Pub Date : 2016-11-21DOI: 10.1109/HealthCom.2016.7749439
M. Shoaib, H. Scholten, P. Havinga, Özlem Durmaz Incel
Smoking is known to be one of the main causes for premature deaths. A reliable smoking detection method can enable applications for an insight into a user's smoking behaviour and for use in smoking cessation programs. However, it is difficult to accurately detect smoking because it can be performed in various postures or in combination with other activities, it is less-repetitive, and it may be confused with other similar activities, such as drinking and eating. In this paper, we propose to use a two-layer hierarchical smoking detection algorithm (HLSDA) that uses a classifier at the first layer, followed by a lazy context-rule-based correction method that utilizes neighbouring segments to improve the detection. We evaluated our algorithm on a dataset of 45 hours collected over a three month period where 11 participants performed 17 hours (230 cigarettes) of smoking while sitting, standing, walking, and in a group conversation. The rest of 28 hours consists of other similar activities, such as eating, and drinking. We show that our algorithm improves recall as well as precision for smoking compared to a single layer classification approach. For smoking activity, we achieve an F-measure of 90-97% in person-dependent evaluations and 83-94% in person-independent evaluations. In most cases, our algorithm corrects up to 50% of the misclassified smoking segments. Our algorithm also improves the detection of eating and drinking in a similar way. We make our dataset and data logger publicly available for the reproducibility of our work.
{"title":"A hierarchical lazy smoking detection algorithm using smartwatch sensors","authors":"M. Shoaib, H. Scholten, P. Havinga, Özlem Durmaz Incel","doi":"10.1109/HealthCom.2016.7749439","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749439","url":null,"abstract":"Smoking is known to be one of the main causes for premature deaths. A reliable smoking detection method can enable applications for an insight into a user's smoking behaviour and for use in smoking cessation programs. However, it is difficult to accurately detect smoking because it can be performed in various postures or in combination with other activities, it is less-repetitive, and it may be confused with other similar activities, such as drinking and eating. In this paper, we propose to use a two-layer hierarchical smoking detection algorithm (HLSDA) that uses a classifier at the first layer, followed by a lazy context-rule-based correction method that utilizes neighbouring segments to improve the detection. We evaluated our algorithm on a dataset of 45 hours collected over a three month period where 11 participants performed 17 hours (230 cigarettes) of smoking while sitting, standing, walking, and in a group conversation. The rest of 28 hours consists of other similar activities, such as eating, and drinking. We show that our algorithm improves recall as well as precision for smoking compared to a single layer classification approach. For smoking activity, we achieve an F-measure of 90-97% in person-dependent evaluations and 83-94% in person-independent evaluations. In most cases, our algorithm corrects up to 50% of the misclassified smoking segments. Our algorithm also improves the detection of eating and drinking in a similar way. We make our dataset and data logger publicly available for the reproducibility of our work.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"84 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699338","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 : 2016-11-21DOI: 10.1109/HealthCom.2016.7749502
Drishty Sobnath, N. Philip, R. Kayyali, S. Nabhani-Gebara, B. Pierscionek, Andreas Raptopoulos
This paper presents the design and evaluation of a COPD mobile application which forms part of the WELCOME project (Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities). A first prototype of this application has been implemented and is currently being evaluated with patients, human computer interaction experts and healthcare professionals in the UK and the Netherlands. The application allows COPD patients suffering also from different comorbidities to self-manage their disease by taking regular measurement, fill questionnaires requested by their healthcare professionals and follow different programs remotely. The usability and acceptability of the system by COPD patients in the UK are discussed in this paper.
{"title":"Mobile self-management application for COPD patients with comorbidities: A usability study","authors":"Drishty Sobnath, N. Philip, R. Kayyali, S. Nabhani-Gebara, B. Pierscionek, Andreas Raptopoulos","doi":"10.1109/HealthCom.2016.7749502","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749502","url":null,"abstract":"This paper presents the design and evaluation of a COPD mobile application which forms part of the WELCOME project (Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities). A first prototype of this application has been implemented and is currently being evaluated with patients, human computer interaction experts and healthcare professionals in the UK and the Netherlands. The application allows COPD patients suffering also from different comorbidities to self-manage their disease by taking regular measurement, fill questionnaires requested by their healthcare professionals and follow different programs remotely. The usability and acceptability of the system by COPD patients in the UK are discussed in this paper.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126539473","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 : 2016-11-21DOI: 10.1109/HealthCom.2016.7749485
I. Rehman, N. Philip, Moustafa M. Nasralla
Small cell technology is expected to be an integral part of future 5G networks in order to meet the increasingly high user demands for traffic volume, frequency efficiency, and energy and cost reductions. Small cell networks can play an important role in enhancing the Quality of Service (QoS) and Quality of Experience (QoE) in m-health applications, and in particular, in medical video streaming. In this paper, we propose a hybrid medical QoE prediction model based on a Fuzzy Inference System (FIS) that correlates the network QoS (NQoS) and application QoS (AQoS) parameters to the QoE. The model is tested on the transmission of medical ultrasound video over small cell technology. The results show that the predicted QoE scores of our proposed model have a high correlation with the subjective scores of medical experts.
{"title":"A hybrid quality evaluation approach based on fuzzy inference system for medical video streaming over small cell technology","authors":"I. Rehman, N. Philip, Moustafa M. Nasralla","doi":"10.1109/HealthCom.2016.7749485","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749485","url":null,"abstract":"Small cell technology is expected to be an integral part of future 5G networks in order to meet the increasingly high user demands for traffic volume, frequency efficiency, and energy and cost reductions. Small cell networks can play an important role in enhancing the Quality of Service (QoS) and Quality of Experience (QoE) in m-health applications, and in particular, in medical video streaming. In this paper, we propose a hybrid medical QoE prediction model based on a Fuzzy Inference System (FIS) that correlates the network QoS (NQoS) and application QoS (AQoS) parameters to the QoE. The model is tested on the transmission of medical ultrasound video over small cell technology. The results show that the predicted QoE scores of our proposed model have a high correlation with the subjective scores of medical experts.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122057240","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 : 2016-11-18DOI: 10.1109/HealthCom.2016.7749449
O. Fukuda, Tatsuma Shimizu, H. Okumura, K. Arai, S. Muraki, K. Fukumoto
To evaluate capacity of the elderly body and the extremities, we developed a measurement system for thigh cross-sectional image. The cross-sectional images were suitable for observing muscle volume. Some previous studies estimated muscular strength based on muscle volume measured the medical images. However there were no studies that used information regarding proportion in human extremities, e. g. muscles, subcutaneous fat and a bone. In this case, the measurements provide the same results even if the proportion is different among two people. We have investigated changes of thigh proportion by age and gender using the cross-sectional ultrasound images. In the survey, 150 ultrasound echo images and the body measurements (height, weight, and so on) were used. We conducted t-test between two groups that have different age, and discussed the causes of their differences. The statistical analysis revealed that the proportion of human thigh was dramatically changed with aging; the shape of thigh outline was deformed and the position of the thigh bone in the cross-sectional image was moved. We considered that the decreasing of the tissue elasticity with aging is responsible for these changes. The proportion analysis can be expected to be a new approach for developing a novel evaluation technique of the elderly body and extremity ability.
{"title":"Analysis of thigh cross-sectional proportion using the portable ultrasound imaging system","authors":"O. Fukuda, Tatsuma Shimizu, H. Okumura, K. Arai, S. Muraki, K. Fukumoto","doi":"10.1109/HealthCom.2016.7749449","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749449","url":null,"abstract":"To evaluate capacity of the elderly body and the extremities, we developed a measurement system for thigh cross-sectional image. The cross-sectional images were suitable for observing muscle volume. Some previous studies estimated muscular strength based on muscle volume measured the medical images. However there were no studies that used information regarding proportion in human extremities, e. g. muscles, subcutaneous fat and a bone. In this case, the measurements provide the same results even if the proportion is different among two people. We have investigated changes of thigh proportion by age and gender using the cross-sectional ultrasound images. In the survey, 150 ultrasound echo images and the body measurements (height, weight, and so on) were used. We conducted t-test between two groups that have different age, and discussed the causes of their differences. The statistical analysis revealed that the proportion of human thigh was dramatically changed with aging; the shape of thigh outline was deformed and the position of the thigh bone in the cross-sectional image was moved. We considered that the decreasing of the tissue elasticity with aging is responsible for these changes. The proportion analysis can be expected to be a new approach for developing a novel evaluation technique of the elderly body and extremity ability.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"324 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121253289","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 : 2016-11-18DOI: 10.1109/HealthCom.2016.7749517
Ting-Wei Lin, Po-Yu Huang, C. Cheng
The goal of this paper is to explore whether the premarket regulatory system of the United States functions ideally in facing the emergence of commercial computer-aided diagnosis (CAD) systems for medical imaging. To outline the commercial CAD systems available in the United States, clinical trials published in PubMed and EMBASE from 2012 to 2016 that investigated the clinical competence of commercial CAD products were obtained, and the product information provided in these studies was searched in the Establishment Registration & Device Listing database, the Releasable 510(k) Premarket Notification database, and the Premarket Approval (PMA) database of the FDA to trace the processes through which such CAD systems entered the healthcare market. A review of current premarket regulatory system for medical devices, and the potential problems that may hinder the social and clinical integration of CAD systems are presented. We noticed expansion of regulatory definition and variation of device classes and product codes among CAD systems with similar clinical uses, which may compromise the efficacy of such regulatory controls. The results suggested ineffectiveness of current premarket regulatory controls for CAD systems in the United States.
{"title":"Computer-aided diagnosis in medical imaging: Review of legal barriers to entry for the commercial systems","authors":"Ting-Wei Lin, Po-Yu Huang, C. Cheng","doi":"10.1109/HealthCom.2016.7749517","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749517","url":null,"abstract":"The goal of this paper is to explore whether the premarket regulatory system of the United States functions ideally in facing the emergence of commercial computer-aided diagnosis (CAD) systems for medical imaging. To outline the commercial CAD systems available in the United States, clinical trials published in PubMed and EMBASE from 2012 to 2016 that investigated the clinical competence of commercial CAD products were obtained, and the product information provided in these studies was searched in the Establishment Registration & Device Listing database, the Releasable 510(k) Premarket Notification database, and the Premarket Approval (PMA) database of the FDA to trace the processes through which such CAD systems entered the healthcare market. A review of current premarket regulatory system for medical devices, and the potential problems that may hinder the social and clinical integration of CAD systems are presented. We noticed expansion of regulatory definition and variation of device classes and product codes among CAD systems with similar clinical uses, which may compromise the efficacy of such regulatory controls. The results suggested ineffectiveness of current premarket regulatory controls for CAD systems in the United States.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019543","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 : 2016-11-18DOI: 10.1109/HealthCom.2016.7749448
S. Tivatansakul, K. Uchimura
Worldwide Health Organization (WHO) has reported that cancer is a major cause of death around the world. The most common cancer in female is breast cancer. Radiologists typically diagnose breast abnormalities and indicate their regions from mammography. However, they might sometimes fail to detect the abnormalities or miss to correctly indicate their regions. To assist them and address the issues, a computer-aided diagnosis (CAD) is generally adopted to confirm the diagnosis results and increase the diagnosis accuracy. This study focused on precise detection of mass boundary from mammography. We adapted and applied a gray-level co-occurrence matrix (GLCM) with statistical features and edge detection which were originally used for color edges extraction. We also improved the method using pre-processing and GLCM iterations with six features: mean, diagonal moment, contrast, energy, inverse difference moment, and variance to distinguish breast mass region from other breast area (background), remove breast tissue, and detect masses. Our method was evaluated with a mini-MIAS database of mammograms (MIAS). The results indicated that the improved method was more suitable for detection of well-defined, circumscribed, ill-defined and other mass types. However, our method needed to improve to detect masses that infiltrated into high dense breast area with unclear boundary such as spiculated masses. This case would be taken into account as our future works.
{"title":"Breast mass detection from mammography using iteration of gray-level co-occurrence matrix","authors":"S. Tivatansakul, K. Uchimura","doi":"10.1109/HealthCom.2016.7749448","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749448","url":null,"abstract":"Worldwide Health Organization (WHO) has reported that cancer is a major cause of death around the world. The most common cancer in female is breast cancer. Radiologists typically diagnose breast abnormalities and indicate their regions from mammography. However, they might sometimes fail to detect the abnormalities or miss to correctly indicate their regions. To assist them and address the issues, a computer-aided diagnosis (CAD) is generally adopted to confirm the diagnosis results and increase the diagnosis accuracy. This study focused on precise detection of mass boundary from mammography. We adapted and applied a gray-level co-occurrence matrix (GLCM) with statistical features and edge detection which were originally used for color edges extraction. We also improved the method using pre-processing and GLCM iterations with six features: mean, diagonal moment, contrast, energy, inverse difference moment, and variance to distinguish breast mass region from other breast area (background), remove breast tissue, and detect masses. Our method was evaluated with a mini-MIAS database of mammograms (MIAS). The results indicated that the improved method was more suitable for detection of well-defined, circumscribed, ill-defined and other mass types. However, our method needed to improve to detect masses that infiltrated into high dense breast area with unclear boundary such as spiculated masses. This case would be taken into account as our future works.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128469629","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 : 2016-11-06DOI: 10.1109/HealthCom.2016.7749457
Qingchao Chen, Bo Tan, K. Chetty, K. Woodbridge
Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method.
{"title":"Activity recognition based on micro-Doppler signature with in-home Wi-Fi","authors":"Qingchao Chen, Bo Tan, K. Chetty, K. Woodbridge","doi":"10.1109/HealthCom.2016.7749457","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749457","url":null,"abstract":"Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126721277","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 : 2016-09-14DOI: 10.1109/HealthCom.2016.7749442
M. Bagot, P. Launay, F. Guidec
There is a growing need for systems that allow to monitor continuously the health condition of patients with chronic diseases, while allowing these patients to live their daily life as usual, at home as well as out of home. Developing such systems is now feasible based on currently available wireless transmission technologies and off-the-shelf wearable sensors, but most of the applications developed so far fall into the quantified-self movement, and can hardly be used for medical monitoring. This paper presents a general architecture for mobile biophysical monitoring, covering all stages of data acquisition, transmission, and processing. This architecture has been designed so as to meet the expectations of the medical field (especially regarding confidentiality and dependability), while remaining open and flexible (i.e., new types of sensors or data processing algorithms can be incorporated as and when needed).
{"title":"A flexible architecture for mobile health monitoring","authors":"M. Bagot, P. Launay, F. Guidec","doi":"10.1109/HealthCom.2016.7749442","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749442","url":null,"abstract":"There is a growing need for systems that allow to monitor continuously the health condition of patients with chronic diseases, while allowing these patients to live their daily life as usual, at home as well as out of home. Developing such systems is now feasible based on currently available wireless transmission technologies and off-the-shelf wearable sensors, but most of the applications developed so far fall into the quantified-self movement, and can hardly be used for medical monitoring. This paper presents a general architecture for mobile biophysical monitoring, covering all stages of data acquisition, transmission, and processing. This architecture has been designed so as to meet the expectations of the medical field (especially regarding confidentiality and dependability), while remaining open and flexible (i.e., new types of sensors or data processing algorithms can be incorporated as and when needed).","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126864303","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 : 2016-09-14DOI: 10.1109/HealthCom.2016.7749506
M. Ali, Hassine Moungla, M. Younis, A. Mehaoua
When sensors of different coexisting wireless body area networks (WBANs) transmit at the same time using the same channel, a co-channel interference is experienced and hence the performance of the involved WBANs may be degraded. In this paper, we exploit the 16 channels available in the 2.4 GHz international band of ZIGBEE, and propose a distributed scheme that avoids interference through predictable channel hopping based on Latin rectangles, namely, CHIM. In the proposed CHIM scheme, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and columns are sensor IDs. Based on the Latin rectangle of the individual WBAN, each sensor is allocated a backup time-slot and a channel to use if it experiences interference such that collisions among different transmissions of coexisting WBANs are minimized. We further present a mathematical analysis that derives the collision probability of each sensor's transmission in the network. In addition, the efficiency of CHIM in terms of transmission delay and energy consumption minimization are validated by simulations.
{"title":"Distributed scheme for interference mitigation of WBANs using predictable channel hopping","authors":"M. Ali, Hassine Moungla, M. Younis, A. Mehaoua","doi":"10.1109/HealthCom.2016.7749506","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749506","url":null,"abstract":"When sensors of different coexisting wireless body area networks (WBANs) transmit at the same time using the same channel, a co-channel interference is experienced and hence the performance of the involved WBANs may be degraded. In this paper, we exploit the 16 channels available in the 2.4 GHz international band of ZIGBEE, and propose a distributed scheme that avoids interference through predictable channel hopping based on Latin rectangles, namely, CHIM. In the proposed CHIM scheme, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and columns are sensor IDs. Based on the Latin rectangle of the individual WBAN, each sensor is allocated a backup time-slot and a channel to use if it experiences interference such that collisions among different transmissions of coexisting WBANs are minimized. We further present a mathematical analysis that derives the collision probability of each sensor's transmission in the network. In addition, the efficiency of CHIM in terms of transmission delay and energy consumption minimization are validated by simulations.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132230446","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749445
I. Nejadgholi, S. Rajan, M. Bolic
This paper presents a novel algorithm for radar-based estimation of vital signs in a noncontact, privacy friendly manner while subjects are in motion. Unlike the traditional methods that merely use the Fourier spectrum of the output of the radar receiver to obtain estimates of breathing and heart rates, the proposed algorithm uses time-frequency approach. From the Time-Frequency Representation (TFR) of the output of a pseudo-random binary Phase Modulated Continuous Wave (PMCW) radar, frequency of the maximum amplitude at every time instant is estimated and a timeseries of dominant frequencies is formed. MUSIC algorithm is then applied to estimate the vital signs from this series. The proposed algorithm is demonstrated using simulated and real data. Simulated data is obtained through modeling the output of a PMCW radar. Real data is obtained by monitoring a walking subject for 10 minutes in a realistic setting with a 24.125 GHz PMCW radar. The vital sign estimates obtained using the proposed method are found to match closely the estimates from wearable devices that were applied to provide the ground truth for breathing and heart rates.
{"title":"Time-frequency based contactless estimation of vital signs of human while walking using PMCW radar","authors":"I. Nejadgholi, S. Rajan, M. Bolic","doi":"10.1109/HealthCom.2016.7749445","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749445","url":null,"abstract":"This paper presents a novel algorithm for radar-based estimation of vital signs in a noncontact, privacy friendly manner while subjects are in motion. Unlike the traditional methods that merely use the Fourier spectrum of the output of the radar receiver to obtain estimates of breathing and heart rates, the proposed algorithm uses time-frequency approach. From the Time-Frequency Representation (TFR) of the output of a pseudo-random binary Phase Modulated Continuous Wave (PMCW) radar, frequency of the maximum amplitude at every time instant is estimated and a timeseries of dominant frequencies is formed. MUSIC algorithm is then applied to estimate the vital signs from this series. The proposed algorithm is demonstrated using simulated and real data. Simulated data is obtained through modeling the output of a PMCW radar. Real data is obtained by monitoring a walking subject for 10 minutes in a realistic setting with a 24.125 GHz PMCW radar. The vital sign estimates obtained using the proposed method are found to match closely the estimates from wearable devices that were applied to provide the ground truth for breathing and heart rates.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126835272","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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