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

Smartphone as an ultra-low cost medical tricorder for real-time cardiological measurements via ballistocardiography 智能手机作为一种超低成本的医疗三录仪，通过ballocardiography进行实时心脏测量

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299425

Constantinos Gavriel, K. Parker, A. Faisal

{"title":"Smartphone as an ultra-low cost medical tricorder for real-time cardiological measurements via ballistocardiography","authors":"Constantinos Gavriel, K. Parker, A. Faisal","doi":"10.1109/BSN.2015.7299425","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299425","url":null,"abstract":"In this preliminary study, we investigate the potential use of smartphones as portable heart-monitoring devices that can capture and analyse heart activity in real time. We have developed a smartphone application called “Medical Tricorder” that can exploit smartphone;s inertial sensors and when placed on a subject;s chest, it can efficiently capture the motion patterns caused by the mechanical activity of the heart. Using the measured ballistocardiograph signal (BCG), the application can efficiently extract the heart rate in real time while matching the performance of clinical-grade electrocardiographs (ECG). Although the BCG signal can provide much richer information regarding the mechanical aspects of the human heart, we have developed a method of mapping the chest BCG signal into an ECG signal, which can be made directly available to clinicians for diagnostics. Comparing the estimated ECG signal to empirical data from cardiovascular diseases, may allow detection of heart abnormalities at a very early stage without any medical staff involvement. Our method opens up the potential of turning smartphones into portable healthcare systems which can provide patients and general public an easy access to continuous healthcare monitoring. Additionally, given that our solution is mainly software based, it can be deployed on smartphones around the world with minimal costs.","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":"127541424","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: 10

Anticipatory signals in kinematics and muscle activity during functional grasp and release 在功能性抓取和释放过程中运动学和肌肉活动的预期信号

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2015-06-09 DOI: 10.1109/BSN.2015.7299360

N. Beckers, R. Fineman, L. Stirling

引用次数: 8

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