Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928485
K. Vatanparvar, Migyeong Gwak, Li Zhu, Jilong Kuang, A. Gao
Contactless measurement of vitals has been seen as a promising alternative to contact sensors for monitoring of health condition. In this paper, we focus on respiration rate (RR) as one of the fundamental biomarkers of a person’s cardio and pulmonary activities. Remote RR estimation has gained attraction due to its various potential applications; use of RGB cameras to extract remote photoplethysmography (PPG) signal from subjects’ face has been debated as one of the enabling technologies for remote RR estimation. The technology is challenged with respect to wide range of RR and non-voluntary motion in uncontrolled settings. We propose a novel methodology to enhance the remote PPG signal and remove artifacts from the respiration signal. The method achieves 3.9bpm MAE of 90% percentile (1.3bpm decrease) for estimating RR in range of 5-25bpm. We validate the performance using smartphone video recordings of 30 subjects with uniform distribution of skin tone.
{"title":"Respiration Rate Estimation from Remote PPG via Camera in Presence of Non-Voluntary Artifacts","authors":"K. Vatanparvar, Migyeong Gwak, Li Zhu, Jilong Kuang, A. Gao","doi":"10.1109/BSN56160.2022.9928485","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928485","url":null,"abstract":"Contactless measurement of vitals has been seen as a promising alternative to contact sensors for monitoring of health condition. In this paper, we focus on respiration rate (RR) as one of the fundamental biomarkers of a person’s cardio and pulmonary activities. Remote RR estimation has gained attraction due to its various potential applications; use of RGB cameras to extract remote photoplethysmography (PPG) signal from subjects’ face has been debated as one of the enabling technologies for remote RR estimation. The technology is challenged with respect to wide range of RR and non-voluntary motion in uncontrolled settings. We propose a novel methodology to enhance the remote PPG signal and remove artifacts from the respiration signal. The method achieves 3.9bpm MAE of 90% percentile (1.3bpm decrease) for estimating RR in range of 5-25bpm. We validate the performance using smartphone video recordings of 30 subjects with uniform distribution of skin tone.","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":"122196880","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/BSN56160.2022.9928472
Simon Gjerde, Torjus L. Steffensen, Håvard N. Vestad, M. Steinert
Continuous measurement of cardiovascular parameters is important for monitoring cardiovascular health. Ballistocardiography is a noninvasive method of recording cardiovascular events. Here, we present a sensor system prototype for recording of the full-body ballistocardiogram in a wearable. An array of soft bladders in each sole are filled with water and connected to barometric pressure sensors. We demonstrate the use of the prototype to estimate the pulse transit time against continuous blood pressure in a validation experiment (n=14). Participants wore the sensor shoes while standing on a reference weight-scale. Simultaneous recordings were taken of the sole pressure arrays, finger-clip photoplethysmography, and continuous blood pressure via the volume-clamp method. Measurements were taken at rest, during cold-pressor intervention for 60 seconds, and 3 minutes following end of intervention. The waveform of the ballistocardiograms captured by the proposed sensor system corresponded well to the simultaneously collected waveforms from the reference weigh-scale. Pulse-transit time estimated from shoe BCG and PPG show inverse correlation to vasoconstriction-induced blood pressure increase. By demonstrating the use of the system to compute a vascular transit time, we show the potential of ballistocardiographic insoles as a wearable sensor interface for cardiovascular monitoring.
{"title":"Windows to the Sole: Prototyping Soft Sensors for Wearable Ballistocardiography","authors":"Simon Gjerde, Torjus L. Steffensen, Håvard N. Vestad, M. Steinert","doi":"10.1109/BSN56160.2022.9928472","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928472","url":null,"abstract":"Continuous measurement of cardiovascular parameters is important for monitoring cardiovascular health. Ballistocardiography is a noninvasive method of recording cardiovascular events. Here, we present a sensor system prototype for recording of the full-body ballistocardiogram in a wearable. An array of soft bladders in each sole are filled with water and connected to barometric pressure sensors. We demonstrate the use of the prototype to estimate the pulse transit time against continuous blood pressure in a validation experiment (n=14). Participants wore the sensor shoes while standing on a reference weight-scale. Simultaneous recordings were taken of the sole pressure arrays, finger-clip photoplethysmography, and continuous blood pressure via the volume-clamp method. Measurements were taken at rest, during cold-pressor intervention for 60 seconds, and 3 minutes following end of intervention. The waveform of the ballistocardiograms captured by the proposed sensor system corresponded well to the simultaneously collected waveforms from the reference weigh-scale. Pulse-transit time estimated from shoe BCG and PPG show inverse correlation to vasoconstriction-induced blood pressure increase. By demonstrating the use of the system to compute a vascular transit time, we show the potential of ballistocardiographic insoles as a wearable sensor interface for cardiovascular monitoring.","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":"131522490","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/BSN56160.2022.9928522
M. Reiser, A. Breidenassel, O. Amft
We analyse the influence of reflective photoplethysmography (PPG) sensor positioning relative to blood vessels. A voxel based Monte Carlo simulation framework was developed and validated to simulate photon-tissue interactions. An anatomical model comprising a multi-layer skin description with a blood vessel is presented to simulate PPG sensor positioning at the volar wrist. The simulation framework was validated against standard test cases reported in literature. The blood vessel was considered in regular and dilated states. Simulations were performed with 108 photon packets and repeated five times for each condition, including wavelength, relative position of PPG sensor and vessel, and vessel dilation state. Statistical weights were associated to photon packets to represent absorption and scattering effects. A symmetrical arrangement of the PPG sensor around the blood vessel showed the maximum AC signal. When the PPG sensor was not centrally placed over the vessel, simulated photon weight in systolic and diastolic state deteriorated by ≥5% for both wavelengths. With a position-dependent variation of ≥5% at 660 nm and ≥12% at 940 nm of light absorption, blood had the most profound effect on signal quality. The mean penetration depth is dependent on the blood vessel position for both wavelengths. Our simulation results demonstrate the susceptibility of reflective PPG measurement to interference and could explain wearable PPG sensor performance variations related to positioning and wavelength.
{"title":"Simulation framework for reflective PPG signal analysis depending on sensor placement and wavelength","authors":"M. Reiser, A. Breidenassel, O. Amft","doi":"10.1109/BSN56160.2022.9928522","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928522","url":null,"abstract":"We analyse the influence of reflective photoplethysmography (PPG) sensor positioning relative to blood vessels. A voxel based Monte Carlo simulation framework was developed and validated to simulate photon-tissue interactions. An anatomical model comprising a multi-layer skin description with a blood vessel is presented to simulate PPG sensor positioning at the volar wrist. The simulation framework was validated against standard test cases reported in literature. The blood vessel was considered in regular and dilated states. Simulations were performed with 108 photon packets and repeated five times for each condition, including wavelength, relative position of PPG sensor and vessel, and vessel dilation state. Statistical weights were associated to photon packets to represent absorption and scattering effects. A symmetrical arrangement of the PPG sensor around the blood vessel showed the maximum AC signal. When the PPG sensor was not centrally placed over the vessel, simulated photon weight in systolic and diastolic state deteriorated by ≥5% for both wavelengths. With a position-dependent variation of ≥5% at 660 nm and ≥12% at 940 nm of light absorption, blood had the most profound effect on signal quality. The mean penetration depth is dependent on the blood vessel position for both wavelengths. Our simulation results demonstrate the susceptibility of reflective PPG measurement to interference and could explain wearable PPG sensor performance variations related to positioning and wavelength.","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":"131524966","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/BSN56160.2022.9928521
Abdullah Mamun, Krista S. Leonard, M. Buman, Hassan Ghasemzadeh
Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person’s physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future’s activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2% to 11.1% in mean-absolute-error.
{"title":"Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design","authors":"Abdullah Mamun, Krista S. Leonard, M. Buman, Hassan Ghasemzadeh","doi":"10.1109/BSN56160.2022.9928521","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928521","url":null,"abstract":"Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person’s physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future’s activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2% to 11.1% in mean-absolute-error.","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":"116331453","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/BSN56160.2022.9928477
Chengyi Zhang, Qingyun Jin, Mohan Zhao, Dingguo Zhang, Lin Lin
For a long time, people have carried out various studies on human body communication (HBC) in order to establish a suitable communication link through human body. However, in the galvanic coupled method of HBC, the high current intensity is rarely used to implement the communication link. In the medical field, functional electrical stimulation (FES) is often used to send high intensity electrical pulses to make muscles contract, and this contraction phenomenon will generate surface electromyography (sEMG) signals on the surface of human skins. According to this principle and the galvanic coupling method of HBC, we propose a new digital communication system based on FES and sEMG signal detection with human body as communication channel in this paper. We modulate the transmitted signal into electrical stimulation to stimulate the muscles and detect the sEMG signal caused by it to achieve a complete communication process. The framework of the entire communication system is proposed. Its error performance for different stimulation parameters is tested and evaluated by experiments. Using FES and sEMG signal detection, our work makes a new exploration of HBC at high current intensities and enables a complete communication link. This work is expected to be applied to the HBC design combined with electrical stimulation in medical field.
{"title":"An Experimental Study of Digital Communication System with Human Body as Communication Channel","authors":"Chengyi Zhang, Qingyun Jin, Mohan Zhao, Dingguo Zhang, Lin Lin","doi":"10.1109/BSN56160.2022.9928477","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928477","url":null,"abstract":"For a long time, people have carried out various studies on human body communication (HBC) in order to establish a suitable communication link through human body. However, in the galvanic coupled method of HBC, the high current intensity is rarely used to implement the communication link. In the medical field, functional electrical stimulation (FES) is often used to send high intensity electrical pulses to make muscles contract, and this contraction phenomenon will generate surface electromyography (sEMG) signals on the surface of human skins. According to this principle and the galvanic coupling method of HBC, we propose a new digital communication system based on FES and sEMG signal detection with human body as communication channel in this paper. We modulate the transmitted signal into electrical stimulation to stimulate the muscles and detect the sEMG signal caused by it to achieve a complete communication process. The framework of the entire communication system is proposed. Its error performance for different stimulation parameters is tested and evaluated by experiments. Using FES and sEMG signal detection, our work makes a new exploration of HBC at high current intensities and enables a complete communication link. This work is expected to be applied to the HBC design combined with electrical stimulation in medical field.","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":"116438122","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/BSN56160.2022.9928524
Giannis Botilias, C. Stylios
Leading a sedentary lifestyle is becoming a significant public health issue, but a way of dealing with this issue is Physical Activity (PA). Regular PA is proven to help prevent and manage chronic diseases, maintain healthy body weight, and improve fitness. However, starting any PA after a long sedentary lifestyle carries risks of injuries, and a smoother transition with appropriate fitness programs is required. The rapidly growing number of smartphone users has given birth to broad-spectrum apps that use built-in sensors and collect data to provide insights about health and fitness. This work presents a fitness mobile application developed following the World Health Organization (WHO) guidelines for the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) self-screening tool utilizing built-in mobile sensors.
{"title":"App for Physical Fitness Improvement based on Physical Activity Guidelines and Self-Testing Tools","authors":"Giannis Botilias, C. Stylios","doi":"10.1109/BSN56160.2022.9928524","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928524","url":null,"abstract":"Leading a sedentary lifestyle is becoming a significant public health issue, but a way of dealing with this issue is Physical Activity (PA). Regular PA is proven to help prevent and manage chronic diseases, maintain healthy body weight, and improve fitness. However, starting any PA after a long sedentary lifestyle carries risks of injuries, and a smoother transition with appropriate fitness programs is required. The rapidly growing number of smartphone users has given birth to broad-spectrum apps that use built-in sensors and collect data to provide insights about health and fitness. This work presents a fitness mobile application developed following the World Health Organization (WHO) guidelines for the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) self-screening tool utilizing built-in mobile sensors.","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":"116535479","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/BSN56160.2022.9928504
L. Uhlenberg, O. Amft
We present a modelling and simulation framework to synthesise body-worn inertial sensor data based on personalised human body surface and biomechanical models. Anthropometric data and reference images were used to create personalised body surface mesh models. The mesh armature was aligned using motion capture reference pose and afterwards mesh and armature were parented. In addition, skeletal models were created using an established musculoskeletal dynamic modelling framework. Four activities of daily living (ADL), including upper and lower limbs were simulated with surface and skeletal models using motion capture data as stimuli. Acceleration and angular velocity data were simulated for 12 body areas of surface models and 8 body areas of skeletal models. We compared simulated inertial sensor data of both models against physical IMU measurements that were obtained simultaneously with video motion capture. Results showed average errors of 27 °/s vs. 31 °/s and 1.7 m/s2 vs. 3.3 m/s2 for surface and skeletal models, respectively. Mean correlation coefficients of body surface models ranged between 0.2 – 0.9 for simulated angular velocity and between 0.1 – 0.8 for simulated acceleration when compared to physical IMU data. The proposed surface modelling consistently showed similar or lower error compared to established skeletal modelling across ADLs and study participants. Body surface models can offer a more realistic representation compared to skeletal models for simulation-based analysis and optimisation of wearable inertial sensor systems.
{"title":"Comparison of Surface Models and Skeletal Models for Inertial Sensor Data Synthesis","authors":"L. Uhlenberg, O. Amft","doi":"10.1109/BSN56160.2022.9928504","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928504","url":null,"abstract":"We present a modelling and simulation framework to synthesise body-worn inertial sensor data based on personalised human body surface and biomechanical models. Anthropometric data and reference images were used to create personalised body surface mesh models. The mesh armature was aligned using motion capture reference pose and afterwards mesh and armature were parented. In addition, skeletal models were created using an established musculoskeletal dynamic modelling framework. Four activities of daily living (ADL), including upper and lower limbs were simulated with surface and skeletal models using motion capture data as stimuli. Acceleration and angular velocity data were simulated for 12 body areas of surface models and 8 body areas of skeletal models. We compared simulated inertial sensor data of both models against physical IMU measurements that were obtained simultaneously with video motion capture. Results showed average errors of 27 °/s vs. 31 °/s and 1.7 m/s2 vs. 3.3 m/s2 for surface and skeletal models, respectively. Mean correlation coefficients of body surface models ranged between 0.2 – 0.9 for simulated angular velocity and between 0.1 – 0.8 for simulated acceleration when compared to physical IMU data. The proposed surface modelling consistently showed similar or lower error compared to established skeletal modelling across ADLs and study participants. Body surface models can offer a more realistic representation compared to skeletal models for simulation-based analysis and optimisation of wearable inertial sensor systems.","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":"124687000","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/BSN56160.2022.9928488
Yicheng Yang, Weibang Bai, Benny P. L. Lo
Hearing aid devices have been around for decades, while most of them focus on sound amplification and SNR improvement. This paper proposes an artificial ear based on the vibrotactile feedback. The speech signal is converted into the vibrotactile devices placed around the subject’s ear through the speech recognition algorithm and pattern coding method. Preliminary experiments on the prototype consisting of six motors which has shown that the recognition accuracy of letters and daily sentences reached 90%. The learning time of interpreting the vibrotactile signals could be less than four times that in real-time conversation, proving the feasibility of the proposed device for real-life application.
{"title":"A Customized Artificial Ear Based on Vibrotactile Feedback: A Pilot Study","authors":"Yicheng Yang, Weibang Bai, Benny P. L. Lo","doi":"10.1109/BSN56160.2022.9928488","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928488","url":null,"abstract":"Hearing aid devices have been around for decades, while most of them focus on sound amplification and SNR improvement. This paper proposes an artificial ear based on the vibrotactile feedback. The speech signal is converted into the vibrotactile devices placed around the subject’s ear through the speech recognition algorithm and pattern coding method. Preliminary experiments on the prototype consisting of six motors which has shown that the recognition accuracy of letters and daily sentences reached 90%. The learning time of interpreting the vibrotactile signals could be less than four times that in real-time conversation, proving the feasibility of the proposed device for real-life application.","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":"121707439","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/BSN56160.2022.9928473
K. Pavlov, A. Perchik, V. Tsepulin, Georgii Megre, Evgenii Nikolaev, Elena Volkova, Jaehyuck Park, Namseok Chang, Wonseok Lee, Justin Younghyun Kim
This study aimed to develop the new fitness function for wearable devices, namely – Sweat loss estimation during running activity. Machine learning model (polynomial Kernel Ridge Regression) was trained and validated with large and diverse dataset. Totally 568 human subjects participated in 748 running tests. Sweat loss contributing factors such as users’ anthropometric parameters, distance, ambient temperature and humidity were distributed in the wide range of values. The performance of fully automatic sweat loss estimation algorithm provides average root mean square error (RMSE) = 236 ml; more important health-related parameter body weight percentage RMSE (RMSEBWP) = 0.33% and coefficient of determination (R2) = 0.79. To the authors' knowledge the algorithm provides the highest performance among existing solutions or ever described in literature.
{"title":"Sweat Loss Estimation Solution for Smartwatch","authors":"K. Pavlov, A. Perchik, V. Tsepulin, Georgii Megre, Evgenii Nikolaev, Elena Volkova, Jaehyuck Park, Namseok Chang, Wonseok Lee, Justin Younghyun Kim","doi":"10.1109/BSN56160.2022.9928473","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928473","url":null,"abstract":"This study aimed to develop the new fitness function for wearable devices, namely – Sweat loss estimation during running activity. Machine learning model (polynomial Kernel Ridge Regression) was trained and validated with large and diverse dataset. Totally 568 human subjects participated in 748 running tests. Sweat loss contributing factors such as users’ anthropometric parameters, distance, ambient temperature and humidity were distributed in the wide range of values. The performance of fully automatic sweat loss estimation algorithm provides average root mean square error (RMSE) = 236 ml; more important health-related parameter body weight percentage RMSE (RMSEBWP) = 0.33% and coefficient of determination (R2) = 0.79. To the authors' knowledge the algorithm provides the highest performance among existing solutions or ever described in literature.","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":"126814340","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/BSN56160.2022.9928449
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":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":"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}
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：