Pub Date : 2022-09-27DOI: 10.1109/BSN56160.2022.9928483
Edoardo Spairani, Ana Belén Carballo Leyenda, J. Rodríguez-Marroyo, G. D. Toma, G. Magenes
In the present study we propose a novel method to automatically assess the quality of ECG signals collected through a wearable device in typical mountain rescuers activities. ECGs signals have been obtained during sessions of programmed field tests at the Bormio Ski Resort (Valtellina, Lombardy, Italy) in the month of March. Here, following the defined protocol, a group of 15 mountain rescuers has carried out daily rescuers’ activities, while wearing wearable textile system by Smartex Srl. The test protocol was designed to simulate the real physiological demands of mountain rescuers during their emergency deployments. Among the activities performed rescuers had to walk up and down hill in snow-covered trails, carrying stretchers onto which simulated victims were located etc… To infer the quality of ECG signals recorded we developed an algorithm for the automatic evaluation of collected signal deterioration. This method is based on the analysis of regularity of ECGs’ P-QRS-T complexes pattern. To estimate the maintenance of typical ECGs pattern shape, Sample Entropy (SampEn) was computed in moving fixed-length windows sliding along the signal, obtained after applying wavelet transform of the row ECG. The SampEn indices series was then thresholded to spot ECG points where P-QRS-T complexes were more or less easy to identify, respect to points where signal quality was completely deteriorated. Moreover, we evaluated signal quality maintenance while performing low and high intensity activities.
{"title":"Mountain Rescuers through the computation of Sample Entropy","authors":"Edoardo Spairani, Ana Belén Carballo Leyenda, J. Rodríguez-Marroyo, G. D. Toma, G. Magenes","doi":"10.1109/BSN56160.2022.9928483","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928483","url":null,"abstract":"In the present study we propose a novel method to automatically assess the quality of ECG signals collected through a wearable device in typical mountain rescuers activities. ECGs signals have been obtained during sessions of programmed field tests at the Bormio Ski Resort (Valtellina, Lombardy, Italy) in the month of March. Here, following the defined protocol, a group of 15 mountain rescuers has carried out daily rescuers’ activities, while wearing wearable textile system by Smartex Srl. The test protocol was designed to simulate the real physiological demands of mountain rescuers during their emergency deployments. Among the activities performed rescuers had to walk up and down hill in snow-covered trails, carrying stretchers onto which simulated victims were located etc… To infer the quality of ECG signals recorded we developed an algorithm for the automatic evaluation of collected signal deterioration. This method is based on the analysis of regularity of ECGs’ P-QRS-T complexes pattern. To estimate the maintenance of typical ECGs pattern shape, Sample Entropy (SampEn) was computed in moving fixed-length windows sliding along the signal, obtained after applying wavelet transform of the row ECG. The SampEn indices series was then thresholded to spot ECG points where P-QRS-T complexes were more or less easy to identify, respect to points where signal quality was completely deteriorated. Moreover, we evaluated signal quality maintenance while performing low and high intensity activities.","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":"129003725","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/BSN56160.2022.9928469
A. Mallick, Mukesh Kumar, Kamaldeep Arora, A. Sahani
The continual pressure on a skin surface can hamper blood supply from the subcutaneous regions. Blockage of blood supply is the primary reason for the development of Pressure Ulcers (PUs) in patients admitted to hospitals with impaired mobility. The dermal layer of a preterm neonate is less than 60% of the thickness of an adult and has a much higher susceptibility to developing pressure ulcers. In Neonatal Intensive Care Units (NICUs), babies lie down immobile for long hours in fixed positions. Hence, there is a 23% prevalence of PUs in NICUs worldwide. Therefore, it is advised that nursing staff should ensure frequent posture changes to avoid the development of PUs. This leads to an increased workload on them. We designed a Finite Element Modeling (FEM) of a neonatal anti-PU bed made from elastic material with alternating pressure channels and carried out simulations in ABAQUS CAE to validate this problem. We first simulated a neonatal phantom made from hyper-elastic material and laid it down on a flatbed. The pressure on the skin was taken as the baseline. We found that by activating alternating channels, the pressure increases in inflated regions and decreases in deflated regions compared to the baseline. As the inflation and deflation channels will be alternating, no long-term high-pressure points will be formed under the skin.
{"title":"Finite Element Modeling of a Pressure Ulcers Preventive Bed for Neonates","authors":"A. Mallick, Mukesh Kumar, Kamaldeep Arora, A. Sahani","doi":"10.1109/BSN56160.2022.9928469","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928469","url":null,"abstract":"The continual pressure on a skin surface can hamper blood supply from the subcutaneous regions. Blockage of blood supply is the primary reason for the development of Pressure Ulcers (PUs) in patients admitted to hospitals with impaired mobility. The dermal layer of a preterm neonate is less than 60% of the thickness of an adult and has a much higher susceptibility to developing pressure ulcers. In Neonatal Intensive Care Units (NICUs), babies lie down immobile for long hours in fixed positions. Hence, there is a 23% prevalence of PUs in NICUs worldwide. Therefore, it is advised that nursing staff should ensure frequent posture changes to avoid the development of PUs. This leads to an increased workload on them. We designed a Finite Element Modeling (FEM) of a neonatal anti-PU bed made from elastic material with alternating pressure channels and carried out simulations in ABAQUS CAE to validate this problem. We first simulated a neonatal phantom made from hyper-elastic material and laid it down on a flatbed. The pressure on the skin was taken as the baseline. We found that by activating alternating channels, the pressure increases in inflated regions and decreases in deflated regions compared to the baseline. As the inflation and deflation channels will be alternating, no long-term high-pressure points will be formed under the skin.","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":"115127168","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":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":"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 : 2022-07-01DOI: 10.1109/BSN56160.2022.9928500
Minh Cao, Brett Bailey, Wenhao Zhang, S. Fernandez, Aaron Han, Smiti Narayanan, Shrineel Patel, Steven Saletta, A. Stavrakis, Stephen J. Speicher, S. Seidlits, A. Naeim, Ramin Ramezani
A low-cost, accurate device to measure and record knee range of motion (ROM) is of the essential need to improve confidence in at-home rehabilitation. It is to reduce hospital stay duration and overall medical cost after Total Knee Arthroplasty (TKA) procedures. The shift in Medicare funding from pay-as-you-go to the Bundled Payments for Care Improvement (BPCI) has created a push towards at-home care over extended hospital stays. It has heavily affected TKA patients, who typically undergo physical therapy at the clinic after the procedure to ensure full recovery of ROM. In this paper, we use accelerometers to create a ROM sensor that can be integrated into the post-operative surgical dressing, so that the cost of the sensors can be included in the bundled payments. In this paper, we demonstrate the efficacy of our method in comparison to the baseline computer vision method. Our results suggest that calculating angular displacement from accelerometer sensors demonstrates accurate ROM recordings under both stationary and walking conditions. The device would keep track of angle measurements and alert the patient when certain angle thresholds have been crossed, allowing patients to recover safely at home instead of going to multiple physical therapy sessions. The affordability of our sensor makes it more accessible to patients in need. By manufacturing and utilizing our proposed device along with a built-in remote physical therapy program, the expected cost saving would be $2650 per patient throughout the recovery process after surgery.
{"title":"Range of Motion Sensors for Monitoring Recovery of Total Knee Arthroplasty","authors":"Minh Cao, Brett Bailey, Wenhao Zhang, S. Fernandez, Aaron Han, Smiti Narayanan, Shrineel Patel, Steven Saletta, A. Stavrakis, Stephen J. Speicher, S. Seidlits, A. Naeim, Ramin Ramezani","doi":"10.1109/BSN56160.2022.9928500","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928500","url":null,"abstract":"A low-cost, accurate device to measure and record knee range of motion (ROM) is of the essential need to improve confidence in at-home rehabilitation. It is to reduce hospital stay duration and overall medical cost after Total Knee Arthroplasty (TKA) procedures. The shift in Medicare funding from pay-as-you-go to the Bundled Payments for Care Improvement (BPCI) has created a push towards at-home care over extended hospital stays. It has heavily affected TKA patients, who typically undergo physical therapy at the clinic after the procedure to ensure full recovery of ROM. In this paper, we use accelerometers to create a ROM sensor that can be integrated into the post-operative surgical dressing, so that the cost of the sensors can be included in the bundled payments. In this paper, we demonstrate the efficacy of our method in comparison to the baseline computer vision method. Our results suggest that calculating angular displacement from accelerometer sensors demonstrates accurate ROM recordings under both stationary and walking conditions. The device would keep track of angle measurements and alert the patient when certain angle thresholds have been crossed, allowing patients to recover safely at home instead of going to multiple physical therapy sessions. The affordability of our sensor makes it more accessible to patients in need. By manufacturing and utilizing our proposed device along with a built-in remote physical therapy program, the expected cost saving would be $2650 per patient throughout the recovery process after surgery.","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":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116971197","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 : 2022-06-14DOI: 10.1109/BSN56160.2022.9928495
Ramesh Kumar Sah, M. McDonell, Patricia Pendry, Sara Parent, Hassan Ghasemzadeh, M. Cleveland
Stress detection and classification from wearable sensor data is an emerging area of research with significant implications for individuals’ physical and mental health. In this work, we introduce a new dataset, ADARP, which contains physiological data and self-report outcomes collected in real-world ambulatory settings involving individuals diagnosed with alcohol use disorders. We describe the user study, present details of the dataset, establish the significant correlation between physiological data and self-reported outcomes, demonstrate stress classification, and make our dataset public to facilitate research.
{"title":"ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting","authors":"Ramesh Kumar Sah, M. McDonell, Patricia Pendry, Sara Parent, Hassan Ghasemzadeh, M. Cleveland","doi":"10.1109/BSN56160.2022.9928495","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928495","url":null,"abstract":"Stress detection and classification from wearable sensor data is an emerging area of research with significant implications for individuals’ physical and mental health. In this work, we introduce a new dataset, ADARP, which contains physiological data and self-report outcomes collected in real-world ambulatory settings involving individuals diagnosed with alcohol use disorders. We describe the user study, present details of the dataset, establish the significant correlation between physiological data and self-reported outcomes, demonstrate stress classification, and make our dataset public to facilitate research.","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":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127877025","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 : 2022-02-14DOI: 10.1109/BSN56160.2022.9928466
Arttu Lämsä, Jaakko Tervonen, Jussi Liikka, Constantino Álvarez Casado, Miguel Bordallo L'opez
Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled data is not straightforward, since wearable sensor signals are not easy to label upon simple human inspection. In our work, we propose the use of neural networks for the generation of realistic signals and features using human activity monocular videos. We show how these generated features and signals can be utilized, instead of their real counterparts, to train HAR models that can recognize activities using signals obtained with wearable sensors. To prove the validity of our methods, we perform experiments on an activity recognition dataset created for the improvement of industrial work safety. We show that our model is able to realistically generate virtual sensor signals and features usable to train a HAR classifier with comparable performance as the one trained using real sensor data. Our results enable the use of available, labeled video data for training HAR models to classify signals from wearable sensors.
{"title":"Video2IMU: Realistic IMU features and signals from videos","authors":"Arttu Lämsä, Jaakko Tervonen, Jussi Liikka, Constantino Álvarez Casado, Miguel Bordallo L'opez","doi":"10.1109/BSN56160.2022.9928466","DOIUrl":"https://doi.org/10.1109/BSN56160.2022.9928466","url":null,"abstract":"Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled data is not straightforward, since wearable sensor signals are not easy to label upon simple human inspection. In our work, we propose the use of neural networks for the generation of realistic signals and features using human activity monocular videos. We show how these generated features and signals can be utilized, instead of their real counterparts, to train HAR models that can recognize activities using signals obtained with wearable sensors. To prove the validity of our methods, we perform experiments on an activity recognition dataset created for the improvement of industrial work safety. We show that our model is able to realistically generate virtual sensor signals and features usable to train a HAR classifier with comparable performance as the one trained using real sensor data. Our results enable the use of available, labeled video data for training HAR models to classify signals from wearable 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":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121522697","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}
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