Pub Date : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134045
Junseon Kim, Howon Lee
We here propose geographical proximity based target-group formation algorithm to efficiently distribute advertisement messages of social-commerce services with device-to-device (D2D) communications. The convergence of D2D communications and the social-commerce services is able to invoke synergistic effects because the D2D users can voluntarily relay the messages to their neighbor users to obtain a great deal of discounts for the corresponding products. By using both the angular distances among target-areas and the physical distances between the D2D access point and the target-areas, we make target-groups for efficient D2D advertisement dissemination. Through intensive simulations, we evaluate the performances of our algorithm with respect to the total number of successfully received users, the average number of relay users, and transmission efficiency compared with the conventional algorithm with cell sectorization based target-group formation.
{"title":"Geographical proximity based target-group formation algorithm for D2D advertisement dissemination","authors":"Junseon Kim, Howon Lee","doi":"10.1109/PERCOMW.2015.7134045","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134045","url":null,"abstract":"We here propose geographical proximity based target-group formation algorithm to efficiently distribute advertisement messages of social-commerce services with device-to-device (D2D) communications. The convergence of D2D communications and the social-commerce services is able to invoke synergistic effects because the D2D users can voluntarily relay the messages to their neighbor users to obtain a great deal of discounts for the corresponding products. By using both the angular distances among target-areas and the physical distances between the D2D access point and the target-areas, we make target-groups for efficient D2D advertisement dissemination. Through intensive simulations, we evaluate the performances of our algorithm with respect to the total number of successfully received users, the average number of relay users, and transmission efficiency compared with the conventional algorithm with cell sectorization based target-group formation.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116946677","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134034
Raed M. Salih, L. Lilien
We report on use of Active Privacy Bundles using a Trusted Third Party (APB-TTP) for protecting privacy of users' healthcare data (incl. patients' Electronic Health Records). APB-TTP protects data that are being disseminated among different authorized parties within a healthcare cloud. We are nearing completion of the pilot APB-TTP for healthcare applications, and commencing work on its extension, named Active Privacy Bundles with Multi Agents (APB-MA).
{"title":"Protecting users' privacy in healthcare cloud computing with APB-TTP","authors":"Raed M. Salih, L. Lilien","doi":"10.1109/PERCOMW.2015.7134034","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134034","url":null,"abstract":"We report on use of Active Privacy Bundles using a Trusted Third Party (APB-TTP) for protecting privacy of users' healthcare data (incl. patients' Electronic Health Records). APB-TTP protects data that are being disseminated among different authorized parties within a healthcare cloud. We are nearing completion of the pilot APB-TTP for healthcare applications, and commencing work on its extension, named Active Privacy Bundles with Multi Agents (APB-MA).","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123624975","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134043
Keyi Zhang, Alan Marchiori
As sensors become more affordable and versatile, more and more sensors are deployed in different environments to help people observe their surroundings. However, due to their various physical structures, it is very challenging to have a universal schema to identify, search, and query sensors and sensors' data. Fortunately, there are two main approaches to address some of these problems, namely Semantic Sensor Network (SSN) from W3C and Sensor Web Enablement (SWE) from the Open Geospa-tial Consortium (OSG). Both utilize XML to extend sensors' metadata and let machines understand the semantic meaning of a sensor. However, even though they provide a universal way to describe and deliver high-level sensor information, neither enable the querying of historical data. In this paper, we briefly examine the current semantic sensor web developments, SNN and SWE along with their advantages and challenges. Then we present our extensions to enable querying historical data within the semantic sensor domain that we call QueryML. QueryML can be used to extend the capabilities of either SSN or SWE to support querying historical data.
{"title":"Extending semantic sensor networks with QueryML","authors":"Keyi Zhang, Alan Marchiori","doi":"10.1109/PERCOMW.2015.7134043","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134043","url":null,"abstract":"As sensors become more affordable and versatile, more and more sensors are deployed in different environments to help people observe their surroundings. However, due to their various physical structures, it is very challenging to have a universal schema to identify, search, and query sensors and sensors' data. Fortunately, there are two main approaches to address some of these problems, namely Semantic Sensor Network (SSN) from W3C and Sensor Web Enablement (SWE) from the Open Geospa-tial Consortium (OSG). Both utilize XML to extend sensors' metadata and let machines understand the semantic meaning of a sensor. However, even though they provide a universal way to describe and deliver high-level sensor information, neither enable the querying of historical data. In this paper, we briefly examine the current semantic sensor web developments, SNN and SWE along with their advantages and challenges. Then we present our extensions to enable querying historical data within the semantic sensor domain that we call QueryML. QueryML can be used to extend the capabilities of either SSN or SWE to support querying historical data.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123663132","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134006
Meng Cui, W. Tai, D. O’Sullivan
There has been a significant increase in recent years in the volume and diversity of streams of data, data streams from sensors, data streams arising from the analysis of content or data mining, right through to user generated Twitter streams. There has been a corresponding increase in demand for more real-time analysis of these streams in order to spot significant events and trends of interest to an individual or business. This has resulted in an increased need to achieve efficient temporal reasoning upon the streams. In this paper, we present a novel approach to perform temporal reasoning on real time streams of data using Semantic Web Technologies so that we could derive more valuable information by taking account of the time dimension. Moreover, in order to deal with such high-frequency data, several filter mechanisms have been implemented to, significantly, improve the performance of the reasoning process. In order to illustrate and evaluate the approach, the real-time analysis of Twitter data is taken as a concrete use case for such data streams.
{"title":"Temporal reasoning on Twitter streams using semantic web technologies","authors":"Meng Cui, W. Tai, D. O’Sullivan","doi":"10.1109/PERCOMW.2015.7134006","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134006","url":null,"abstract":"There has been a significant increase in recent years in the volume and diversity of streams of data, data streams from sensors, data streams arising from the analysis of content or data mining, right through to user generated Twitter streams. There has been a corresponding increase in demand for more real-time analysis of these streams in order to spot significant events and trends of interest to an individual or business. This has resulted in an increased need to achieve efficient temporal reasoning upon the streams. In this paper, we present a novel approach to perform temporal reasoning on real time streams of data using Semantic Web Technologies so that we could derive more valuable information by taking account of the time dimension. Moreover, in order to deal with such high-frequency data, several filter mechanisms have been implemented to, significantly, improve the performance of the reasoning process. In order to illustrate and evaluate the approach, the real-time analysis of Twitter data is taken as a concrete use case for such data streams.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124502612","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133989
Matthias Wieland, H. Schwarz, Uwe Breitenbücher, F. Leymann
Workflows are an established IT concept to achieve business goals in a reliable and robust manner. However, the dynamic nature of modern information systems, the upcoming Industry 4.0, and the Internet of Things increase the complexity of modeling robust workflows significantly as various kinds of situations, such as the failure of a production system, have to be considered explicitly. Consequently, modeling workflows in a situation-aware manner is a complex challenge that quickly results in big unmanageable workflow models. To overcome these issues, we present an approach that allows workflows to become situation-aware to automatically adapt their behavior according to the situation they are in. The approach is based on aggregated context information, which has been an important research topic in the last decade to capture information about an environment. We introduce a system that derives high-level situations from lower-level context and sensor information. A situation can be used by different situation-aware workflows to adapt to the current situation in their execution environment. SitOPT enables the detection of situations using different situation-recognition systems, exchange of information about detected situations, optimization of the situation-recognition, and runtime adaption and optimization of situation-aware workflows based on the recognized situations.
{"title":"Towards situation-aware adaptive workflows: SitOPT — A general purpose situation-aware workflow management system","authors":"Matthias Wieland, H. Schwarz, Uwe Breitenbücher, F. Leymann","doi":"10.1109/PERCOMW.2015.7133989","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133989","url":null,"abstract":"Workflows are an established IT concept to achieve business goals in a reliable and robust manner. However, the dynamic nature of modern information systems, the upcoming Industry 4.0, and the Internet of Things increase the complexity of modeling robust workflows significantly as various kinds of situations, such as the failure of a production system, have to be considered explicitly. Consequently, modeling workflows in a situation-aware manner is a complex challenge that quickly results in big unmanageable workflow models. To overcome these issues, we present an approach that allows workflows to become situation-aware to automatically adapt their behavior according to the situation they are in. The approach is based on aggregated context information, which has been an important research topic in the last decade to capture information about an environment. We introduce a system that derives high-level situations from lower-level context and sensor information. A situation can be used by different situation-aware workflows to adapt to the current situation in their execution environment. SitOPT enables the detection of situations using different situation-recognition systems, exchange of information about detected situations, optimization of the situation-recognition, and runtime adaption and optimization of situation-aware workflows based on the recognized situations.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126416677","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134044
Kalyanaraman Shankari, Mogeng Yin, D. Culler, R. Katz
Tracking travel patterns and modes is useful on many levels. Prior efforts to collect this information have been stymied by low accuracies or reliance on supplementary devices. One technique to overcome low accuracies is to use prompted recall, in which the user is prompted to supply the ground truth for automatically generated information. However, prompted recall increases the burden on the user, which could lead to low adoption or high drop out rates. Using techniques from behavioral economics, and prompting directly on the smartphone can reduce user burden, and also increase engagement for ongoing data collection. In this paper, we describe a system that improves accuracy by using behavioral techniques for prompted recall on the smartphone, and aggregates the information to help detect large scale patterns. We also present the evaluation of a prototype implementation that was used to collect data from 44 unpaid volunteers in the San Francisco Bay Area over 3 months and compute their transportation carbon footprint.
{"title":"E-mission: Automated transportation emission calculation using smartphones","authors":"Kalyanaraman Shankari, Mogeng Yin, D. Culler, R. Katz","doi":"10.1109/PERCOMW.2015.7134044","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134044","url":null,"abstract":"Tracking travel patterns and modes is useful on many levels. Prior efforts to collect this information have been stymied by low accuracies or reliance on supplementary devices. One technique to overcome low accuracies is to use prompted recall, in which the user is prompted to supply the ground truth for automatically generated information. However, prompted recall increases the burden on the user, which could lead to low adoption or high drop out rates. Using techniques from behavioral economics, and prompting directly on the smartphone can reduce user burden, and also increase engagement for ongoing data collection. In this paper, we describe a system that improves accuracy by using behavioral techniques for prompted recall on the smartphone, and aggregates the information to help detect large scale patterns. We also present the evaluation of a prototype implementation that was used to collect data from 44 unpaid volunteers in the San Francisco Bay Area over 3 months and compute their transportation carbon footprint.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127133242","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134042
Shelby E. Kilgore, C. Graves
This paper presents an algorithm for processing Connect-the-Dots puzzles. In particular, the use of Optical Character Recognition (OCR) and other image processing algorithms to process pre-existing Connect-the-Dots puzzles is explored. An algorithm was developed which utilizes Matlab and C# to locate and identify the numbers in the puzzles. To test the accuracy of the algorithm an experiment was conducted using 20 hand selected puzzles from an online source. The function of the algorithm was evaluated by visually capturing the make-up of the puzzles and comparing them to the results generated by the algorithm. Results show that the algorithm has promise of great accuracy with the implementation of small improvements. The proposed research will aid in the development of an application that will provide educational benefits to children in an emergent technological world.
{"title":"Processing pre-existing connect-the-dots puzzles for educational repurposing applications","authors":"Shelby E. Kilgore, C. Graves","doi":"10.1109/PERCOMW.2015.7134042","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134042","url":null,"abstract":"This paper presents an algorithm for processing Connect-the-Dots puzzles. In particular, the use of Optical Character Recognition (OCR) and other image processing algorithms to process pre-existing Connect-the-Dots puzzles is explored. An algorithm was developed which utilizes Matlab and C# to locate and identify the numbers in the puzzles. To test the accuracy of the algorithm an experiment was conducted using 20 hand selected puzzles from an online source. The function of the algorithm was evaluated by visually capturing the make-up of the puzzles and comparing them to the results generated by the algorithm. Results show that the algorithm has promise of great accuracy with the implementation of small improvements. The proposed research will aid in the development of an application that will provide educational benefits to children in an emergent technological world.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122315805","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134079
Charith D. Chitraranjan, A. Perera, A. Denton
Tracking vehicles has many applications, especially in traffic engineering, including estimation of travel time/speed, traffic density, and Origin-Destination matrices. In this paper, we propose local alignment of mobile phone signal strength measurements to track the movement of vehicles, and demonstrate its application to travel-time estimation for a road segment. We use local alignment instead of the traditionally used global alignment to allow for vehicles changing roads. More specifically, we use local dynamic time warping (LDTW) to align the signal strength trace of a phone carried in a vehicle, to a reference trace that we had collected for the relevant road segment. The signal strength trace from a mobile phone includes the strength of the signals received from the serving cell and six neighbor cells that form a multivariate time series. We perform the alignments on these multi-dimensional time series as they provide better location specificity than the univariate time series of the strongest cell, used in existing alignment-based methods. Experiments on drive test data show that our LDTW-based algorithm yields a lower positioning error with respect to ground truth (GPS traces), than comparison methods. Application of LDTW on real world call traces, made available to us by a mobile service provider, produced travel-time estimates with an average error of 11% and significant correlation with respect to travel-times computed through manual number plate recognition of vehicles.
{"title":"Tracking vehicle trajectories by local dynamic time warping of mobile phone signal strengths and its potential in travel-time estimation","authors":"Charith D. Chitraranjan, A. Perera, A. Denton","doi":"10.1109/PERCOMW.2015.7134079","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134079","url":null,"abstract":"Tracking vehicles has many applications, especially in traffic engineering, including estimation of travel time/speed, traffic density, and Origin-Destination matrices. In this paper, we propose local alignment of mobile phone signal strength measurements to track the movement of vehicles, and demonstrate its application to travel-time estimation for a road segment. We use local alignment instead of the traditionally used global alignment to allow for vehicles changing roads. More specifically, we use local dynamic time warping (LDTW) to align the signal strength trace of a phone carried in a vehicle, to a reference trace that we had collected for the relevant road segment. The signal strength trace from a mobile phone includes the strength of the signals received from the serving cell and six neighbor cells that form a multivariate time series. We perform the alignments on these multi-dimensional time series as they provide better location specificity than the univariate time series of the strongest cell, used in existing alignment-based methods. Experiments on drive test data show that our LDTW-based algorithm yields a lower positioning error with respect to ground truth (GPS traces), than comparison methods. Application of LDTW on real world call traces, made available to us by a mobile service provider, produced travel-time estimates with an average error of 11% and significant correlation with respect to travel-times computed through manual number plate recognition of vehicles.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134580060","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134018
Christoph Rauterberg, Xiaoming Fu
Various approaches exist to detect gestures and movements via smartphones. Most of them, however, require that the smartphone is carried on-body. The abscence of reliable ad-hoc on-line gesture detection from environmental sources inspired this project for on-line hand gesture detection on a smartphone using only WiFi RSSI. We highlight our line of work and explain problems at hand to provide information for possible future work. We will furthermore introduce wifiJedi, a smartphone application, that is able to detect movement in front of the smartphone by reading the WiFi RSSI and use this information to control a Slideshow.
{"title":"Demo abstract: Use the force, Luke: Implementation of RF-based gesture interaction on an android phone","authors":"Christoph Rauterberg, Xiaoming Fu","doi":"10.1109/PERCOMW.2015.7134018","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134018","url":null,"abstract":"Various approaches exist to detect gestures and movements via smartphones. Most of them, however, require that the smartphone is carried on-body. The abscence of reliable ad-hoc on-line gesture detection from environmental sources inspired this project for on-line hand gesture detection on a smartphone using only WiFi RSSI. We highlight our line of work and explain problems at hand to provide information for possible future work. We will furthermore introduce wifiJedi, a smartphone application, that is able to detect movement in front of the smartphone by reading the WiFi RSSI and use this information to control a Slideshow.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133412835","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133994
Ramin Fallahzadeh, Mahdi Pedram, Ramyar Saeedi, Bahman Sadeghi, Michael K. Ong, Hassan Ghasemzadeh
Leg swelling produced by retention of fluid in leg tissues is known as peripheral edema, which is regarded as a symptom for various systematic diseases such as heart or kidney failure. In current clinical practice, edema is manually assessed by clinical experts. Such an assessment can often be inaccurate and unreliable especially if it is made by different operators at different times. Despite the importance of monitoring edema for the purpose of evaluating the course of disease or the effect of treatment, quantifying peripheral edema in a continuous and accurate fashion has remained a challenge. In this paper, we propose a wearable real-time platform (namely, Smart-Cuff), which integrates advanced technologies in sensing, computation, and signal processing and machine learning for continuous and real-time edema monitoring in remote and in-home settings. Given that peripheral edema is highly dependent on various contextual attributes such as body posture, we present an activity-sensitive approach to discard erroneous or contextually invalid sensor data in order to meet the requirements of both energy efficiency and quality of information. Examination of our hardware prototype demonstrates the effectiveness of the proposed force-sensitive resistor-based edema sensor (with an R2 of 0.97 for our regression model) as well as the activity monitoring mechanism (over 99% accuracy) that provide the means to perform reliable data sanity check on ankle circumference measurements in a continuous manner.
{"title":"Smart-Cuff: A wearable bio-sensing platform with activity-sensitive information quality assessment for monitoring ankle edema","authors":"Ramin Fallahzadeh, Mahdi Pedram, Ramyar Saeedi, Bahman Sadeghi, Michael K. Ong, Hassan Ghasemzadeh","doi":"10.1109/PERCOMW.2015.7133994","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133994","url":null,"abstract":"Leg swelling produced by retention of fluid in leg tissues is known as peripheral edema, which is regarded as a symptom for various systematic diseases such as heart or kidney failure. In current clinical practice, edema is manually assessed by clinical experts. Such an assessment can often be inaccurate and unreliable especially if it is made by different operators at different times. Despite the importance of monitoring edema for the purpose of evaluating the course of disease or the effect of treatment, quantifying peripheral edema in a continuous and accurate fashion has remained a challenge. In this paper, we propose a wearable real-time platform (namely, Smart-Cuff), which integrates advanced technologies in sensing, computation, and signal processing and machine learning for continuous and real-time edema monitoring in remote and in-home settings. Given that peripheral edema is highly dependent on various contextual attributes such as body posture, we present an activity-sensitive approach to discard erroneous or contextually invalid sensor data in order to meet the requirements of both energy efficiency and quality of information. Examination of our hardware prototype demonstrates the effectiveness of the proposed force-sensitive resistor-based edema sensor (with an R2 of 0.97 for our regression model) as well as the activity monitoring mechanism (over 99% accuracy) that provide the means to perform reliable data sanity check on ankle circumference measurements in a continuous manner.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123468065","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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