2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献_第3页

Campus safety and the internet of wearable things: assessing student safety conditions on campus while riding a smart scooter 校园安全和可穿戴物联网:在骑智能滑板车时评估校园学生的安全状况

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507033

Devansh Gupta, Wenyao Xu, Xiong Yu, Ming-chun Huang

{"title":"Campus safety and the internet of wearable things: assessing student safety conditions on campus while riding a smart scooter","authors":"Devansh Gupta, Wenyao Xu, Xiong Yu, Ming-chun Huang","doi":"10.1109/BSN51625.2021.9507033","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507033","url":null,"abstract":"The campus environments have traditionally revolved around the use of sustainable and practical mobility vehicles such as bicycles, but similar to pedestrians and bicyclists, the students riding smart-scooter are also vulnerable road users and to severe injuries during road accidents. In this paper, we created a “smart android system”. STEADi, for monitoring the Smart scooter riders. The system uses a Wearable Gait Lab for, a wearable underfoot force-sensing intelligent unit, as one of the main components. The purpose of this system is to help students who are new to using smart scooters on campus to avoid injuries and accidents by alerting the rider about unforeseen conditions. The system provides adequate data for path tracking, Potholes Detection system, and human balancing ability for the Smart Scooter riders. After careful selection of training data, we have been able to integrate a pothole detector system that identifies worse road segments as having potholes. The proposed system is evaluated based on four balance tests on different terrain and with different diverse riding experiences related to the Smart Scooters. The system testing showed that it can successfully detect several real potholes in and around the Cleveland area and is successfully able to alert the riders, including the lesser experienced ones while riding on different terrains for the potential road-related threats.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128187072","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: 0

A UWB Radar-based Approach of Detecting Vital Signals 一种基于超宽带雷达的生命信号检测方法

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507032

Qimeng Li, Jikui Liu, Raffaele Gravina, Ye Li, G. Fortino

{"title":"A UWB Radar-based Approach of Detecting Vital Signals","authors":"Qimeng Li, Jikui Liu, Raffaele Gravina, Ye Li, G. Fortino","doi":"10.1109/BSN51625.2021.9507032","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507032","url":null,"abstract":"The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116187232","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

Semi-Supervised Contrastive Learning for Generalizable Motor Imagery EEG Classification 半监督对比学习在广义运动意象脑电分类中的应用

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507038

Jinpei Han, Xiao Gu, Benny P. L. Lo

引用次数: 9

Design and Evaluation of a Wrist Wearable Joint Acoustic Emission Monitoring System 腕部可穿戴关节声发射监测系统的设计与评价

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507015

Daniel M Hochman, G. Ozmen, L. Ponder, S. Prahalad, O. Inan

{"title":"Design and Evaluation of a Wrist Wearable Joint Acoustic Emission Monitoring System","authors":"Daniel M Hochman, G. Ozmen, L. Ponder, S. Prahalad, O. Inan","doi":"10.1109/BSN51625.2021.9507015","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507015","url":null,"abstract":"Joint acoustic emission (JAE) sensing is emerging as a potential modality for quantitative at-home joint health assessment. We designed and validated a low-profile, easy-to-use wearable system for JAE sensing at the wrist. An embedded microcontroller on a wrist-worn printed circuit board is used to record multi-microphone (mic) joint acoustics sampled at 46.875 kHz using an on-board analog-to-digital converter. A flex sensor and a force sensitive resistor (FSR) are sampled at 300 Hz to capture kinematics and mic backing force. Custom sensor casing solutions and real-time user feedback systems enhance audio sensing capabilities at locations both proximal and distal to the wrist. An experiment extracting wrist JAEs from healthy adults (n=6) allowed for comparison to a previously established benchtop JAE sensing system. The acoustic data were bandpass filtered (150 Hz-5.5 kHz). Qualitative observations reveal the wearable system mics successfully capture JAEs. Signal-to-noise ratio (SNR), intraclass correlation coefficient (model 3, k), and coefficients of variability were calculated to evaluate acoustic signal strength and repeatability in both systems' recordings. SNR reveals that JAE acoustic signal strength is higher when recorded using the wearable system than with the benchtop system (p<0.01). Reliability measures show that the wearable system records JAEs with similar levels of reliability to the benchtop system. Initial recordings in clinical wrist JAE research studies demonstrate high quality JAE measurements from children with Juvenile Idiopathic Arthritis (JIA) and healthy controls (HCs) (4 JIA, 3 HC). Eventually, this system may be developed into a tool for at-home wrist joint health monitoring.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127865284","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: 0

Multi-Objective Optimisation for SSVEP Detection SSVEP检测的多目标优化

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507041

Yue Zhang, Zhiqiang Zhang, Shengquan Xie

引用次数: 1

Adherence to a garment-adhered respiratory force monitor in patients with advanced COPD 晚期慢性阻塞性肺病患者穿戴式呼吸力监测仪的依从性

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507013

N. Moraveji, M. Holt, J. Hollenbach, R. Murray, Hadley White, M. Crocker

{"title":"Adherence to a garment-adhered respiratory force monitor in patients with advanced COPD","authors":"N. Moraveji, M. Holt, J. Hollenbach, R. Murray, Hadley White, M. Crocker","doi":"10.1109/BSN51625.2021.9507013","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507013","url":null,"abstract":"Anticipating adverse events among chronic respiratory disease patients may benefit from remote respiratory monitoring. However, capturing granular respiratory features continuously previously required body-worn sensors devices not suitable for long-term wear. Long monitoring periods are required given to the chronic nature of chronic respiratory diseases. This study evaluates adherence to the health tag physiological sensing form factor. This is a class of devices adhered to the inside of a patient's undergarment as an affordance for adherence, but which also provides on-body contact to produce robust respiratory data. 94 individuals with moderate to severe COPD were enrolled in a 9-month remote monitoring study. The mean enrollment period was 5.3 months (SD=3.5 months). The device yielded an overall daily adherence rate of 87% (SD=20%). The presence of two groups of participants emerged, novelty and sustained, providing additional support for a 90-day novelty period for wearable devices, found in prior research. The group that sustained enrollment past the novelty period was characterized by an increased proportion of device wear time. The study indicates that long-term, remote respiratory monitoring of advanced COPD patients is feasible and can produce respiratory features beyond rate alone. Such datasets could be used to train models that potentially identify respiratory deterioration preceding adverse events.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129821152","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

Digitally-embroidered Liquid Metal Textiles for Near-field Wireless Body Sensor Networks 用于近场无线身体传感器网络的数字刺绣液态金属纺织品

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507043

Rongzhou Lin, Han-Joon Kim, Sippanat Achavananthadith, J. S. Ho

{"title":"Digitally-embroidered Liquid Metal Textiles for Near-field Wireless Body Sensor Networks","authors":"Rongzhou Lin, Han-Joon Kim, Sippanat Achavananthadith, J. S. Ho","doi":"10.1109/BSN51625.2021.9507043","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507043","url":null,"abstract":"Clothing with electromagnetic functionalities can be used to interconnect a wireless network of battery-free sensors around the human body. Such smart clothing require textiles that are highly conductive, flexible, durable, and compatible with established manufacturing processes. Here, we demonstrate textiles with near-field functionalities fabricated by digital embroidery of liquid metal fibers. The liquid metal fibers, consisting of Galinstan in perfluoroalkoxy alkane tubing, exhibit mechanical flexibility comparable to the underlying materials and durability against mechanical bending (<1% electrical resistance variation on 10000 cycles), and high electrical conductance at radio-frequencies (~9.6 Ωm at 13.56 MHz). The digital embroidery process enables transfer of near-field inductive patterns optimized using full-wave electromagnetic simulations onto conventional textiles without blocking water vapour transport. We design and fabricate liquid metal fibers onto fabric skin patches for wireless power transfer at 13.56 MHz. Experiments show that the patches can conformally attach onto the surface of the body and provide robust wireless power transfer to devices in both wearable and implantable configurations during physical activity (<1.5% relative standard deviation during standing and running at 9.2 km/h), These results suggest the potential of liquid-metal based wireless systems to establish robust and unobtrusive wireless networks of battery-free wearable and implantable devices using near-field technologies.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132779585","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

Multi-band Implantable Microstrip Antenna on Large Ground Plane and TiO2 Substrate 大地平面和TiO2衬底多波段可植入微带天线

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507029

Lida Kouhalvandi, L. Matekovits, I. Peter

引用次数: 1

CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform CoughBuddy:使用耳机平台的多模态咳嗽事件检测

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507017

Ebrahim Nemati, Shibo Zhang, Tousif Ahmed, Md. Mahbubur Rahman, Jilong Kuang, A. Gao

{"title":"CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform","authors":"Ebrahim Nemati, Shibo Zhang, Tousif Ahmed, Md. Mahbubur Rahman, Jilong Kuang, A. Gao","doi":"10.1109/BSN51625.2021.9507017","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507017","url":null,"abstract":"There has been an extensive amount of study on cough detection using acoustic features captured from smartphones and smartwatches in the past decade. However, the specificity of the algorithms has always been a concern when exposed to the unseen field data containing cough-like sounds. In this paper, we propose a novel sensor fusion algorithm that employs a hybrid of classification and template matching algorithms to tackle the problem of unseen classes. The algorithm utilizes in-ear audio signal as well as head motion captured by the inertial measurement unit (IMU). A clinical study including 45 subjects from healthy and chronic cough cohorts was conducted that contained various tasks including cough and cough-like body sounds in various conditions such as quiet/noisy and stationary/non-stationary. Our hybrid model was evaluated for sensitivity and specificity in these conditions using leave one-subject out validation (LOSOV) and achieved an average sensitivity of 83% for stationary tasks and an specificity of 91.7% for cough-like sounds reducing the false positive rate by 55%. These results indicate the feasibility and superiority of fusion in earbuds platforms for detection of cough events.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125233533","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: 8

A Soft Inflatable Elbow-Assistive Robot for Children with Cerebral Palsy 一种用于脑瘫儿童的软充气肘辅助机器人

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507023

Yulin Wang, Benny P. L. Lo

引用次数: 1