Pub Date : 2017-11-01DOI: 10.1109/HIC.2017.8227608
B. Moatamed, Sajad Darabi, Migyeong Gwak, Mohammad Kachuee, Casey J. Metoyer, Mike Linn, M. Sarrafzadeh
Many coaches and athletes are showing an increasing interest in training monitoring systems every year. There is a plethora of performance markers that can aid in a coaches assessment of physiological and psychological conditions of their athletes. These markers can indicate an athletes readiness for competition, adaptation to training, or risk for injury. However, studies have shown examination of these performance markers individually may not result in a clear perception of ones performance. Hence, an inclusive analysis of these metrics is required to achieve meaningful assessment. Recently with the growing use of wearable activity trackers, we have access to many of these markers. Currently, there are a few sport monitoring tools which are using a subset of these metrics and are mostly providing real-time data visualization to coaching staff. However, an appropriate athletic performance monitoring system should be intuitive, provide useful data analysis, feedback and reliable predictions to coaches and athletes. In this paper, we are proposing an athletic monitoring system which collects a comprehensive set of metrics and visualize them in real-time and informs coaches about athlete's readiness score.
{"title":"Sport analytics platform for athletic readiness assessment","authors":"B. Moatamed, Sajad Darabi, Migyeong Gwak, Mohammad Kachuee, Casey J. Metoyer, Mike Linn, M. Sarrafzadeh","doi":"10.1109/HIC.2017.8227608","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227608","url":null,"abstract":"Many coaches and athletes are showing an increasing interest in training monitoring systems every year. There is a plethora of performance markers that can aid in a coaches assessment of physiological and psychological conditions of their athletes. These markers can indicate an athletes readiness for competition, adaptation to training, or risk for injury. However, studies have shown examination of these performance markers individually may not result in a clear perception of ones performance. Hence, an inclusive analysis of these metrics is required to achieve meaningful assessment. Recently with the growing use of wearable activity trackers, we have access to many of these markers. Currently, there are a few sport monitoring tools which are using a subset of these metrics and are mostly providing real-time data visualization to coaching staff. However, an appropriate athletic performance monitoring system should be intuitive, provide useful data analysis, feedback and reliable predictions to coaches and athletes. In this paper, we are proposing an athletic monitoring system which collects a comprehensive set of metrics and visualize them in real-time and informs coaches about athlete's readiness score.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131221633","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227625
D. Bender, R. Morgan, V. Nadkarni, C. Nataraj
In this study we address an important pediatric cardiopulmonary resuscitation problem to identify the cause of a cardiac arrest during the beginning of cardiopulmonary resuscitation. A support vector algorithm was trained and tested using a feature set constructed through wavelet transform analysis of experimental electrocardiography and heart rate data provided by Children's Hospital of Philadelphia. The approach developed in this study yielded an average classification accuracy above 93%.
{"title":"Classification of asphyxia & ventricular fibrillation induced cardiac arrest for cardiopulmonary resuscitation","authors":"D. Bender, R. Morgan, V. Nadkarni, C. Nataraj","doi":"10.1109/HIC.2017.8227625","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227625","url":null,"abstract":"In this study we address an important pediatric cardiopulmonary resuscitation problem to identify the cause of a cardiac arrest during the beginning of cardiopulmonary resuscitation. A support vector algorithm was trained and tested using a feature set constructed through wavelet transform analysis of experimental electrocardiography and heart rate data provided by Children's Hospital of Philadelphia. The approach developed in this study yielded an average classification accuracy above 93%.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129773899","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227570
S. Almouahed, C. Hamitouche, P. Poignet, E. Stindel
Energy harvesting inside human body is crucial for powering implantable long-term biomedical devices. In this paper, the performance of a piezoelectric energy harvester embedded within custom-designed knee implant in powering RF transmitter was evaluated through simulations. This power harvester is composed of four piezoelectric generators along with four off-the-shelf power conditioning circuits. It can harvest electric power from dynamic forces dissipated inside knee implant during walking. The results demonstrate the possibility to deliver a constant electric power of 59.4mW during a simulated walking cycle. This conditioned power can operate an off-the-shelf ultra-low power RF transmitter. This may eliminate the need for rechargeable batteries to power instrumented knee implants. The proposed piezoelectric generator along with the ultra-miniature power conditioning and telemetry circuits may be integrated within the knee implant without increasing its physical size or changing its geometrical shape significantly with respect to the conventional one. Therefore, its dynamic functionality and mechanical longevity may not be affected.
{"title":"Battery-free force sensor for instrumented knee implant","authors":"S. Almouahed, C. Hamitouche, P. Poignet, E. Stindel","doi":"10.1109/HIC.2017.8227570","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227570","url":null,"abstract":"Energy harvesting inside human body is crucial for powering implantable long-term biomedical devices. In this paper, the performance of a piezoelectric energy harvester embedded within custom-designed knee implant in powering RF transmitter was evaluated through simulations. This power harvester is composed of four piezoelectric generators along with four off-the-shelf power conditioning circuits. It can harvest electric power from dynamic forces dissipated inside knee implant during walking. The results demonstrate the possibility to deliver a constant electric power of 59.4mW during a simulated walking cycle. This conditioned power can operate an off-the-shelf ultra-low power RF transmitter. This may eliminate the need for rechargeable batteries to power instrumented knee implants. The proposed piezoelectric generator along with the ultra-miniature power conditioning and telemetry circuits may be integrated within the knee implant without increasing its physical size or changing its geometrical shape significantly with respect to the conventional one. Therefore, its dynamic functionality and mechanical longevity may not be affected.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123685249","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227606
Marisa Walker, Weiwei Ge, J. Gichoya, S. Purkayastha
The use of clinical practice guidelines to improve quality of care has been a vividly discussed topic. Clinical practice guidelines (CPG) aim to improve the health of patients by guiding individual care in clinical settings. CPGs bring potential benefits for patients by improving clinical decision making, improving efficiency and enhancing patient care, while essentially optimizing financial value. Chronic conditions like heart disease, stroke, and chronic obstructive pulmonary disease (COPD), plague the US healthcare system causing several million dollars in healthcare related cost. This paper demonstrates the development of a CPG into an open-source EHR system to effectively manage COPD patients. The CPG is incorporated using the open web app standard, which allows it to be used with any web browser based EHR system, once data from the EHR system can be fed into the app. As a result, the CPG helps create a more effective and efficient decision-making process.
{"title":"Implementing clinical practice guidelines for chronic obstructive pulmonary disease in an EHR system","authors":"Marisa Walker, Weiwei Ge, J. Gichoya, S. Purkayastha","doi":"10.1109/HIC.2017.8227606","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227606","url":null,"abstract":"The use of clinical practice guidelines to improve quality of care has been a vividly discussed topic. Clinical practice guidelines (CPG) aim to improve the health of patients by guiding individual care in clinical settings. CPGs bring potential benefits for patients by improving clinical decision making, improving efficiency and enhancing patient care, while essentially optimizing financial value. Chronic conditions like heart disease, stroke, and chronic obstructive pulmonary disease (COPD), plague the US healthcare system causing several million dollars in healthcare related cost. This paper demonstrates the development of a CPG into an open-source EHR system to effectively manage COPD patients. The CPG is incorporated using the open web app standard, which allows it to be used with any web browser based EHR system, once data from the EHR system can be fed into the app. As a result, the CPG helps create a more effective and efficient decision-making process.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124778517","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227595
Si Mon Kueh, T. Kazmierski
This paper outlines the feasibility of detecting epilepsy though low-cost and low-energy dedicated hardware with bit-serial processing. The concept of a novel bit-serial data processing unit (DPU) is presented which implements the functionality of a complete neuron. The proposed approach has been tested using various network configurations and compared with related work. The proposed DPU uses only 24 Adaptive Logic Modules on an Altera Cyclone V FPGA. An array of these DPUs are controlled by a simple finite state machine. The proposed DPU allows the construction of complex hardware ANNs that can be implemented in portable equipment that suits the needs of a single epileptic patient in his or her daily activities to detect impending seizure events.
本文概述了利用低成本、低能耗的专用硬件进行位串行处理检测癫痫的可行性。提出了一种实现完整神经元功能的新型位串行数据处理单元(DPU)的概念。该方法已在不同的网络配置下进行了测试，并与相关工作进行了比较。提出的DPU在Altera Cyclone V FPGA上仅使用24个自适应逻辑模块。这些dpu的数组由一个简单的有限状态机控制。提出的DPU允许构建复杂的硬件人工神经网络，可以在便携式设备中实现，以满足单个癫痫患者在其日常活动中检测即将发生的癫痫事件的需要。
{"title":"A dedicated bit-serial hardware neuron for massively-parallel neural networks in fast epilepsy diagnosis","authors":"Si Mon Kueh, T. Kazmierski","doi":"10.1109/HIC.2017.8227595","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227595","url":null,"abstract":"This paper outlines the feasibility of detecting epilepsy though low-cost and low-energy dedicated hardware with bit-serial processing. The concept of a novel bit-serial data processing unit (DPU) is presented which implements the functionality of a complete neuron. The proposed approach has been tested using various network configurations and compared with related work. The proposed DPU uses only 24 Adaptive Logic Modules on an Altera Cyclone V FPGA. An array of these DPUs are controlled by a simple finite state machine. The proposed DPU allows the construction of complex hardware ANNs that can be implemented in portable equipment that suits the needs of a single epileptic patient in his or her daily activities to detect impending seizure events.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911835","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227588
Mohsen Nabian, A. Nouhi, Yu Yin, S. Ostadabbas
Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG) and Impedance Cardiography (ICG) are among physiological signals widely used in various biomedical applications including health tracking, sleep quality assessment, early disease detection/diagnosis and human affective state recognition. This paper presents the development of a biosignal-specific processing and feature extraction tool for analyzing these physiological signals according to the state-of-the-art studies reported in the scientific literature. This tool is intended to assist researchers in machine learning and pattern recognition to extract feature matrix from these bio-signals automatically and reliably. In this paper, we provided the algorithms used for the signal-specific filtering and segmentation as well as extracting features that have been shown highly relevant to a better category discrimination in an intended application. This tool is an open-source software written in MATLAB and made compatible with MathWorks Classification Learner app for further classification purposes such as model training, cross-validation scheme farming, and classification result computation.
{"title":"A biosignal-specific processing tool for machine learning and pattern recognition","authors":"Mohsen Nabian, A. Nouhi, Yu Yin, S. Ostadabbas","doi":"10.1109/HIC.2017.8227588","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227588","url":null,"abstract":"Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG) and Impedance Cardiography (ICG) are among physiological signals widely used in various biomedical applications including health tracking, sleep quality assessment, early disease detection/diagnosis and human affective state recognition. This paper presents the development of a biosignal-specific processing and feature extraction tool for analyzing these physiological signals according to the state-of-the-art studies reported in the scientific literature. This tool is intended to assist researchers in machine learning and pattern recognition to extract feature matrix from these bio-signals automatically and reliably. In this paper, we provided the algorithms used for the signal-specific filtering and segmentation as well as extracting features that have been shown highly relevant to a better category discrimination in an intended application. This tool is an open-source software written in MATLAB and made compatible with MathWorks Classification Learner app for further classification purposes such as model training, cross-validation scheme farming, and classification result computation.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129343235","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227629
F. Sun, Weili Chen, Hojeong Yu, Akid Omob, Ryan Brisbin, A. Ganguli, V. Vemuri, P. Strzeboński, Guangzhe Cui, K. J. Allen, Smit Desai, Weiran Lin, David M. Nash, D. Hirschberg, Ian Brooks, R. Bashir, B. Cunningham
New tools are needed to enable rapid detection, identification, and reporting of infectious viral and microbial pathogens in a wide variety of point-of-care applications that impact human and animal health. We report the design, construction, and characterization of a platform for multiplexed analysis of disease-specific DNA sequences that utilizes a smartphone camera as the sensor in conjunction with a handheld instrument that interfaces the phone with a silicon-based microfluidic chip. Utilizing specific nucleic acid sequences for four equine respiratory pathogens as representative examples, we demonstrated the ability of the system to use a single 15-μL droplet of test sample to perform selective positive/negative determination of target sequences, including integrated experimental controls, in approximately 30 minutes. The system achieves detection limits comparable to those obtained by laboratory-based methods and instruments.
{"title":"Multiplexed detection of infectious diseases with microfluidic loop-mediated isothermal amplification and a smartphone","authors":"F. Sun, Weili Chen, Hojeong Yu, Akid Omob, Ryan Brisbin, A. Ganguli, V. Vemuri, P. Strzeboński, Guangzhe Cui, K. J. Allen, Smit Desai, Weiran Lin, David M. Nash, D. Hirschberg, Ian Brooks, R. Bashir, B. Cunningham","doi":"10.1109/HIC.2017.8227629","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227629","url":null,"abstract":"New tools are needed to enable rapid detection, identification, and reporting of infectious viral and microbial pathogens in a wide variety of point-of-care applications that impact human and animal health. We report the design, construction, and characterization of a platform for multiplexed analysis of disease-specific DNA sequences that utilizes a smartphone camera as the sensor in conjunction with a handheld instrument that interfaces the phone with a silicon-based microfluidic chip. Utilizing specific nucleic acid sequences for four equine respiratory pathogens as representative examples, we demonstrated the ability of the system to use a single 15-μL droplet of test sample to perform selective positive/negative determination of target sequences, including integrated experimental controls, in approximately 30 minutes. The system achieves detection limits comparable to those obtained by laboratory-based methods and instruments.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694822","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227626
Jairo Maldonado-Contreras, P. Marayong, I-Hung Khoo, Rae Rivera, Brian Ruhe, Will Wu
Limited mobility severely impacts the quality of life of persons with lower-limb amputations. Therefore, it is imperative to develop proper rehabilitation techniques to prevent falls and injuries. A vibrotactile device was developed as a training tool to enhance the rehabilitation of persons with recent lower-limb amputations. Stimuli provided by the device trains the user to sense discrete perturbations and then perform a corrective movement to reduce the chance of a fall. This pilot study was conducted to test the functionality of the device in improving the prosthetic proprioception of lower-limb amputees and the effect of the training instruction on motor learning. Two subjects were included in this study, one control and one receiving experimental training, with both subjects performing standing and walking tasks. Standing trials were used to evaluate the improvements in response and movement times and walking trials were tested for improvements in correct movement. In the standing task, the control and the experimental subject showed a 0.1% and 17% improvement in response time, respectively. In the walking task, both subjects showed improvements in making correct movement. Future work will focus on the design improvements of the device and the experiment protocols to further evaluate the effectiveness of the training.
{"title":"Proprioceptive improvements of lower-limb amputees under training with a vibrotactile device — A pilot study","authors":"Jairo Maldonado-Contreras, P. Marayong, I-Hung Khoo, Rae Rivera, Brian Ruhe, Will Wu","doi":"10.1109/HIC.2017.8227626","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227626","url":null,"abstract":"Limited mobility severely impacts the quality of life of persons with lower-limb amputations. Therefore, it is imperative to develop proper rehabilitation techniques to prevent falls and injuries. A vibrotactile device was developed as a training tool to enhance the rehabilitation of persons with recent lower-limb amputations. Stimuli provided by the device trains the user to sense discrete perturbations and then perform a corrective movement to reduce the chance of a fall. This pilot study was conducted to test the functionality of the device in improving the prosthetic proprioception of lower-limb amputees and the effect of the training instruction on motor learning. Two subjects were included in this study, one control and one receiving experimental training, with both subjects performing standing and walking tasks. Standing trials were used to evaluate the improvements in response and movement times and walking trials were tested for improvements in correct movement. In the standing task, the control and the experimental subject showed a 0.1% and 17% improvement in response time, respectively. In the walking task, both subjects showed improvements in making correct movement. Future work will focus on the design improvements of the device and the experiment protocols to further evaluate the effectiveness of the training.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124233472","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227593
A. Samadani, D. Schulman, Portia E. Singh, Mladen Milošević
Personal emergency response systems (PERS) such as Philips Lifeline help seniors maintain independence and age in place. PERS can use predictive analytics to help risk stratification and promote response-efficient emergency services. This paper presents a framework for estimating significant associations between Lifeline user characteristics and occurrence of emergency events. Predictive variables including demographics, health conditions, environmental, and user-specific lifeline history were identified and their associations to emergency events were delineated. The predictive variables can help with 1) identifying individuals at high risk and 2) management and prioritization of care and preventive services, which can result in reducing adverse health events and improving user's quality of life.
{"title":"Association rule mining for risk prediction and stratification: A philips lifeline case study","authors":"A. Samadani, D. Schulman, Portia E. Singh, Mladen Milošević","doi":"10.1109/HIC.2017.8227593","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227593","url":null,"abstract":"Personal emergency response systems (PERS) such as Philips Lifeline help seniors maintain independence and age in place. PERS can use predictive analytics to help risk stratification and promote response-efficient emergency services. This paper presents a framework for estimating significant associations between Lifeline user characteristics and occurrence of emergency events. Predictive variables including demographics, health conditions, environmental, and user-specific lifeline history were identified and their associations to emergency events were delineated. The predictive variables can help with 1) identifying individuals at high risk and 2) management and prioritization of care and preventive services, which can result in reducing adverse health events and improving user's quality of life.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125691869","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 : 2017-11-01DOI: 10.1109/HIC.2017.8227614
E. Pino, Javier A. P. Chávez, P. Aqueveque
Ballistocardiogram (BCG) has been revisited in the last years as an unobtrusive method to detect heart beats. New electromechanical film (EMFi) sensors are now able to detect minimal oscillations in its surface, allowing to detect the mechanical action of the heart as it beats. This has allowed to develop unobtrusive systems for heart rate monitoring to be used as Point-of-Care devices, and to deploy them in waiting rooms, assisted living facilities or at home. In this work, an EMFi sensor is used to measure BCG via the pressure changes on the seat produced by the beating heart. In a lab environment, 34 healthy volunteers are measured under two conditions: at rest and after exercise, simultaneously with ECG. Also, in a clinical environment, 24 volunteers are also measured while waiting. The algorithm looks for the variability of the length transform at different scales or windows to determine a search window to detect beats from the BCG. A second correlation filter helps eliminate false peaks detected due to noise in the signal. Results show that in resting conditions, the mean error between the BCG HR and the reference ECG is only 0.4 beats per minute, with a standard deviation of 1.88. The noise rejection accuracy is 93%. The proposed algorithm can be used to identify beats and issue alarms under abnormal rhythms, providing timely alerts for at-risk population.
{"title":"BCG algorithm for unobtrusive heart rate monitoring","authors":"E. Pino, Javier A. P. Chávez, P. Aqueveque","doi":"10.1109/HIC.2017.8227614","DOIUrl":"https://doi.org/10.1109/HIC.2017.8227614","url":null,"abstract":"Ballistocardiogram (BCG) has been revisited in the last years as an unobtrusive method to detect heart beats. New electromechanical film (EMFi) sensors are now able to detect minimal oscillations in its surface, allowing to detect the mechanical action of the heart as it beats. This has allowed to develop unobtrusive systems for heart rate monitoring to be used as Point-of-Care devices, and to deploy them in waiting rooms, assisted living facilities or at home. In this work, an EMFi sensor is used to measure BCG via the pressure changes on the seat produced by the beating heart. In a lab environment, 34 healthy volunteers are measured under two conditions: at rest and after exercise, simultaneously with ECG. Also, in a clinical environment, 24 volunteers are also measured while waiting. The algorithm looks for the variability of the length transform at different scales or windows to determine a search window to detect beats from the BCG. A second correlation filter helps eliminate false peaks detected due to noise in the signal. Results show that in resting conditions, the mean error between the BCG HR and the reference ECG is only 0.4 beats per minute, with a standard deviation of 1.88. The noise rejection accuracy is 93%. The proposed algorithm can be used to identify beats and issue alarms under abnormal rhythms, providing timely alerts for at-risk population.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131386189","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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