2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献_第2页

Identifying causal relationships in time-series data from a pair of wearable sensors 从一对可穿戴传感器中识别时间序列数据中的因果关系

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507030

D. Arvind, S. Maiya, P. A. Sedeño

{"title":"Identifying causal relationships in time-series data from a pair of wearable sensors","authors":"D. Arvind, S. Maiya, P. A. Sedeño","doi":"10.1109/BSN51625.2021.9507030","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507030","url":null,"abstract":"According to the Lancet report on global burden of disease published in October 2020, air pollution is amongst the five highest risk factors for global health, reducing life expectancy on average by 20 months. This paper describes a data-driven method for establishing causal relationships between two time-series data streams derived from wearable sensors: personal exposure to airborne particulate matter (PM) of aerodynamic sizes less than 2.5 $mu mathrm{m}(text{PM}_{2.5})$ gathered from the Airspeck monitor, and continuous respiratory rate (breaths/minute) measured by the wireless Respeck monitor worn as a plaster on the chest. Results are presented for a cohort of asthmatic adolescents using the PCMCI method on the short-term causal relationship between $text{PM}_{2.5}$ exposure and respiratory rate for time lags in the first 60 minutes at minute-level intervals, and for time lags between 2 to 8 hours at 10-minute time intervals. For the first time a personalised exposure-response relationship between $text{PM}_{2.5}$ exposure and respiratory rate has been demonstrated for short-term effects in asthmatic adolescents during their every day lives.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"52 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":"115128110","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

Better Battery Life: Towards Energy-Efficient Smartwatch-Based Atrial Fibrillation Detection in Ambulatory Free-living Environments 更长的电池寿命:在动态自由生活环境中实现基于节能智能手表的房颤检测

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507025

Hanbin Zhang, Li Zhu, Viswam Nathan, Jilong Kuang, Jacob Kim, A. Gao

引用次数: 0

Noninvasive Continuous Blood Pressure Measurement with Wearable Millimeter Wave Device 使用可穿戴毫米波设备进行无创连续血压测量

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507020

Catherine Liao, Oliver Shay, Elizabeth Gomes, Nikhil Bikhchandani

引用次数: 4

Assessing Internal and External Attention in AR using Brain Computer Interfaces: A Pilot Study 使用脑机接口评估AR的内部和外部注意:一项试点研究

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507034

Nataliya Kosmyna, Qiuxuan Wu, Chi-Yun Hu, Yujie Wang, C. Scheirer, P. Maes

{"title":"Assessing Internal and External Attention in AR using Brain Computer Interfaces: A Pilot Study","authors":"Nataliya Kosmyna, Qiuxuan Wu, Chi-Yun Hu, Yujie Wang, C. Scheirer, P. Maes","doi":"10.1109/BSN51625.2021.9507034","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507034","url":null,"abstract":"Most research works featuring AR and Brain-Computer Interface (BCI) systems are not taking advantage of the opportunities to integrate the two planes of data. Additionally, AR devices that use a Head-Mounted Display (HMD) face one major problem: constant closeness to a screen makes it hard to avoid distractions within the virtual environment. In this project, we reduced this distraction by including information about the current attentional state. We first introduce a clip-on solution for AR-BCI integration. A simple game was designed for the Microsoft HoloLens 2, which changed in real time according to the user's state of attention measured via electroencephalography (EEG). The system only responded if the attentional orientation was classified as “external.” Fourteen users tested the attention-aware system; we show that the augmentation of the interface improved the usability of the system. We conclude that more systems would benefit from clearly visualizing the user's ongoing attentional state as well as further efficient integration of AR and BCI headsets.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"22 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":"132243852","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

Artificial ear - a wearable device for the hearing impaired 人造耳——一种为听力受损人士设计的可穿戴设备

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507021

Antonia Pavlidou, Benny P. L. Lo

引用次数: 2

Wavelet-based analysis of gait for automated frailty assessment with a wrist-worn device 基于小波分析的腕戴式自动衰弱评估步态

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507037

Domenico Minici, Guglielmo Cola, A. Giordano, Silvana Antoci, E. Girardi, M. Bari, M. Avvenuti

{"title":"Wavelet-based analysis of gait for automated frailty assessment with a wrist-worn device","authors":"Domenico Minici, Guglielmo Cola, A. Giordano, Silvana Antoci, E. Girardi, M. Bari, M. Avvenuti","doi":"10.1109/BSN51625.2021.9507037","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507037","url":null,"abstract":"Recent advancements in the field of smart wearable sensors provide the opportunity of continuous analysis of user's movements, which enables the assessment of clinical conditions like frailty. This study explores the use of Continuous Wavelet Transform in combination with sensor-derived gait parameters for frailty status assessment. A total of 34 volunteers aged 70+ were initially screened by geriatricians for the presence of frailty according to Fried's criteria. After screening, participants were asked to perform a 60 m walk test at preferred pace, while wearing an accelerometer on the wrist. A gait detection technique was applied to the sensor-derived signal, in order to identify segments made of four gait cycles. Continuous Wavelet Transform was applied to obtain time-frequency domain representations, which were subsequently used in a band-based feature extraction phase. Here, the most significant band-based features for frailty status assessment were identified by means of ANOVA and statistical t-test. Finally, a Random Forest for each frequency band was trained and tested for classifying subjects as robust or nonrobust (i.e., pre-frail or frail). Results from both the statistical analysis and machine learning show that features extracted from $[1.5, 2.5]Hz$ frequency band can provide valuable information for recognizing frailty in older adults. This information may help achieve continuous assessment of frailty in older adults with a wrist-worn device.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"94 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":"114223811","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

Heart Rate Detection using a Contactless Bed Sensor: A Comparative Study of Wavelet Methods 基于非接触式床传感器的心率检测:小波方法的比较研究

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507027

Ibrahim Sadek, B. Abdulrazak, Terry Tan Soon Heng, E. Seet

{"title":"Heart Rate Detection using a Contactless Bed Sensor: A Comparative Study of Wavelet Methods","authors":"Ibrahim Sadek, B. Abdulrazak, Terry Tan Soon Heng, E. Seet","doi":"10.1109/BSN51625.2021.9507027","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507027","url":null,"abstract":"Contactless monitoring of vital signs, e.g., heart rate (HR) attracts the researcher's attention due to its convenience and affordability. Among others, under-mattress ballistocardiogram (BCG) sensors have proved effective for contactless remote monitoring of HR. Nevertheless, HR detection from BCG sensors is a challenging task because the signal morphology can vary between and within-subjects. In this paper, we studied the potential of two wavelet-based methods, i.e., the multiresolution analysis of the maximal overlap discrete wavelet transform (MODWT-MRA) and continuous wavelet transform (CWT) for HR detection via a microbend fiber optic sensor (MFOS). BCG signals were gathered from ten sleep apnea patients during overnight polysomnography (PSG) study. The MFOS was placed under the bed mattress and the PSG electrocardiogram (ECG) signals were used as a reference to evaluate the proposed HR detection algorithms. Overall, CWT with derivative of Gaussian provided (Gaus2) slightly better results compared with the MODWT-MRA, CWT (frequency Bspline), and CWT (Shannon). Across the ten patients, the mean absolute error, mean absolute percentage error, and root mean square error metrics were as follows: 4.71 (1.22), 7.58% (2.17), and 5.58 (1.20), and 4.71 (1.07), 7.61% (1.65), and 5.59 (1.02), for Gau2 and MODWT-MRA, respectively. That said, the total precision for MODWT-MRA, i.e., 80.22 (19.01) was higher than Gaus2, i.e., 78.83 (17.84).","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"45 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":"114626512","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

Deep 3D Body Landmarks Estimation for Smart Garments Design 智能服装设计的深度3D人体地标估计

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507035

A. Baronetto, Dominik Wassermann, O. Amft

引用次数: 3

Small-form wearable device for long-term monitoring of cardiac sounds on the body surface 用于长期监测体表心音的小型可穿戴设备

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507024

B. Rosa, S. Anastasova, Benny P. L. Lo

{"title":"Small-form wearable device for long-term monitoring of cardiac sounds on the body surface","authors":"B. Rosa, S. Anastasova, Benny P. L. Lo","doi":"10.1109/BSN51625.2021.9507024","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507024","url":null,"abstract":"Sound monitoring from sources inside the human body can have important diagnostic relevance in medicine. Cardiac sounds originated from the pumping activity of the heart structure is such an example, with valuable cardiovascular parameters being extracted from the signal, including heart rate (HR) and the systolic intervals. Novel non-invasive methods for early detection of potential life-threatening risks convoyed by unbalanced cardiovascular parameters are essential to reduce the mortality rates associated with cardiac diseases nowadays. In this paper, we propose a small-form wearable device for longterm monitoring of the cardiac sounds through a miniaturized microphone in contact with the body surface at specific locations, which extend from the chest region to the upper and lower body parts. Powered by battery, the device can measure signals for a consecutive period of 28 h in continuous recording mode that is extensive up to 7 days in discontinuous mode, achieving signal amplitude resolution of 0.81 μV and optimal bandwidth between 5 to 20 Hz (infrasound range). The proposed device was able to detect cardiac sound patterns in locations as distant as the forehead, wrist, or ankle, thus paving the way to the use of acoustic signals for wearable heartbeat estimators still relying on optical or bio-potential methods, while replacing the obtrusive and expensive cardiography equipment dedicated to the estimation of the systolic intervals directly from the chest.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"59 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":"121895417","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

Adaptive Surface Electromyography Normalization for Long-Duration Recordings 适应表面肌电归一化的长时间记录

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507026

Samantha R. Fox, Reed D. Gurchiek, Anna T. Ursiny, Brett M. Meyer, Julianne M. Boughton, R. Mcginnis

引用次数: 1