2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献_第3页

Joint Angle Measurements Using Magnetic Sensing: A Feasibility Study 磁感测关节角度的可行性研究

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928508

Fereshteh Shahmiri, sshahmiri

{"title":"Joint Angle Measurements Using Magnetic Sensing: A Feasibility Study","authors":"Fereshteh Shahmiri, sshahmiri","doi":"10.1109/BSN56160.2022.9928508","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928508","url":null,"abstract":"Inertial measurement units (IMUs) are extensively used for body motion tracking applications. Despite their ubiquity, they often suffer from sensor drift over time, and environmental disturbances. Additionally, their use cases are mostly limited to applications with slowly varying accelerations and low-dynamic motions. Sensor fusion algorithms are used for scenarios where more dynamic, faster motions are encountered. However, such algorithms often come with high computational costs. In this work, we present a low-drift, computationally-efficient motion tracking system that suppresses ambient magnetic noise and is applicable to various motion dynamics. We augmented inertial sensors with localized magnets, and implemented a localization algorithm that takes in the magnetic measurements and outputs the sensor positions as the sensors move in the vicinity of the magnets. For applications with movements around a central joint, we extended our position tracking to a joint angle measurement platform. We conducted two preliminary studies to evaluate our system performance, and validated our system against a computer vision system. Our first study uses a goniometric setup to evaluate drift-reductions in angle estimates. Our method is compared against a commonly-used IMU-based method. We collected 60 minutes of data from 4 study sessions, with both static conditions and various dynamic motions. The motions had angular velocities ranging from 0 to 47 (°/sec). Results show the average root mean square error (RMSE) of 1° for static and 2.7° for dynamic motions. In the second study, an on-body setup monitors the knee flexions and extensions performed by a pilot user. We collected 30 minutes of data from 4 study sessions. Our system reports the average RMSE of 3.7° for dynamic motions with an average angular velocity of 17 (°/sec). Based on these promising results, in future work we will extend our user studies to a greater number of users to evaluate the generalizability.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564476","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

Enhancement of Remote PPG and Heart Rate Estimation with Optimal Signal Quality Index 用最优信号质量指数增强远程PPG和心率估计

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928503

Jiyang Li, K. Vatanparvar, Li Zhu, Jilong Kuang, A. Gao

{"title":"Enhancement of Remote PPG and Heart Rate Estimation with Optimal Signal Quality Index","authors":"Jiyang Li, K. Vatanparvar, Li Zhu, Jilong Kuang, A. Gao","doi":"10.1109/BSN56160.2022.9928503","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928503","url":null,"abstract":"With the popularity of non-invasive vital signs detection, remote photoplethysmography (rPPG) is drawing attention in the community. Remote PPG or rPPG signals are extracted in a contactless manner that is more prone to artifacts than PPG signals collected by wearable sensors. To develop a robust and accurate pipeline to estimate heart rate (HR) from rPPG signals, we propose a novel real-time dynamic ROI tracking algorithm that applies to slight motions and light changes. Furthermore, we develop and include a signal quality index (SQI) to improve the HR estimation accuracy. Studies have explored optimal SQIs for PPG signals, but not for remote PPG signals. In this paper, we select and test six SQIs: Perfusion, Kurtosis, Skewness, Zero-crossing, Entropy, and signal-to-noise ratio (SNR) on 124 rPPG sessions from 30 participants wearing masks. Based on the mean absolute error (MAE) of HR estimation, the optimal SQI is selected and validated by Mann–Whitney U test (MWU). Lastly, we show that the HR estimation accuracy is improved by 29% after removing outliers decided by the optimal SQI, and the best result achieves the MAE of 2.308 bpm.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127944432","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

BSN 2022 Cover Page BSN 2022封面

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/bsn56160.2022.9928510

引用次数: 0

Performance Analysis of Single Coreshell Magnetoelectric Microdevice for Electrical Stimulation 电刺激单芯壳磁电微器件性能分析

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928514

R. Narayanan, F. R. Rostami, A. Khaleghi, I. Balasingham

{"title":"Performance Analysis of Single Coreshell Magnetoelectric Microdevice for Electrical Stimulation","authors":"R. Narayanan, F. R. Rostami, A. Khaleghi, I. Balasingham","doi":"10.1109/BSN56160.2022.9928514","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928514","url":null,"abstract":"Electrical stimulation of biological cells and tissues is an established technique to stimulate cells such as neurons and cardiomyocytes to enable the treatment of some disorders like Parkinson’s disease, cardiac arrhythmias, obstructive sleep apnea epilepsy, and depression. These devices use electronic circuits, batteries, and wires to transfer the stimulation signal to the target region. On the contrary, macro-scale devices such as scalp based bioelectrodes, surgical implants etc., require invasive surgery and constant fault monitoring. The use of standalone bio-compatible wireless micro-devices that can enable remote control and monitoring, powering and stimulation of cells and tissues and, deliver the stimulation therapy without additional circuits and battery, can be a significant advantage. In this paper, we introduce the concept of using magnetoelectric (ME) material composition to generate controllable electrical stimulation patterns for the Central Nervous System (CNS) stimulation therapy. We propose the potential use of ME structures in multi-modal resonant frequencies, for active stimulation. A spherical ME coreshell microdevice is designed and the Multiphysics numerical computations are used to evaluate the strain induced voltage on the device by using a remote magnetic bias and alternating magnetic field. It is shown that using the ME device in the resultant strain mode can create a sufficient voltage gradient that can potentially be used for wireless stimulation.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124731376","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

Non-contact temporalis muscle monitoring to detect eating in free-living using smart eyeglasses 非接触式颞肌监测，在自由生活中使用智能眼镜检测饮食

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928447

Addythia Saphala, Rui Zhang, Trinh Nam Thái, O. Amft

{"title":"Non-contact temporalis muscle monitoring to detect eating in free-living using smart eyeglasses","authors":"Addythia Saphala, Rui Zhang, Trinh Nam Thái, O. Amft","doi":"10.1109/BSN56160.2022.9928447","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928447","url":null,"abstract":"We investigate non-contact sensing of temporalis muscle contraction in smart eyeglasses frames to detect eating activity. Our approach is based on infra-red proximity sensors that were integrated into sleek eyeglasses frame temples. The proximity sensors capture distance variations between frame temple and skin at the frontal, hair-free section of the temporal head region. To analyse distance variations during chewing and other activities, we initially perform an in-lab study, where proximity signals and Electromyography (EMG) readings were simultaneously recorded while eating foods with varying texture and hardness. Subsequently, we performed a free-living study with 15 participants wearing integrated, fully functional 3Dprinted eyeglasses frames, including proximity sensors, processing, storage, and battery, for an average recording duration of 8.3hours per participant. We propose a new chewing sequence and eating event detection method to process proximity signals. Free-living retrieval performance ranged between the precision of 0.83 and 0.68, and recall of 0.93 and 0.90, for personalised and general detection models, respectively. We conclude that noncontact proximity-based estimation of chewing sequences and eating integrated into eyeglasses frames is a highly promising tool for automated dietary monitoring. While personalised models can improve performance, already general models can be practically useful to minimise manual food journalling.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127649383","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

Wireless Intra-Body Power Transfer via Capacitively Coupled Link 通过电容耦合链路的无线体内能量传输

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928464

Noor Mohammed, R. W. Jackson, Jeremy Gummeson, S. Lee

{"title":"Wireless Intra-Body Power Transfer via Capacitively Coupled Link","authors":"Noor Mohammed, R. W. Jackson, Jeremy Gummeson, S. Lee","doi":"10.1109/BSN56160.2022.9928464","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928464","url":null,"abstract":"Over the past couple of years, the Capacitive Intra-Body Power Transfer (C-IBPT) technology, which uses the human body as a wireless power transfer medium via capacitive links, has received tremendous attention in the field as a potential solution to support a network of battery-free body sensors. However, circuit modeling of C-IBPT systems, despite its importance in supporting the reliable operation of battery-free body sensors, has been significantly understudied in the field. This paper proposes a finite element model (FEM) and equivalent linear circuit models to estimate path loss and inter-electrode capacitance of a C-IBPT system. As a demonstrative example, the model approximates a typical human forearm (from wrist to elbow) and allows for investigation of the transmission loss between a skin-coupled power transmitter and a receiver in the electro-quasistatic domain. The computed transmission loss from the proposed model is further validated against experimental measurements obtained from five healthy human subjects using a wearable 40 MHz radio frequency (RF) transmitter and an isolated power receiver system in a laboratory environment. The preliminary experimental data show an approximate 40 dB transmission loss within 10 cm body channel length for the parallel plate electrode configuration with dimensions of 30 mm ×40 mm. The simulation finding shows a lower transmission loss of 35 dB and 13.5 fF coupling capacitance across a 10 cm body channel.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127089433","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

Development of an Inertial Sensor-Based Exergame for Combined Cognitive and Physical Training 一种基于惯性传感器的认知与体能结合训练游戏的研制

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928474

Fabio Egle, F. Kluge, D. Schoene, L. Becker, A. Koelewijn

{"title":"Development of an Inertial Sensor-Based Exergame for Combined Cognitive and Physical Training","authors":"Fabio Egle, F. Kluge, D. Schoene, L. Becker, A. Koelewijn","doi":"10.1109/BSN56160.2022.9928474","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928474","url":null,"abstract":"Mild cognitive impairment (MCI) is a condition where older people have experienced cognitive decline, which can then transition into dementia. Hence, it is important to prevent further health decline. Therefore, we have developed an exergame that aims to prevent cognitive and physical decline in older people with MCI. The exergame uses inertial measurement units, worn on the user’s wrists and feet, to record their movements. The user steps in place to move through the game environment and interacts with different obstacles through movement. We performed an experiment to evaluate the technical game performance, exercise intensity, and game usability and enjoyment. We found that our movement detection algorithms were able to detect 90% of all movements after one attempt, on average between 1.7-3.5 seconds. While our young participants’ heart rates did not reach moderate exercise intensity while playing the game, we expect that the activity is suitable for the target population. Furthermore, young participants’ user feedback from questionnaires regarding usability and enjoyment was positive.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129094668","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

CCA-based Spatio-temporal Filtering for Enhancing SSVEP Detection 基于cca的时空滤波增强SSVEP检测

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928502

Yue Zhang, Shengquan Xie, Zhenhong Li, Yihui Zhao, Kun Qian, Zhi-Li Zhang

{"title":"CCA-based Spatio-temporal Filtering for Enhancing SSVEP Detection","authors":"Yue Zhang, Shengquan Xie, Zhenhong Li, Yihui Zhao, Kun Qian, Zhi-Li Zhang","doi":"10.1109/BSN56160.2022.9928502","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928502","url":null,"abstract":"Brain-computer interface (BCI) can provide a direct communication path between the human brain and an external device. The steady-state visual evoked potential (SSVEP)-based BCI has been widely explored in the past decades due to its high signal-to-noise ratio and fast communication rate. Several spatial filtering methods have been developed for frequency detection. However the temporal knowledge contained in the SSVEP signal is not effectively utilized. In this study, we propose a canonical correlation analysis (CCA)-based spatio-temporal filtering method to improve target classification. The training signal and two types of template signals (i.e. individual template and artificial sine-cosine reference) are first augmented via temporal information. Three sets of augmented data are then concatenated by trials. The CCA is performed twice, between the newly obtained training data and each template. The trained four spatial filters can be applied in the following test process. A public benchmark dataset was used to evaluate the performance of the proposed method and the other three comparing methods, such as CCA, MsetCCA, and TRCA. The experimental results indicate that the proposed method yields significantly higher performance. This paper also explored the effects of the number of electrodes and training blocks on classification accuracy. The results further demonstrated the effectiveness of the proposed method in SSVEP detection.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127972203","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

Prototype of An Optoelectronic Joint Sensor Using Curvature Based Reflector for Body Shape Sensing 基于曲率反射器的人体形状传感光电关节传感器原型

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928463

Dalia Osman, Wanlin Li, Xinli Du, Timothy Minton, Y. Noh

引用次数: 0

Real-Time Breathing Phase Detection Using Earbuds Microphone 实时呼吸相位检测使用耳塞麦克风

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI: 10.1109/BSN56160.2022.9928520

Zihan Wang, Tousif Ahmed, Md. Mahbubur Rahman, M. Y. Ahmed, Ebrahim Nemati, Jilong Kuang, A. Gao

引用次数: 2