The rapid advancements of mobile technologies promote many applications for public health, such as continuous health monitoring. The inherent mobility of these applications imposes new security and privacy challenges. Since mobile devices usually use public network, such as WiFi, to transfer patient data, patient data is exposed to various security breaches. Moreover, patient data stored on cloud servers are also exposed to malicious attacks. Therefore, it's crucial to encrypt patient data for secure transfer and storage. To address this problem, we present a new access control model for managing patient data. Our approach utilizes a key server for key assignment, which associates a key with each user based on his specific role in medical applications. The doctors, nurses, family members, and insurance companies of a patient can access different sets of patient data from cloud given their keys. Different from existing attribute based encryption, which protects data from inappropriate disclosure for individual files, our design provides a fine-grained access control scheme that protects any specified part of a file. Our role-based access control provides high security, accuracy, and update flexibility for patient data management. Performance evaluations of our solution are stated in the paper.
{"title":"Multi-part file encryption for electronic health records cloud","authors":"X. Hei, Shan Lin","doi":"10.1145/2633651.2637473","DOIUrl":"https://doi.org/10.1145/2633651.2637473","url":null,"abstract":"The rapid advancements of mobile technologies promote many applications for public health, such as continuous health monitoring. The inherent mobility of these applications imposes new security and privacy challenges. Since mobile devices usually use public network, such as WiFi, to transfer patient data, patient data is exposed to various security breaches. Moreover, patient data stored on cloud servers are also exposed to malicious attacks. Therefore, it's crucial to encrypt patient data for secure transfer and storage. To address this problem, we present a new access control model for managing patient data. Our approach utilizes a key server for key assignment, which associates a key with each user based on his specific role in medical applications. The doctors, nurses, family members, and insurance companies of a patient can access different sets of patient data from cloud given their keys. Different from existing attribute based encryption, which protects data from inappropriate disclosure for individual files, our design provides a fine-grained access control scheme that protects any specified part of a file. Our role-based access control provides high security, accuracy, and update flexibility for patient data management. Performance evaluations of our solution are stated in the paper.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121480385","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}
Bhanu Kaushik, M. Brunette, Xinwen Fu, Benyuan Liu
In pursuance of the Millennium Development Goals (MDGs) set by United Nations in 2000, both Community Based Participatory Research (CBPR) and Mobile Health (mHealth) have proved to be a great tool for advancements in patient monitoring, emergency care and community empowerment. Rapid proliferation of mobile telephony in low income, rural and underserved populations in the absence of other information and communication technology media have prompted the interests of researchers in public health sector. Exploiting mobile communication has resulted in formulation of a dependable and effective socio-technical ecosystem for public health. Whereas, involving academic researchers and community partners to collaborate and develop social and computational models, Community Based Participatory Research (CBPR) approach targets building communication, trust and capacity, with the final goal of increasing community participation in the research process. CBPR is a collaborative approach to research which equitably involves all partners in the research process for betterment of the targeted community. In this paper we present a conceptual and implementation architecture for conducting mHealth assisted community-based interventions. The framework allows CBPR partners to customize the system and design interventions around locale, technology, geographic, scale, and nonetheless social and cultural aspects. We also present the design of our planned intervention addressing prenatal monitoring of underserved populations in the Andean regions of Peru.
{"title":"Customizable, scalable and reliable community-based mobile health interventions","authors":"Bhanu Kaushik, M. Brunette, Xinwen Fu, Benyuan Liu","doi":"10.1145/2633651.2633659","DOIUrl":"https://doi.org/10.1145/2633651.2633659","url":null,"abstract":"In pursuance of the Millennium Development Goals (MDGs) set by United Nations in 2000, both Community Based Participatory Research (CBPR) and Mobile Health (mHealth) have proved to be a great tool for advancements in patient monitoring, emergency care and community empowerment. Rapid proliferation of mobile telephony in low income, rural and underserved populations in the absence of other information and communication technology media have prompted the interests of researchers in public health sector. Exploiting mobile communication has resulted in formulation of a dependable and effective socio-technical ecosystem for public health. Whereas, involving academic researchers and community partners to collaborate and develop social and computational models, Community Based Participatory Research (CBPR) approach targets building communication, trust and capacity, with the final goal of increasing community participation in the research process. CBPR is a collaborative approach to research which equitably involves all partners in the research process for betterment of the targeted community. In this paper we present a conceptual and implementation architecture for conducting mHealth assisted community-based interventions. The framework allows CBPR partners to customize the system and design interventions around locale, technology, geographic, scale, and nonetheless social and cultural aspects. We also present the design of our planned intervention addressing prenatal monitoring of underserved populations in the Andean regions of Peru.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127501366","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}
Ryan A. Danas, Douglas T. Lally, Nathaniel W. Miller, J. Synnott, Craig A. Shue, Hassan Ghasemzadeh, K. Venkatasubramanian
A Body Area Network (BAN) consists of a set of sensing devices deployed on a person (user) typically for health monitoring purposes. The BAN continuously monitors various physiological and environmental parameters and typically transfers this information to a base station for processing and storage in a back-end medical cloud. Despite the incredible potential that these systems offer, their utilization is largely limited to lab settings. One of the requirements for adoption in the real-world is the ease of deployment and configuration of such systems for the users. Much work has been done in developing middleware-based solutions that enable easy application development for BANs by abstracting out the details of the devices and sensors. However, none of the current approaches extend this capability to the users of the system. What is required is the ability to provide a means to dynamically add diverse devices into the system without requiring substantial reprogramming of the device and the base station. In this paper, we present BAN-PnP, a communication protocol for enabling devices and the base station (or middleware) to communicate effectively with minimal user involvement. The key idea of the protocol is to allow the devices in the BAN to "teach" the base station about their capabilities. By adding a few extra control messages, we are able to transform a traditional BAN into a plug-n-play BAN that is easy for the usually non-tech-savvy users of such systems to deploy. The performance analysis of the BAN-PnP protocol demonstrates that the protocol enables plug-n-play operation of BANs with an affordable increase in overhead.
{"title":"Designing user-specific plug-n-play into body area networks","authors":"Ryan A. Danas, Douglas T. Lally, Nathaniel W. Miller, J. Synnott, Craig A. Shue, Hassan Ghasemzadeh, K. Venkatasubramanian","doi":"10.1145/2633651.2633655","DOIUrl":"https://doi.org/10.1145/2633651.2633655","url":null,"abstract":"A Body Area Network (BAN) consists of a set of sensing devices deployed on a person (user) typically for health monitoring purposes. The BAN continuously monitors various physiological and environmental parameters and typically transfers this information to a base station for processing and storage in a back-end medical cloud. Despite the incredible potential that these systems offer, their utilization is largely limited to lab settings. One of the requirements for adoption in the real-world is the ease of deployment and configuration of such systems for the users. Much work has been done in developing middleware-based solutions that enable easy application development for BANs by abstracting out the details of the devices and sensors. However, none of the current approaches extend this capability to the users of the system. What is required is the ability to provide a means to dynamically add diverse devices into the system without requiring substantial reprogramming of the device and the base station. In this paper, we present BAN-PnP, a communication protocol for enabling devices and the base station (or middleware) to communicate effectively with minimal user involvement. The key idea of the protocol is to allow the devices in the BAN to \"teach\" the base station about their capabilities. By adding a few extra control messages, we are able to transform a traditional BAN into a plug-n-play BAN that is easy for the usually non-tech-savvy users of such systems to deploy. The performance analysis of the BAN-PnP protocol demonstrates that the protocol enables plug-n-play operation of BANs with an affordable increase in overhead.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121772521","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}
Y. Sun, Pei Xue, Jinze Yang, C. Phillips, Xiaodong Xu
This paper proposes a novel routing protocol called Energy Constraint-Aware Routing Protocol (ECAR) for data transmission in Mobile Ad hoc Medical Care Networks (MAMCN). MAMCN is a sophisticated network environment where multiple types of mobile devices are involved, employing different forms of data transmission without a pre-defined infrastructure. Besides common data, images and "big data" are also significant contributors to the hop-by-hop transmissions. Given the real-time energy status of every node in MAMCN, the route selection scheme proposed in ECAR not only treats the application data differently, but also involves a distributed image compression mechanism as part of the overall design goal, that is prolonging whole network's lifetime. Simulation results show that ECAR out performs other routing protocols greatly in the challenging MAMCN environment.
{"title":"Energy constraint-aware routing protocol for data transmission in ad hoc medical care networks","authors":"Y. Sun, Pei Xue, Jinze Yang, C. Phillips, Xiaodong Xu","doi":"10.1145/2633651.2633654","DOIUrl":"https://doi.org/10.1145/2633651.2633654","url":null,"abstract":"This paper proposes a novel routing protocol called Energy Constraint-Aware Routing Protocol (ECAR) for data transmission in Mobile Ad hoc Medical Care Networks (MAMCN). MAMCN is a sophisticated network environment where multiple types of mobile devices are involved, employing different forms of data transmission without a pre-defined infrastructure. Besides common data, images and \"big data\" are also significant contributors to the hop-by-hop transmissions. Given the real-time energy status of every node in MAMCN, the route selection scheme proposed in ECAR not only treats the application data differently, but also involves a distributed image compression mechanism as part of the overall design goal, that is prolonging whole network's lifetime. Simulation results show that ECAR out performs other routing protocols greatly in the challenging MAMCN environment.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794239","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}
Prescription medication abuse is a major healthcare problem and can lead to addiction syndrome, higher healthcare cost, and serious harm to patients. Mobile health can play a major role in addressing prescription medication abuse. This is due to the ability to (a) monitor patient's health conditions anywhere anytime, (b) monitor patient's medication consumption, and (c) connect with healthcare professionals and utilize suitable interventions in time. More specifically, medication behavior can be monitored using smart medication systems, specialized wearable sensors or mobile devices with patient-entered consumption data. This data can then be analyzed for certain patterns to detect medication abuse. The goal is to design and develop an advance warning system based on the patterns of medication use to alert healthcare professionals and/or family members. Such system will utilize additional contextual knowledge of patient's condition and past history, current use, and information on abuse and addictive potential of medications. In this paper, we present medication related challenges and a preliminary design of a system to monitor and analyze the patterns of medication use, and utilize an analytical model for performance evaluation. The known patterns are utilized to estimate probability of near-future addiction. Our results show that medication adherence can be estimated and probabilities of multi-dosing and super adherence (>100% medication adherence) can be computed based on thresholds supplied by healthcare professionals. The work applies to m-health analytics and decision support systems.
{"title":"Mobile health: medication abuse and addiction","authors":"U. Varshney","doi":"10.1145/2633651.2633656","DOIUrl":"https://doi.org/10.1145/2633651.2633656","url":null,"abstract":"Prescription medication abuse is a major healthcare problem and can lead to addiction syndrome, higher healthcare cost, and serious harm to patients. Mobile health can play a major role in addressing prescription medication abuse. This is due to the ability to (a) monitor patient's health conditions anywhere anytime, (b) monitor patient's medication consumption, and (c) connect with healthcare professionals and utilize suitable interventions in time. More specifically, medication behavior can be monitored using smart medication systems, specialized wearable sensors or mobile devices with patient-entered consumption data. This data can then be analyzed for certain patterns to detect medication abuse. The goal is to design and develop an advance warning system based on the patterns of medication use to alert healthcare professionals and/or family members. Such system will utilize additional contextual knowledge of patient's condition and past history, current use, and information on abuse and addictive potential of medications. In this paper, we present medication related challenges and a preliminary design of a system to monitor and analyze the patterns of medication use, and utilize an analytical model for performance evaluation. The known patterns are utilized to estimate probability of near-future addiction. Our results show that medication adherence can be estimated and probabilities of multi-dosing and super adherence (>100% medication adherence) can be computed based on thresholds supplied by healthcare professionals. The work applies to m-health analytics and decision support systems.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117206594","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}
A. Visvanathan, Rohan Banerjee, A. Choudhury, Aniruddha Sinha, Shaswati Kundu
In this paper, we propose a methodology to estimate the range of human blood pressure (BP) using Photoplethysmography (PPG). 12 time domain features and 7 frequency domain features are pointed out and extracted from the PPG signal. A feature selection algorithm based on Maximal Information Coefficient (MIC) is presented to reduce the dimensionality of the feature set to effective ones, thereby cutting down resource requirements. Support Vector Machine (SVM) is used to classify the BP values into separate bins. The proposed methodology is validated and tested on a standard benchmark clean dataset as well as phone captured noisy dataset to justify its robustness and efficiency. Apart from a commending performance improvement, BP estimation is achieved with minimal features and processing, making the algorithm light weight for porting on smart phones.
{"title":"Smart phone based blood pressure indicator","authors":"A. Visvanathan, Rohan Banerjee, A. Choudhury, Aniruddha Sinha, Shaswati Kundu","doi":"10.1145/2633651.2633657","DOIUrl":"https://doi.org/10.1145/2633651.2633657","url":null,"abstract":"In this paper, we propose a methodology to estimate the range of human blood pressure (BP) using Photoplethysmography (PPG). 12 time domain features and 7 frequency domain features are pointed out and extracted from the PPG signal. A feature selection algorithm based on Maximal Information Coefficient (MIC) is presented to reduce the dimensionality of the feature set to effective ones, thereby cutting down resource requirements. Support Vector Machine (SVM) is used to classify the BP values into separate bins. The proposed methodology is validated and tested on a standard benchmark clean dataset as well as phone captured noisy dataset to justify its robustness and efficiency. Apart from a commending performance improvement, BP estimation is achieved with minimal features and processing, making the algorithm light weight for porting on smart phones.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129297066","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}
Terrell R. Bennett, Claudio Savaglio, David Lu, Hunter Massey, Xianan Wang, Jian Wu, R. Jafari
Wearable computing devices and body sensor networks (BSNs) are becoming more prevalent. Collecting the data necessary to develop the new concepts for these systems can be difficult. We present the MotionSynthesis Toolset (MoST) to alleviate some of the difficulties in data collection and algorithm development. This toolset allows researchers to generate a sequence of movements (i.e. a diary), synthesize a data stream using real sensor data, visualize, and validate the sequence of movements and data with video and waveforms.
{"title":"MotionSynthesis toolset (MoST): a toolset for human motion data synthesis and validation","authors":"Terrell R. Bennett, Claudio Savaglio, David Lu, Hunter Massey, Xianan Wang, Jian Wu, R. Jafari","doi":"10.1145/2633651.2637472","DOIUrl":"https://doi.org/10.1145/2633651.2637472","url":null,"abstract":"Wearable computing devices and body sensor networks (BSNs) are becoming more prevalent. Collecting the data necessary to develop the new concepts for these systems can be difficult. We present the MotionSynthesis Toolset (MoST) to alleviate some of the difficulties in data collection and algorithm development. This toolset allows researchers to generate a sequence of movements (i.e. a diary), synthesize a data stream using real sensor data, visualize, and validate the sequence of movements and data with video and waveforms.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159882","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}
In recent years, with the explosive adoption of smart phone devices, mobile health and fitness applications have been increasingly used by healthcare practitioners and the general public to manage electronic health records, chronic medical conditions, dietary references etc. Despite the rapid growth in the number of mobile and fitness applications on various platforms, very little work has been done to quantitatively and qualitatively assess these applications to guide users in the selection process. Automatic categorization of mobile health and fitness applications is the first step in this direction. In this paper, we report results from crawling 1,430 Android and 62,286 iOS apps in Nov. 2013. Among them, 1,399 apps were manually classified to one or multiple categories out of a total of 11 categories. Text mining tools were applied to the description section of the apps for keyword extraction, feature selection and automatic categorization. The classifiers we experimented with have comparable performance with Linear SVC achieving the highest precision, recall and f1 scores of 0.89, 0.79 and 0.88, respectively.
{"title":"Toward automated categorization of mobile health and fitness applications","authors":"Qiang Xu, George Ibrahim, Rong Zheng, N. Archer","doi":"10.1145/2633651.2633658","DOIUrl":"https://doi.org/10.1145/2633651.2633658","url":null,"abstract":"In recent years, with the explosive adoption of smart phone devices, mobile health and fitness applications have been increasingly used by healthcare practitioners and the general public to manage electronic health records, chronic medical conditions, dietary references etc. Despite the rapid growth in the number of mobile and fitness applications on various platforms, very little work has been done to quantitatively and qualitatively assess these applications to guide users in the selection process. Automatic categorization of mobile health and fitness applications is the first step in this direction. In this paper, we report results from crawling 1,430 Android and 62,286 iOS apps in Nov. 2013. Among them, 1,399 apps were manually classified to one or multiple categories out of a total of 11 categories. Text mining tools were applied to the description section of the apps for keyword extraction, feature selection and automatic categorization. The classifiers we experimented with have comparable performance with Linear SVC achieving the highest precision, recall and f1 scores of 0.89, 0.79 and 0.88, respectively.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116031520","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}
This paper analyzes the requirements of mobile health applications concerning real-time criteria and describes the current state of real-time capabilities on constrained devices and low-power networks. Based on this analysis we observe that for these applications real-time capabilities are not only required per system, but also for the entire distributed system. Furthermore, we describe which technologies are available for the network stack, the software platform, and the hardware in order to fulfill these requirements. From the requirements on the network stack, following a top-down approach, we derive hardware prerequisites. We then conduct measurements on typical IoT hardware and operating system. We conclude that it is feasible to fulfill the identified prerequisites.
{"title":"On real-time requirements in constrained wireless networks for mobile health","authors":"O. Hahm, Stefan Pfeiffer, J. Schiller","doi":"10.1145/2633651.2637475","DOIUrl":"https://doi.org/10.1145/2633651.2637475","url":null,"abstract":"This paper analyzes the requirements of mobile health applications concerning real-time criteria and describes the current state of real-time capabilities on constrained devices and low-power networks. Based on this analysis we observe that for these applications real-time capabilities are not only required per system, but also for the entire distributed system. Furthermore, we describe which technologies are available for the network stack, the software platform, and the hardware in order to fulfill these requirements. From the requirements on the network stack, following a top-down approach, we derive hardware prerequisites. We then conduct measurements on typical IoT hardware and operating system. We conclude that it is feasible to fulfill the identified prerequisites.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781141","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}
There is an increase rise in the usage of mobile health sensors in wearable devices and smartphones. These embedded systems have tight limits on storage, computation power, network connectivity and battery usage making it important to ensure efficient storage/ communication of sensor readings to centralized node/ server. Frequency Transform or Entropy encoding schemes such as arithmetic or Huffman coding can be used for compression, but they incur high computational cost in some scenarios or are oblivious to the higher level redundancies in signal. To this end, we used the property of periodicity in these naturally occurring signals such as heart rate or gait measurements to design a simple low cost scheme for data compression. First, a modified Chi-square periodogram metric is used to adaptively determine the exact time-varying periodicity of the signal. Next, the time-series signal is folded into Frames of length equal to a pre-determined period value. We have successfully tested the scheme for good compression performance in ECG, motion accelerometer data and Parkinson patients samples, leading to 8-14X compression in large sample sizes (6-8K samples) and 2-3X in small sample sizes (200 samples). The proposed scheme can be used stand-alone or as pre-processing step for existing techniques in literature.
{"title":"Efficient health data compression on mobile devices","authors":"A. Pande, E. Baik, P. Mohapatra","doi":"10.1145/2491148.2493888","DOIUrl":"https://doi.org/10.1145/2491148.2493888","url":null,"abstract":"There is an increase rise in the usage of mobile health sensors in wearable devices and smartphones. These embedded systems have tight limits on storage, computation power, network connectivity and battery usage making it important to ensure efficient storage/ communication of sensor readings to centralized node/ server. Frequency Transform or Entropy encoding schemes such as arithmetic or Huffman coding can be used for compression, but they incur high computational cost in some scenarios or are oblivious to the higher level redundancies in signal. To this end, we used the property of periodicity in these naturally occurring signals such as heart rate or gait measurements to design a simple low cost scheme for data compression. First, a modified Chi-square periodogram metric is used to adaptively determine the exact time-varying periodicity of the signal. Next, the time-series signal is folded into Frames of length equal to a pre-determined period value. We have successfully tested the scheme for good compression performance in ECG, motion accelerometer data and Parkinson patients samples, leading to 8-14X compression in large sample sizes (6-8K samples) and 2-3X in small sample sizes (200 samples). The proposed scheme can be used stand-alone or as pre-processing step for existing techniques in literature.","PeriodicalId":150900,"journal":{"name":"International Workshop on Pervasive Wireless Healthcare","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128654853","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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