protocol to reduce inter-WBAN interference for wireless body area networks 自适应CSMA/CA MAC协议减少无线体域网络间wban干扰

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299349

Wen-Tien Huang, Tony Q. S. Quek

{"title":"Adaptive CSMA/CA MAC protocol to reduce inter-WBAN interference for wireless body area networks","authors":"Wen-Tien Huang, Tony Q. S. Quek","doi":"10.1109/BSN.2015.7299349","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299349","url":null,"abstract":"In this paper, we design a medium access control (MAC) layer prootocol for wireless body area networks (WBANs) to cope with inter-WBAN interference. Each WBAN consists of a coordinator and multiple sensor nodes. Interference happens when multiple nodes transmit to their coordinators at the same time. To avoid interference among WBANs, carrier sensing multiple access with collision avoidance (CSMA/CA) is implemented at the coordinator level; while for communications within each WBAN, the coordinator uses beacon messages to centrally arrange all the transmissions to avoid collisions. In the proposed protocol, the coordinator adaptively adjusts its frame length based on its perceived interference level. By doing so, a WBAN can enjoy a high throughput under a light interference level, while giving other WBANs a fair share of channel access chance when the number of surrounding WBANs is large. We further design a sensing scheme to reduce the sensing power consumption for the sensor nodes and discuss how to extend the protocol for multi-channel and QoS support. Finally the simulation results verify the effectiveness of the proposed protocol.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"68 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":"129365649","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: 29

Towards a real time kinect signature based human activity assessment at home 在家中实现基于kinect签名的实时人体活动评估

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299359

Gaddi Blumrosen, Y. Miron, M. Plotnik, N. Intrator

引用次数: 3

Extending optimistic transmission protocol for other movement patterns 为其他移动模式扩展乐观传输协议

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299388

TiongHoo Lim, I. Bate

引用次数: 0

Toward robust and platform-agnostic gait analysis 鲁棒与平台无关的步态分析

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299366

Yuchao Ma, Ramin Fallahzadeh, Hassan Ghasemzadeh

{"title":"Toward robust and platform-agnostic gait analysis","authors":"Yuchao Ma, Ramin Fallahzadeh, Hassan Ghasemzadeh","doi":"10.1109/BSN.2015.7299366","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299366","url":null,"abstract":"Biometric gait analysis using wearable sensors offers an objective and quantitative method for gait parameter extraction. However, current techniques are constrained to specific platform parameters, and hence significantly lack generality, scalability and sustainability. In this paper, we propose a platform-independent and self-adaptive approach for gait cycle detection and cadence estimation. Our algorithm utilizes physical kinematic properties and cyclic patterns of foot acceleration signals to automatically adjust internal parameters of the algorithm. As a result, the proposed approach is robust to noise and changes in sensor platform parameters such as sampling rate and sensor resolution. For the evaluation purpose, we use acceleration signals collected from 16 subjects in a clinical setting to examine the accuracy and robustness of the proposed algorithm. The results show that our approach achieves a precision above 98% and a recall above 95% in stride detection, and an average accuracy of 98% in cadence estimation under various uncertainty conditions such as noisy signals and changes in sampling frequency and sensor resolution.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"13 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":"133574716","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

Novel approaches to measure acoustic emissions as biomarkers for joint health assessment 测量声发射作为关节健康评估生物标志物的新方法

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299389

Caitlin N. Teague, Sinan Hersek, H. Toreyin, M. Millard-Stafford, Michael L. Jones, G. Kogler, M. Sawka, O. Inan

{"title":"Novel approaches to measure acoustic emissions as biomarkers for joint health assessment","authors":"Caitlin N. Teague, Sinan Hersek, H. Toreyin, M. Millard-Stafford, Michael L. Jones, G. Kogler, M. Sawka, O. Inan","doi":"10.1109/BSN.2015.7299389","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299389","url":null,"abstract":"The ultimate objective of this research is to quantify changes in joint sounds during recovery from musculoskeletal injury, and to then use the characteristics of such sounds as a biomarker for quantifying joint rehabilitation progress. This paper focuses on the robust measurement of joint acoustic emissions using miniature microphones placed on the knee and interfaced to custom hardware. Two types of microphones were investigated: (1) miniature microphones with a sound port for detecting airborne sounds; and (2) piezoelectric film based contact microphones for detecting skin vibrations associated with internal sounds. Additionally, inertial measurements were taken simultaneously with joint sounds to observe the consistency in the acoustic emissions in the context of particular activities: knee flexion / extension (without load) and multi-joint weighted movement involving knee and hip flexion / extension (i.e. sit-to-stand). The preliminary data demonstrated that high quality joint sound measurements can be obtained with unique and repeatable acoustic signatures in healthy and injured joints. Additionally, the results suggest that combining piezoelectric contact microphones (which detect high quality acoustic emission signals directly from the skin vibrations but can be compromised with loss of skin contact) and electret microphones (which measure lower signal-to-noise ratio airborne sounds from the joint but can even measure such sounds at 5 cm distance from the skin) can provide robust measurements for a future wearable system to assess joint health in patients during rehabilitation at home.","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":"114444877","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: 15

Kinematic body sensor networks and behaviourmetrics for objective efficacy measurements in neurodegenerative disease drug trials 神经退行性疾病药物试验中用于客观疗效测量的运动学身体传感器网络和行为度量

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299426

Constantinos Gavriel, Andreas A. C. Thomik, P. Lourenço, S. Nageshwaran, Stavros Athanasopoulos, Anastasia Sylaidi, R. Festenstein, A. Faisal

{"title":"Kinematic body sensor networks and behaviourmetrics for objective efficacy measurements in neurodegenerative disease drug trials","authors":"Constantinos Gavriel, Andreas A. C. Thomik, P. Lourenço, S. Nageshwaran, Stavros Athanasopoulos, Anastasia Sylaidi, R. Festenstein, A. Faisal","doi":"10.1109/BSN.2015.7299426","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299426","url":null,"abstract":"We have deployed body sensor network (BSN) technology in clinical trials and developed behavioural analytics to quantify and monitor longitudinally the progression of Friedreich's Ataxia (FRDA) outside the lab. Patients and their carers administered themselves our ETHO1 wireless BSN and we captured motion time-series from patient sleep. We extracted behavioural biomarkers that objectively capture the progression of the disease throughout time and compares well with the SARA clinical scale gold-standard. Such clinical scales require patients to go through a series of lengthy tasks where clinicians observe patients' performance and aggregate a score that represents the stage of the disease. Unfortunately, such scales have been shown to be inconsistent across and within clinicians, as they are observation based subjective measures: Scales are highly dependent on the assessor's experience and they also have low sensitivity and resolution that fails to capture the slow disease progression in short periods of time, requiring longer clinical testing time frames. Using the neurobehavioural data we collected in our clinical trials, we extracted three behavioural biomarkers (MIM, SIM & KIM) based on patient movement intensity, activity and stillness while in bed. Our behavioural biomarkers correlation with the SARA clinical scale allows us to capture the disease progression in FRDA patients and establishes a proof of concept for BSN technology that we are applying towards more rapid efficacy measurements of drugs.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"12 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":"116175874","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: 4

Impact of sensor misplacement on estimating metabolic equivalent of task with wearables 传感器错位对可穿戴设备任务代谢当量估算的影响

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299385

Parastoo Alinia, Ramyar Saeedi, B. Mortazavi, Seyed Ali Rokni, Hassan Ghasemzadeh

{"title":"Impact of sensor misplacement on estimating metabolic equivalent of task with wearables","authors":"Parastoo Alinia, Ramyar Saeedi, B. Mortazavi, Seyed Ali Rokni, Hassan Ghasemzadeh","doi":"10.1109/BSN.2015.7299385","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299385","url":null,"abstract":"Metabolic equivalent of task (MET) indicates the intensity of physical activities. This measurement is used in providing physical activity intervention in many chronic illnesses such as coronary heart disease, type-2 diabetes, and cancer. Due to the small size, portability, low power consumption, and low cost, wearable motion sensors are widely used to estimate MET values. However, one major obstacle in widespread adoption of current wearable monitoring systems is that the sensors must be worn on predefined locations on the body. This imposes much discomfort for users as they are not allowed to wear the sensors on their own desired body locations. In addition, non-adherence to the predefined location of the sensors results in significant reduction in the accuracy of physical activity monitoring. In this paper, we propose a framework for sensor location-independent MET estimation. We introduce a sensor localization approach that allows users to wear the sensors on different body locations without having to adhere to a specific installation protocol. We study how such an algorithm impacts the performance of MET estimation algorithms. Using daily physical activity data, we demonstrate that an automatic sensor localization algorithm decreases the estimation error of the MET calculation by a factor of 2.3 compared to the case without sensor localization. Furthermore, our sensor localization algorithm achieves an accuracy of 90.8% in detecting on-body locations of wearable sensors. The integration of sensor localization and MET estimation achieves an accuracy of 80% in calculating the MET values of daily physical activities.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"44 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":"122570389","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

Wearable chemical sensors: Characterization of heart rate electrodes using electrochemical impedance spectroscopy 可穿戴化学传感器:用电化学阻抗谱表征心率电极

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299357

J. Deignan, Javier Monedero, S. Coyle, Donal O'Gorman, D. Diamond, M. McBrearty

引用次数: 2

Monitoring cardio-respiratory and posture movements during sleep: What can be achieved by a single motion sensor 监测睡眠中的心肺和姿势运动:单个运动传感器可以实现什么

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299409

Zhiqiang Zhang, Guang-Zhong Yang

{"title":"Monitoring cardio-respiratory and posture movements during sleep: What can be achieved by a single motion sensor","authors":"Zhiqiang Zhang, Guang-Zhong Yang","doi":"10.1109/BSN.2015.7299409","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299409","url":null,"abstract":"Quality of sleep is an important index of wellbeing and health. Irregular sleep patterns are often associated with stress and disorders such as cardiovascular disease, diabetes, depression, sleep apnea and obesity. In addition to key physiological indices, body movements and posture during sleep are also important for assessing causal relationship of irregular sleep patterns and underlying health issues. In this paper, we explore the feasibility of using a single accelerometer strapped onto the chest to detect posture and cardio-respiratory parameters during sleep. An efficient movement detector suitable for on-node implementation is developed to distinguish static postures from dynamics movements. When in static postures, a linear discriminant analysis (IDA) classifier is used to further divide the static postures into four common sleeping positions. Simultaneously, both heart rate and respiratory rate are extracted from the acceleration signal. A small cohort of 7 healthy subjects were recruited for lab-controlled experiments to evaluate the performance of our proposed methods. ECG signal and K4b2 system's V02 measurements were also collected to extract heart rate and respiratory rate as the ground truth for comparison. An overall classification accuracy of 99% is achieved for recognising the correct sleeping positions. Good matches to ground truths were also obtained for the derived cardiac and respiratory rates.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"40 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":"128121372","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: 25