2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献_第2页

Smart wireless headphone for cardiovascular and stress monitoring 用于心血管和压力监测的智能无线耳机

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936011

B. Rosa, Guang-Zhong Yang

{"title":"Smart wireless headphone for cardiovascular and stress monitoring","authors":"B. Rosa, Guang-Zhong Yang","doi":"10.1109/BSN.2017.7936011","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936011","url":null,"abstract":"Wearable technology has become ubiquitous in recent years due to the miniaturization of circuit electronics and advances in smart materials that can conform to the requirements posed by the human body, behaviour and experience. Sensors of this type are found attached almost to every body segment, capable of delivering signals even in harsh activity scenarios. The reliability and relevance of the physiological data retrieved by wearables have yet to surpass the conventional technologies in the healthcare system today. In this paper we present a small device incorporated inside an headphone set that continuously monitors the ECG, impedance and acceleration of the head. As opposed to most biometric sensors, ECG measurement relies on non-optical methods by capturing the electrical potential around the ear in both sides of the head, whereas impedance monitoring involves AC stimulation instead of DC, the latter commonly involved in skin galvanic response estimation. Signal processing of impedance parameters is performed in situ using a fast variant of the Discrete Fourier Transform in order to save computational resources and power expenditure from a microcontroller equipped with Bluetooth Low Energy. Applications that can benefit from this device include cardiovascular and stress level assessment of individuals for whom an hearable is a requirement for work or leisure.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127470954","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

A connected chair as part of a smart home environment 连接椅子作为智能家居环境的一部分

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936004

Marc Hesse, A. Krause, Ludwig Vogel, B. Chamadiya, Michael Schilling, T. Schack, T. Jungeblut

引用次数: 6

Improved heart rate variability measurement based on modulation spectral processing of noisy electrocardiogram signals 基于噪声心电图信号调制频谱处理的改进心率变异性测量

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936005

Diana P. Tobón, Srinivasan Jayaraman, T. Falk

{"title":"Improved heart rate variability measurement based on modulation spectral processing of noisy electrocardiogram signals","authors":"Diana P. Tobón, Srinivasan Jayaraman, T. Falk","doi":"10.1109/BSN.2017.7936005","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936005","url":null,"abstract":"Wearable device usage is burgeoning, with representative applications ranging from patient/athelete monitoring to stress/fatigue identification to the so-called quantified self movement. Typically, cardiac information is monitored via electrocardiograms (ECG) and information such as heart rate (HR) and heart rate variability (HRV) are used as key health-related metrics. With many wearable devices, however, lower quality sensors are used, thus resulting in devices that are highly sensible to artifacts due to e.g., user's movement. The introduced artifacts hamper HR/HRV analyses, thus ECG enhancement has been the focus of recent research. Existing enhancement algorithms, however, do not perform well in very noisy conditions, as well as add additional computational processing to already battery-hungry wearable applications. Here, we propose to overcome these limitations by describing a new ECG signal representation called the modulation spectrum. By quantifying the rate-of-change of ECG spectral components, signal and artifactual components become separable, thus allowing for accurate HR and HRV measurement from the noisy signal, even in very extreme conditions typically seen in athletic performance training. The proposed MD-HRV (modulation-domain HRV) metric is tested with noise-corrupted synthetic ECG signals and is compared to ‘true’ HRV values obtained from the clean signals. Experimental results show the proposed metric significantly outperforming conventional HRV indices computed on both the noisy, as well as enhanced ECG signals processed by a state-of-the-art wavelet-based algorithm. The obtained findings suggest that the proposed metric is well suited for wearable applications, particularly those involved with intense movement (e.g., in elite athletic training).","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116059299","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: 2

Smart healthcare: Cloud-enabled body sensor networks 智能医疗保健:支持云的身体传感器网络

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936017

Ruoxi Yu, T. Mak, Ruikai Zhang, S. Wong, Yali Zheng, J. Lau, Carmen C. Y. Poon

引用次数: 14

Human activity recognition for emergency first responders via body-worn inertial sensors 人体活动识别的紧急第一响应者通过身体穿戴惯性传感器

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7935994

Sebastian Scheurer, Salvatore Tedesco, Kenneth N. Brown, B. O’flynn

{"title":"Human activity recognition for emergency first responders via body-worn inertial sensors","authors":"Sebastian Scheurer, Salvatore Tedesco, Kenneth N. Brown, B. O’flynn","doi":"10.1109/BSN.2017.7935994","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935994","url":null,"abstract":"Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786929","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: 32

Secure key generation using gait features for Body Sensor Networks 安全密钥生成使用步态特征的身体传感器网络

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936042

Yingnan Sun, Charence Wong, Guang-Zhong Yang, Benny P. L. Lo

{"title":"Secure key generation using gait features for Body Sensor Networks","authors":"Yingnan Sun, Charence Wong, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2017.7936042","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936042","url":null,"abstract":"With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"41 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868798","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: 33

A wearable sensor system for neonatal seizure monitoring 用于新生儿癫痫监测的可穿戴传感器系统

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7935999

Hongyu Chen, Xiao Gu, Zhenning Mei, Ke Xu, Kai Yan, Chunmei Lu, Laishuan Wang, Feng Shu, Qixin Xu, S. Oetomo, Wei Chen

引用次数: 14

A personalized air quality sensing system - a preliminary study on assessing the air quality of London underground stations 个人化空气质素感应系统——伦敦地铁站空气质素评估初步研究

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936020

Ruizhe Zhang, D. Ravì, Guang-Zhong Yang, Benny P. L. Lo

{"title":"A personalized air quality sensing system - a preliminary study on assessing the air quality of London underground stations","authors":"Ruizhe Zhang, D. Ravì, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2017.7936020","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936020","url":null,"abstract":"Recent studies have shown that air pollution has a negative impact on people's health, especially for patients with respiratory and cardiac diseases (e.g. COPD, asthma, ischemic heart disease). Although there are already many air quality monitoring stations in major cities, such as London, these stations are sparsely located, and the periodic collection of information is insufficient to provide the granularity needed to assess the environmental risk for an individual (e.g. to avoid exacerbation). Wearable devices, on the other hand, are more suitable in this context, providing a better estimation of the air quality in the proximity of the person. Therefore, relevant warnings and information on health risks can be provided in real-time. As a proof of concept, we have developed a wearable sensor for continuous monitoring of air quality around the user, and a preliminary study was conducted to validate the sensor and assess the air quality in London underground stations. Based on the PM2.5 (particulate matter with a diameter of 2.5 µm), temperature and location information, a model is generated for predicting the air quality of each station at different times. Our preliminary results have shown that there are significant differences in air quality among stations and metro lines. It also demonstrates that wearable sensors can provide necessary information for users to make travel arrangements that minimize their exposure to polluted air.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114236452","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

Evaluation of an RF wearable device for non-invasive real-time hydration monitoring 一种用于无创实时水合监测的射频可穿戴设备的评估

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936015

Junchao Wang, Z. Zilic, Yutian Shu

引用次数: 7

Portable impedance measurement device for sweat based glucose detection 用于汗液葡萄糖检测的便携式阻抗测量装置

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2017-05-09 DOI: 10.1109/BSN.2017.7936008

A. Thulasi, D. Bhatia, P. Balsara, S. Prasad

引用次数: 6