Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928449
Asiful Arefeen, S. Fessler, Carol Johnston, H. Ghasemzadeh
Postprandial hyperglycemia (PPHG) is detrimental to health and increases risk of cardiovascular diseases, reduced eyesight, and life-threatening conditions like cancer. Detecting PPHG events before they occur can potentially help with providing early interventions. Prior research suggests that PPHG events can be predicted based on information about diet. However, such computational approaches (1) are data hungry requiring significant amounts of data for algorithm training; and (2) work as a black-box and lack interpretability, thus limiting the adoption of these technologies for use in clinical interventions. Motivated by these shortcomings, we propose, DietNudge1, a machine learning based framework that integrates multi-modal data about diet, insulin, and blood glucose to predict PPHG events before they occur. Using data from patients with diabetes, we demonstrate that our model can predict PPHG events with up to 90% classification accuracy and an average F1 score of 0.93. The proposed decision-tree-based approach also identifies modifiable factors that contribute to an impending PPHG event while providing personalized thresholds to prevent such events. Our results suggest that we can develop simple, yet effective, computational algorithms that can be used as preventative mechanisms for diabetes and obesity management.
{"title":"Forewarning Postprandial Hyperglycemia with Interpretations using Machine Learning","authors":"Asiful Arefeen, S. Fessler, Carol Johnston, H. Ghasemzadeh","doi":"10.1109/BSN56160.2022.9928449","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928449","url":null,"abstract":"Postprandial hyperglycemia (PPHG) is detrimental to health and increases risk of cardiovascular diseases, reduced eyesight, and life-threatening conditions like cancer. Detecting PPHG events before they occur can potentially help with providing early interventions. Prior research suggests that PPHG events can be predicted based on information about diet. However, such computational approaches (1) are data hungry requiring significant amounts of data for algorithm training; and (2) work as a black-box and lack interpretability, thus limiting the adoption of these technologies for use in clinical interventions. Motivated by these shortcomings, we propose, DietNudge1, a machine learning based framework that integrates multi-modal data about diet, insulin, and blood glucose to predict PPHG events before they occur. Using data from patients with diabetes, we demonstrate that our model can predict PPHG events with up to 90% classification accuracy and an average F1 score of 0.93. The proposed decision-tree-based approach also identifies modifiable factors that contribute to an impending PPHG event while providing personalized thresholds to prevent such events. Our results suggest that we can develop simple, yet effective, computational algorithms that can be used as preventative mechanisms for diabetes and obesity management.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129410353","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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928447
Addythia Saphala, Rui Zhang, Trinh Nam Thái, O. Amft
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.
{"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":"44 1","pages":"0"},"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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928503
Jiyang Li, K. Vatanparvar, Li Zhu, Jilong Kuang, A. Gao
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.
{"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":"1146 1","pages":"0"},"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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928514
R. Narayanan, F. R. Rostami, A. Khaleghi, I. Balasingham
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.
{"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":"40 1","pages":"0"},"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}
Pub Date : 2022-09-27DOI: 10.1109/bsn56160.2022.9928510
{"title":"BSN 2022 Cover Page","authors":"","doi":"10.1109/bsn56160.2022.9928510","DOIUrl":"https://doi.org/10.1109/bsn56160.2022.9928510","url":null,"abstract":"","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129913150","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}
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.
{"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":"18 1","pages":"0"},"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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928464
Noor Mohammed, R. W. Jackson, Jeremy Gummeson, S. Lee
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.
在过去的几年中，电容式体内能量传输(C-IBPT)技术作为支持无电池身体传感器网络的潜在解决方案，在该领域受到了极大的关注，该技术通过电容链路将人体作为无线能量传输介质。然而，尽管C-IBPT系统的电路建模对于支持无电池身体传感器的可靠运行非常重要，但在该领域的研究还远远不够。本文提出了估算C-IBPT系统路径损耗和电极间电容的有限元模型和等效线性电路模型。作为演示示例，该模型近似于典型的人类前臂(从手腕到肘部)，并允许在准静电域调查皮肤耦合功率发射器和接收器之间的传输损耗。通过在实验室环境中使用可穿戴式40 MHz射频(RF)发射机和隔离电源接收器系统，对5名健康受试者进行实验测量，进一步验证了所提出模型计算的传输损耗。初步实验数据表明，尺寸为30 mm ×40 mm的平行板电极配置在10 cm体通道长度内的传输损耗约为40 dB。仿真结果表明，在10 cm的体通道上，传输损耗为35 dB，耦合电容为13.5 fF。
{"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":"40 1","pages":"0"},"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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928474
Fabio Egle, F. Kluge, D. Schoene, L. Becker, A. Koelewijn
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.
{"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":"1 1","pages":"0"},"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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928463
Dalia Osman, Wanlin Li, Xinli Du, Timothy Minton, Y. Noh
This paper demonstrates a working prototype for shape sensing using miniature optoelectronic sensors integrated into a chain of rotational links. Wearable sensors for rehabilitation, prosthetics and robotics must be lightweight, miniature, and compact to allow comfortable range of motion without obstruction, and therefore, the integrated network of sensors and hardware must be adapted to this. The sensing principle is based on light intensity modulation using a curvature varying reflector. The modular sensing configuration design offers a low-cost, miniaturized approach to shape sensing, compatible in clinical applications. A prototype is constructed, and calibration is carried out. Shape sensing estimation is evaluated to assess accuracy. A four-link rotational chain prototype shows average estimation errors of 2.4° for shape sensing compared to an inertial measurement unit.
{"title":"Prototype of An Optoelectronic Joint Sensor Using Curvature Based Reflector for Body Shape Sensing","authors":"Dalia Osman, Wanlin Li, Xinli Du, Timothy Minton, Y. Noh","doi":"10.1109/BSN56160.2022.9928463","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928463","url":null,"abstract":"This paper demonstrates a working prototype for shape sensing using miniature optoelectronic sensors integrated into a chain of rotational links. Wearable sensors for rehabilitation, prosthetics and robotics must be lightweight, miniature, and compact to allow comfortable range of motion without obstruction, and therefore, the integrated network of sensors and hardware must be adapted to this. The sensing principle is based on light intensity modulation using a curvature varying reflector. The modular sensing configuration design offers a low-cost, miniaturized approach to shape sensing, compatible in clinical applications. A prototype is constructed, and calibration is carried out. Shape sensing estimation is evaluated to assess accuracy. A four-link rotational chain prototype shows average estimation errors of 2.4° for shape sensing compared to an inertial measurement unit.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133854273","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}
Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928520
Zihan Wang, Tousif Ahmed, Md. Mahbubur Rahman, M. Y. Ahmed, Ebrahim Nemati, Jilong Kuang, A. Gao
Tracking breathing phases (inhale and exhale) outside the hospitals can offer significant health and wellness benefits. For example, the breathing phases can provide fine-grained breathing information for breathing exercises. While previous works use smartphones and smartwatches for tracking breathing phases, in this work, we use earbuds for breathing phase detection, which can be a better form factor for breathing exercises as it requires less user attention from the user. We propose a convolutional neural network-based algorithm for detecting breathing phases using the audio captured through the earbuds during guided breathing sessions. We conducted a user study with 30 participants in both lab and home environments to develop and evaluate our algorithm. Our algorithm can detect the breathing phases with 85% accuracy by taking only a 500ms audio signal. Our work demonstrates the potential of using earbuds for tracking the breathing phases in real-time.
{"title":"Real-Time Breathing Phase Detection Using Earbuds Microphone","authors":"Zihan Wang, Tousif Ahmed, Md. Mahbubur Rahman, M. Y. Ahmed, Ebrahim Nemati, Jilong Kuang, A. Gao","doi":"10.1109/BSN56160.2022.9928520","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928520","url":null,"abstract":"Tracking breathing phases (inhale and exhale) outside the hospitals can offer significant health and wellness benefits. For example, the breathing phases can provide fine-grained breathing information for breathing exercises. While previous works use smartphones and smartwatches for tracking breathing phases, in this work, we use earbuds for breathing phase detection, which can be a better form factor for breathing exercises as it requires less user attention from the user. We propose a convolutional neural network-based algorithm for detecting breathing phases using the audio captured through the earbuds during guided breathing sessions. We conducted a user study with 30 participants in both lab and home environments to develop and evaluate our algorithm. Our algorithm can detect the breathing phases with 85% accuracy by taking only a 500ms audio signal. Our work demonstrates the potential of using earbuds for tracking the breathing phases in real-time.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692249","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：