Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299371
Heike Leutheuser, Stefan Gradl, B. Eskofier, A. Tobola, N. Lang, L. Anneken, M. Arnold, S. Achenbach
Far too many people are dying from stroke or other heart related diseases each year. Early detection of abnormal heart rhythm could trigger the timely presentation to the emergency department or outpatient unit. Smartphones are an integral part of everyone;s life and they form the ideal basis for mobile monitoring and real-time analysis of signals related to the human heart. In this work, we investigated the performance of arrhythmia classification systems using only features calculated from the time instances of individual heart beats. We built a sinusoidal model using N (N = 10, 15, 20) consecutive RR intervals to predict the (N+1)th RR interval. The integration of the innovative sinusoidal regression feature, together with the amplitude and phase of the proposed sinusoidal model, led to an increase in the mean class-dependent classification accuracies. Best mean class-dependent classification accuracies of 90% were achieved using a Naïve Bayes classifier. Well-performing realtime analysis arrhythmia classification algorithms using only the time instances of individual heart beats could have a tremendous impact in reducing healthcare costs and reducing the high number of deaths related to cardiovascular diseases.
{"title":"Arrhythmia classification using RR intervals: Improvement with sinusoidal regression feature","authors":"Heike Leutheuser, Stefan Gradl, B. Eskofier, A. Tobola, N. Lang, L. Anneken, M. Arnold, S. Achenbach","doi":"10.1109/BSN.2015.7299371","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299371","url":null,"abstract":"Far too many people are dying from stroke or other heart related diseases each year. Early detection of abnormal heart rhythm could trigger the timely presentation to the emergency department or outpatient unit. Smartphones are an integral part of everyone;s life and they form the ideal basis for mobile monitoring and real-time analysis of signals related to the human heart. In this work, we investigated the performance of arrhythmia classification systems using only features calculated from the time instances of individual heart beats. We built a sinusoidal model using N (N = 10, 15, 20) consecutive RR intervals to predict the (N+1)th RR interval. The integration of the innovative sinusoidal regression feature, together with the amplitude and phase of the proposed sinusoidal model, led to an increase in the mean class-dependent classification accuracies. Best mean class-dependent classification accuracies of 90% were achieved using a Naïve Bayes classifier. Well-performing realtime analysis arrhythmia classification algorithms using only the time instances of individual heart beats could have a tremendous impact in reducing healthcare costs and reducing the high number of deaths related to cardiovascular diseases.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128901437","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299415
Shaad Mahmud, Honggang Wang, Yong K Kim, Dapeng Li
A miniaturized monopole antenna was designed and fabricated on an organic paper and LCP material for wireless body area network. Compared with previous work, the proposed design has 20% reduction of the antenna size but with enhanced performance. The effects of the compact coplanar antenna under different twisting conditions is described in this paper. The proposed antennas are simulated and designed on an organic paper and a Liquid Crystal Polymer (LCP) substrate with dielectric constant Dr= 3.4 and thickness 15μm and 5μm respectively, occupying the area of 22×30mm2. A detailed discussion about radiation pattern, Gain, antenna efficiency and power pattern is given with the help of experimental and numerical results.
{"title":"Development of an inkjet printed green antenna and twisting effect for wireless body area network","authors":"Shaad Mahmud, Honggang Wang, Yong K Kim, Dapeng Li","doi":"10.1109/BSN.2015.7299415","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299415","url":null,"abstract":"A miniaturized monopole antenna was designed and fabricated on an organic paper and LCP material for wireless body area network. Compared with previous work, the proposed design has 20% reduction of the antenna size but with enhanced performance. The effects of the compact coplanar antenna under different twisting conditions is described in this paper. The proposed antennas are simulated and designed on an organic paper and a Liquid Crystal Polymer (LCP) substrate with dielectric constant Dr= 3.4 and thickness 15μm and 5μm respectively, occupying the area of 22×30mm2. A detailed discussion about radiation pattern, Gain, antenna efficiency and power pattern is given with the help of experimental and numerical results.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115942549","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299373
Peng Fang, Qifang Zhuo, Yan Cai, Lan Tian, Haoshi Zhang, Yue Zheng, Guanglin Li, Liming Wu, Xiaoqing Zhang
Piezoelectrets are polymer-foam based space-charge electrets with strong piezoelectric effect. The piezoelectricity in piezoelectrets occurs due to the elastic heterogeneous cellular structure and the regularly arranged dipolar space charges stored therein. Some polymers have been experimented for piezoelectret preparation, where polypropylene (PP) is the mostly applied material at present. PP piezoelectrets have several promising features, such as large piezoelectric d33 coefficient, small thickness, light weight, low cost, large area scale, as well as flexibility and even stretchability, which would enable them very suitable for applications in signal sensing and energy harvesting. In this work, the electromechanical properties of flexible and stretchable PP piezoelectrets are introduced and some of their possible applications as wearable physiological-signal sensors and micro-energy harvesters are demonstrated by experiments.
{"title":"Piezoelectrets and their applications as wearable physiological-signal sensors and energy harvesters","authors":"Peng Fang, Qifang Zhuo, Yan Cai, Lan Tian, Haoshi Zhang, Yue Zheng, Guanglin Li, Liming Wu, Xiaoqing Zhang","doi":"10.1109/BSN.2015.7299373","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299373","url":null,"abstract":"Piezoelectrets are polymer-foam based space-charge electrets with strong piezoelectric effect. The piezoelectricity in piezoelectrets occurs due to the elastic heterogeneous cellular structure and the regularly arranged dipolar space charges stored therein. Some polymers have been experimented for piezoelectret preparation, where polypropylene (PP) is the mostly applied material at present. PP piezoelectrets have several promising features, such as large piezoelectric d33 coefficient, small thickness, light weight, low cost, large area scale, as well as flexibility and even stretchability, which would enable them very suitable for applications in signal sensing and energy harvesting. In this work, the electromechanical properties of flexible and stretchable PP piezoelectrets are introduced and some of their possible applications as wearable physiological-signal sensors and micro-energy harvesters are demonstrated by experiments.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114353003","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299424
W. Tomlinson, Fabian Abarca, K. Chowdhury, M. Stojanovic, Christopher C. Yu
The recent surge of implantable and wearable medical devices have paved the way for realizing intra-body networks (IBNs). Traditional RF-based techniques fall short in wirelessly connecting such devices owing to absorption within body tissues. A different approach is known as galvanic coupling, which employs weak electrical current within naturally conducting tissues to enable intra-body communication. This work is focused on channel characterization of the human body tissues considering the propagation of such electrical signals through it that carry data. Experiments were conducted using porcine tissue (in lieu of actual human tissue) with skin, fat and muscle layers in the frequency range of 100 kHz to 1 MHz. By utilizing single-carrier BPSK modulated Pseudorandom Noise Sequences, a correlative channel sounding system was implemented, leading to the following contributions: (1) measurements of the channel impulse and frequency response, (2) a noise analysis and capacity estimation, and (3) the comparison of results with existing models.
{"title":"Experimental assessment of human-body-like tissue as a communication channel for galvanic coupling","authors":"W. Tomlinson, Fabian Abarca, K. Chowdhury, M. Stojanovic, Christopher C. Yu","doi":"10.1109/BSN.2015.7299424","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299424","url":null,"abstract":"The recent surge of implantable and wearable medical devices have paved the way for realizing intra-body networks (IBNs). Traditional RF-based techniques fall short in wirelessly connecting such devices owing to absorption within body tissues. A different approach is known as galvanic coupling, which employs weak electrical current within naturally conducting tissues to enable intra-body communication. This work is focused on channel characterization of the human body tissues considering the propagation of such electrical signals through it that carry data. Experiments were conducted using porcine tissue (in lieu of actual human tissue) with skin, fat and muscle layers in the frequency range of 100 kHz to 1 MHz. By utilizing single-carrier BPSK modulated Pseudorandom Noise Sequences, a correlative channel sounding system was implemented, leading to the following contributions: (1) measurements of the channel impulse and frequency response, (2) a noise analysis and capacity estimation, and (3) the comparison of results with existing models.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131999999","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299384
Stepan Gorgutsa, M. Khalil, Victor Bélanger-Garnier, J. Viens, Y. Messaddeq, B. Gosselin, S. Larochelle
In this work, we present the emissive performance of wearable radio-frequency (RF) textiles made from multi-material fibers, for both on-body and off-body scenarios, for body area network applications through ISM (2.4 GHz) bands. It is shown that the emissive performance of the RF textiles in terms of return loss (S11), radiation pattern, and efficiency (gain) were similar to commercial router antennas, while the center frequency shift and band broadening were reduced due in part to the small form factor of the fiber antennas. The RF textiles were fabricated by integrating unobtrusive polymer-glass-metal fiber composites into a textile host using conventional weaving process. This approach provided good RF emissive performance in compliance with safety regulations while preserving the mechanical and cosmetic properties of the garments.
{"title":"Emissive performance of wearable RF textiles made from multi-material fibers","authors":"Stepan Gorgutsa, M. Khalil, Victor Bélanger-Garnier, J. Viens, Y. Messaddeq, B. Gosselin, S. Larochelle","doi":"10.1109/BSN.2015.7299384","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299384","url":null,"abstract":"In this work, we present the emissive performance of wearable radio-frequency (RF) textiles made from multi-material fibers, for both on-body and off-body scenarios, for body area network applications through ISM (2.4 GHz) bands. It is shown that the emissive performance of the RF textiles in terms of return loss (S11), radiation pattern, and efficiency (gain) were similar to commercial router antennas, while the center frequency shift and band broadening were reduced due in part to the small form factor of the fiber antennas. The RF textiles were fabricated by integrating unobtrusive polymer-glass-metal fiber composites into a textile host using conventional weaving process. This approach provided good RF emissive performance in compliance with safety regulations while preserving the mechanical and cosmetic properties of the garments.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133380147","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299360
N. Beckers, R. Fineman, L. Stirling
Robotic assistive devices show potential to aid hand function using surface electromyography (sEMG) as a control signal. Current implementations of these robotic systems typically do not include interaction with the environment, which naturally occurs during functional tasks. Further, many applications have experts place the sEMG sensors on specific muscles, which benefits precision alignment that may not be possible by non-experts. This study informs algorithm development for controlling assistive devices for grasping and releasing objects using kinematics and non-specifically placed sEMG sensors. Significant effects of object type were found in the grip aperture and joint kinematics. Muscle activity was significantly affected by small alignment changes in the sensor placement, yet the features analyzed showed anticipatory mechanisms prior to grasp and release. The appropriate inclusion of placement variability within a control architecture can be coupled with the kinematics and sEMG features to inform object type and anticipate grasp and release.
{"title":"Anticipatory signals in kinematics and muscle activity during functional grasp and release","authors":"N. Beckers, R. Fineman, L. Stirling","doi":"10.1109/BSN.2015.7299360","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299360","url":null,"abstract":"Robotic assistive devices show potential to aid hand function using surface electromyography (sEMG) as a control signal. Current implementations of these robotic systems typically do not include interaction with the environment, which naturally occurs during functional tasks. Further, many applications have experts place the sEMG sensors on specific muscles, which benefits precision alignment that may not be possible by non-experts. This study informs algorithm development for controlling assistive devices for grasping and releasing objects using kinematics and non-specifically placed sEMG sensors. Significant effects of object type were found in the grip aperture and joint kinematics. Muscle activity was significantly affected by small alignment changes in the sensor placement, yet the features analyzed showed anticipatory mechanisms prior to grasp and release. The appropriate inclusion of placement variability within a control architecture can be coupled with the kinematics and sEMG features to inform object type and anticipate grasp and release.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114311592","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299348
H. Kalantarian, N. Alshurafa, Ebrahim Nemati, Tuan Le, M. Sarrafzadeh
Poor adherence to prescription medication can compromise treatment effectiveness and cost the billions of dollars in unnecessary health care expenses. Though various interventions have been proposed for estimating adherence rates, few have been shown to be effective. Digital systems are capable of estimating adherence without extensive user involvement and can potentially provide higher accuracy with lower user burden than manual methods. In this paper, we propose a smartwatch-based system for detecting adherence to prescription medication based the identification of several motions using the built-in tri-axial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.
{"title":"A smartwatch-based medication adherence system","authors":"H. Kalantarian, N. Alshurafa, Ebrahim Nemati, Tuan Le, M. Sarrafzadeh","doi":"10.1109/BSN.2015.7299348","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299348","url":null,"abstract":"Poor adherence to prescription medication can compromise treatment effectiveness and cost the billions of dollars in unnecessary health care expenses. Though various interventions have been proposed for estimating adherence rates, few have been shown to be effective. Digital systems are capable of estimating adherence without extensive user involvement and can potentially provide higher accuracy with lower user burden than manual methods. In this paper, we propose a smartwatch-based system for detecting adherence to prescription medication based the identification of several motions using the built-in tri-axial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121823112","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299361
Shumei Zhang, P. Mccullagh, Huiyu Zhou, Zhe Wen, Zhengcheng Xu
Three RFID reader based network deployment algorithms (grid-covering, diagonal and mixed) were evaluated in this paper. Experimental results show that the grid-covering method can be used to minimize hardware costs, but it leads to many indeterminate positions. The diagonal method can be used to solve the indeterminate problem, however increases the number of readers, especially in a large tracking field. The mixed algorithm can be used to avoid the indeterminate issue and also has the minimum reader number when deployed in a large space. However, it is not suitable for a small tracking field. An optimal deployment algorithm is selected from these three algorithms according to the environmental conditions and the localization requirement. In addition, an optimal RFID reader network deployment combined with a subarea-mapping algorithm can be used to minimize the hardware costs while improving the fine-grained indoor localization accuracy.
{"title":"RFID network deployment approaches for indoor localisation","authors":"Shumei Zhang, P. Mccullagh, Huiyu Zhou, Zhe Wen, Zhengcheng Xu","doi":"10.1109/BSN.2015.7299361","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299361","url":null,"abstract":"Three RFID reader based network deployment algorithms (grid-covering, diagonal and mixed) were evaluated in this paper. Experimental results show that the grid-covering method can be used to minimize hardware costs, but it leads to many indeterminate positions. The diagonal method can be used to solve the indeterminate problem, however increases the number of readers, especially in a large tracking field. The mixed algorithm can be used to avoid the indeterminate issue and also has the minimum reader number when deployed in a large space. However, it is not suitable for a small tracking field. An optimal deployment algorithm is selected from these three algorithms according to the environmental conditions and the localization requirement. In addition, an optimal RFID reader network deployment combined with a subarea-mapping algorithm can be used to minimize the hardware costs while improving the fine-grained indoor localization accuracy.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372448","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299390
Timm Hormann, Peter Christ, Marc Hesse, U. Rückert
Raising the awareness of being physically active by utilizing wearable body sensors has become a popular research topic. Recent approaches combine physical and physiological information to obtain a precise prediction of a person;s physical activity ratio. However, the error in the determination of physical activity due to invalid physiological values that are resulting from underlying signal disturbances, has so far not been considered. We therefore present a robust measure of activity that fuses accelerometer data, heart rate and other personalized features, and is adaptively responding to missing physiological sensor data. To set up the model, we make use of regression analysis (MARS). Our findings indicate the need for considering signal quality when estimating physical activity. The predictive model shows close agreement (R2 = 0.97) to the reference from indirect calorimetry, even if the physiological information is partly corrupted.
{"title":"Robust estimation of physical activity by adaptively fusing multiple parameters","authors":"Timm Hormann, Peter Christ, Marc Hesse, U. Rückert","doi":"10.1109/BSN.2015.7299390","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299390","url":null,"abstract":"Raising the awareness of being physically active by utilizing wearable body sensors has become a popular research topic. Recent approaches combine physical and physiological information to obtain a precise prediction of a person;s physical activity ratio. However, the error in the determination of physical activity due to invalid physiological values that are resulting from underlying signal disturbances, has so far not been considered. We therefore present a robust measure of activity that fuses accelerometer data, heart rate and other personalized features, and is adaptively responding to missing physiological sensor data. To set up the model, we make use of regression analysis (MARS). Our findings indicate the need for considering signal quality when estimating physical activity. The predictive model shows close agreement (R2 = 0.97) to the reference from indirect calorimetry, even if the physiological information is partly corrupted.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128842015","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299425
Constantinos Gavriel, K. Parker, A. Faisal
In this preliminary study, we investigate the potential use of smartphones as portable heart-monitoring devices that can capture and analyse heart activity in real time. We have developed a smartphone application called “Medical Tricorder” that can exploit smartphone;s inertial sensors and when placed on a subject;s chest, it can efficiently capture the motion patterns caused by the mechanical activity of the heart. Using the measured ballistocardiograph signal (BCG), the application can efficiently extract the heart rate in real time while matching the performance of clinical-grade electrocardiographs (ECG). Although the BCG signal can provide much richer information regarding the mechanical aspects of the human heart, we have developed a method of mapping the chest BCG signal into an ECG signal, which can be made directly available to clinicians for diagnostics. Comparing the estimated ECG signal to empirical data from cardiovascular diseases, may allow detection of heart abnormalities at a very early stage without any medical staff involvement. Our method opens up the potential of turning smartphones into portable healthcare systems which can provide patients and general public an easy access to continuous healthcare monitoring. Additionally, given that our solution is mainly software based, it can be deployed on smartphones around the world with minimal costs.
{"title":"Smartphone as an ultra-low cost medical tricorder for real-time cardiological measurements via ballistocardiography","authors":"Constantinos Gavriel, K. Parker, A. Faisal","doi":"10.1109/BSN.2015.7299425","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299425","url":null,"abstract":"In this preliminary study, we investigate the potential use of smartphones as portable heart-monitoring devices that can capture and analyse heart activity in real time. We have developed a smartphone application called “Medical Tricorder” that can exploit smartphone;s inertial sensors and when placed on a subject;s chest, it can efficiently capture the motion patterns caused by the mechanical activity of the heart. Using the measured ballistocardiograph signal (BCG), the application can efficiently extract the heart rate in real time while matching the performance of clinical-grade electrocardiographs (ECG). Although the BCG signal can provide much richer information regarding the mechanical aspects of the human heart, we have developed a method of mapping the chest BCG signal into an ECG signal, which can be made directly available to clinicians for diagnostics. Comparing the estimated ECG signal to empirical data from cardiovascular diseases, may allow detection of heart abnormalities at a very early stage without any medical staff involvement. Our method opens up the potential of turning smartphones into portable healthcare systems which can provide patients and general public an easy access to continuous healthcare monitoring. Additionally, given that our solution is mainly software based, it can be deployed on smartphones around the world with minimal costs.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127541424","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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