Pub Date : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134028
Shaosong Li, Shivakant Mishra
Modern smartphones are increasingly equipped with mul-ticore processors. However, current applications are yet to take full advantage of this new architecture, particularly in the area of managing power consumption. This demo demonstrates three new scheduling algorithms that dynamically schedule an optimal number of cores with each core running at an optimal frequency. A unique feature of these scheduling algorithms is that they take into account the tradeoff between power consumption, performance and user experience. They achieve the best tradeoff under the current context. A prototype implementation on a quad-core HTC One smartohone shows that these algorithms result in significant reduction in power consumption while ensuring good performance and user experience. The demo includes several different popular smartphone applications such as video streaming using YouTube, Candy Crush game, audio streaming using Pandora, navigation using Google Maps, and converting video from .avi to .mp4 format. Users can run these applications using one of the three new scheduling algorithms under different tradeoff scenarios and observe the amount of power savings achieved by the new algorithms.
{"title":"Demo abstract: Power aware core scheduling in multicore smartphones","authors":"Shaosong Li, Shivakant Mishra","doi":"10.1109/PERCOMW.2015.7134028","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134028","url":null,"abstract":"Modern smartphones are increasingly equipped with mul-ticore processors. However, current applications are yet to take full advantage of this new architecture, particularly in the area of managing power consumption. This demo demonstrates three new scheduling algorithms that dynamically schedule an optimal number of cores with each core running at an optimal frequency. A unique feature of these scheduling algorithms is that they take into account the tradeoff between power consumption, performance and user experience. They achieve the best tradeoff under the current context. A prototype implementation on a quad-core HTC One smartohone shows that these algorithms result in significant reduction in power consumption while ensuring good performance and user experience. The demo includes several different popular smartphone applications such as video streaming using YouTube, Candy Crush game, audio streaming using Pandora, navigation using Google Maps, and converting video from .avi to .mp4 format. Users can run these applications using one of the three new scheduling algorithms under different tradeoff scenarios and observe the amount of power savings achieved by the new algorithms.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"39 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":"124087903","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.2015.7134019
Bozidara Cvetkovic, Vito Janko, M. Luštrek
This paper presents a novel method for activity recognition and estimation of human energy expenditure with a smartphone and an optional heart-rate monitor. The method detects the presence of the devices, normalizes the orientation of the phone, detects its location on the body, and uses location-specific models to recognize the activity and estimate the energy expenditure. The normalization of the orientation and the detection of the location significantly improve the accuracy; the estimated energy expenditure is more accurate than that provided by a state-of-the-art dedicated consumer device.
{"title":"Demo abstract: Activity recognition and human energy expenditure estimation with a smartphone","authors":"Bozidara Cvetkovic, Vito Janko, M. Luštrek","doi":"10.1109/PERCOMW.2015.7134019","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134019","url":null,"abstract":"This paper presents a novel method for activity recognition and estimation of human energy expenditure with a smartphone and an optional heart-rate monitor. The method detects the presence of the devices, normalizes the orientation of the phone, detects its location on the body, and uses location-specific models to recognize the activity and estimate the energy expenditure. The normalization of the orientation and the detection of the location significantly improve the accuracy; the estimated energy expenditure is more accurate than that provided by a state-of-the-art dedicated consumer device.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"4 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":"129240834","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.2015.7133995
Dimitrios Tomaras, Ioannis Boutsis, V. Kalogeraki
Travel time estimation is a strategically important service in urban environments for personalized and eco-friendly route planning optimization, congestion avoidance, ridesharing and taxi dispatching. However, storing and retrieving traffic data in specific spatiotemporal regions is not an easy task as the data generated by these systems are typically very large and dynamic. In this paper we propose an efficient and scalable solution for real-time travel time estimation of trajectories. In our system buses are used as speed probes to obtain real-time traffic data information and spatio-temporal trajectories are stored in a dynamic indexing system optimized for efficiently retrieving spatiotemporal data in real-time. Our experimental evaluation illustrates the efficiency and scalability of our approach.
{"title":"Travel time estimation in real-time using buses as speed probes","authors":"Dimitrios Tomaras, Ioannis Boutsis, V. Kalogeraki","doi":"10.1109/PERCOMW.2015.7133995","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133995","url":null,"abstract":"Travel time estimation is a strategically important service in urban environments for personalized and eco-friendly route planning optimization, congestion avoidance, ridesharing and taxi dispatching. However, storing and retrieving traffic data in specific spatiotemporal regions is not an easy task as the data generated by these systems are typically very large and dynamic. In this paper we propose an efficient and scalable solution for real-time travel time estimation of trajectories. In our system buses are used as speed probes to obtain real-time traffic data information and spatio-temporal trajectories are stored in a dynamic indexing system optimized for efficiently retrieving spatiotemporal data in real-time. Our experimental evaluation illustrates the efficiency and scalability of our approach.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"53 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":"129405162","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.2015.7134103
Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, R. Balan, Youngki Lee
We explore the use of gesture recognition on a wrist-worn smartwatch as an enabler of an automated eating activity (and diet monitoring) system. We show, using small-scale user studies, how it is possible to use the accelerometer and gyroscope data from a smartwatch to accurately separate eating episodes from similar non-eating activities, and to additionally identify the mode of eating (i.e., using a spoon, bare hands or chopsticks). Additionally, we investigate the likelihood of automatically triggering the smartwatch's camera to capture clear images of the food being consumed, for possible offline analysis to identify what (and how much) the user is eating. Our results show both the promise and challenges of this vision: while opportune moments for capturing such useful images almost always exist in an eating episode, significant further work is needed to both (a) correctly identify the appropriate instant when the camera should be triggered and (b) reliably identify the type of food via automated analyses of such images.
{"title":"The case for smartwatch-based diet monitoring","authors":"Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, R. Balan, Youngki Lee","doi":"10.1109/PERCOMW.2015.7134103","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134103","url":null,"abstract":"We explore the use of gesture recognition on a wrist-worn smartwatch as an enabler of an automated eating activity (and diet monitoring) system. We show, using small-scale user studies, how it is possible to use the accelerometer and gyroscope data from a smartwatch to accurately separate eating episodes from similar non-eating activities, and to additionally identify the mode of eating (i.e., using a spoon, bare hands or chopsticks). Additionally, we investigate the likelihood of automatically triggering the smartwatch's camera to capture clear images of the food being consumed, for possible offline analysis to identify what (and how much) the user is eating. Our results show both the promise and challenges of this vision: while opportune moments for capturing such useful images almost always exist in an eating episode, significant further work is needed to both (a) correctly identify the appropriate instant when the camera should be triggered and (b) reliably identify the type of food via automated analyses of such images.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"13 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":"116196518","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.2015.7133991
Juan Ye, Graeme Stevenson, S. Dobson
Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper explores generic solutions towards detecting missing data on event-driven sensors using both temporal correlation and time series analysis. The solutions are evaluated on a real-world dataset and achieve promising results with accuracy around 80%.
{"title":"Using temporal correlation and time series to detect missing activity-driven sensor events","authors":"Juan Ye, Graeme Stevenson, S. Dobson","doi":"10.1109/PERCOMW.2015.7133991","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133991","url":null,"abstract":"Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper explores generic solutions towards detecting missing data on event-driven sensors using both temporal correlation and time series analysis. The solutions are evaluated on a real-world dataset and achieve promising results with accuracy around 80%.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"292 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":"133291791","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.2015.7134076
Takumi Satoh, Akihito Hiromori, H. Yamaguchi, T. Higashino
In this paper, we design and develop a ground surface condition recognition algorithm using shoe-mounted inertial sensors. We use a pair of small sensor boxes mounted on feet with accelerometers and gyro sensors inside and detect walking steps using them. Firstly, we detect stationary stance phase using accelerometers and gyro sensors. Then, based on this information, we estimate the “Angle of Inclination” (AoI) and stability of the ground. Moreover, we estimate whether the road surface is flat or not(unstable or unpaved (covered by gravel rubble or dirt) otherwise) based on variance of AoI. In addition, as for small undulation of surface due to dips, humps and bumps, which is hard to recognize only by a few samplings, we rely on continuous sensing data aggregated spatially from multiple users based on dead-reckoning techniques. We have developed a prototype of the proposed method. In the experiments, we show that our method cannot estimate not only walking steps correctly and but also AoIs of roads in rough trends.
{"title":"A novel estimation method of road condition for pedestrian navigation","authors":"Takumi Satoh, Akihito Hiromori, H. Yamaguchi, T. Higashino","doi":"10.1109/PERCOMW.2015.7134076","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134076","url":null,"abstract":"In this paper, we design and develop a ground surface condition recognition algorithm using shoe-mounted inertial sensors. We use a pair of small sensor boxes mounted on feet with accelerometers and gyro sensors inside and detect walking steps using them. Firstly, we detect stationary stance phase using accelerometers and gyro sensors. Then, based on this information, we estimate the “Angle of Inclination” (AoI) and stability of the ground. Moreover, we estimate whether the road surface is flat or not(unstable or unpaved (covered by gravel rubble or dirt) otherwise) based on variance of AoI. In addition, as for small undulation of surface due to dips, humps and bumps, which is hard to recognize only by a few samplings, we rely on continuous sensing data aggregated spatially from multiple users based on dead-reckoning techniques. We have developed a prototype of the proposed method. In the experiments, we show that our method cannot estimate not only walking steps correctly and but also AoIs of roads in rough trends.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"28 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":"133890405","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.2015.7134009
B. Moreno, V. Times, S. Matwin
With the increasing popularity of Location-based Social Networks (LBSNs), users have shared information about places they have visited, creating a link between the real world (their movements on the globe) and the virtual world (what they express about these movements on the LBSNs). In this article, we propose the SiST model, which contains information captured from different dimensions (Social, Spatial and Temporal). The proposed model is a graph that links two users, as long as both of them are friends and have published that they were at the same place within a predefined time interval. In addition to movement patterns that can be extracted using SiST, this model may be used to predict if two users will meet in a short time span by executing a classification algorithm. Performance tests were conducted with SiST networks that were built based on three real LBSN datasets. Results indicated that it is possible to forecast with high accuracy (ranging from 80.50% to 96.32%) whether two people will meet or not using two days of historical data.
{"title":"A spatio-temporal network model to represent and analyze LBSNs","authors":"B. Moreno, V. Times, S. Matwin","doi":"10.1109/PERCOMW.2015.7134009","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134009","url":null,"abstract":"With the increasing popularity of Location-based Social Networks (LBSNs), users have shared information about places they have visited, creating a link between the real world (their movements on the globe) and the virtual world (what they express about these movements on the LBSNs). In this article, we propose the SiST model, which contains information captured from different dimensions (Social, Spatial and Temporal). The proposed model is a graph that links two users, as long as both of them are friends and have published that they were at the same place within a predefined time interval. In addition to movement patterns that can be extracted using SiST, this model may be used to predict if two users will meet in a short time span by executing a classification algorithm. Performance tests were conducted with SiST networks that were built based on three real LBSN datasets. Results indicated that it is possible to forecast with high accuracy (ranging from 80.50% to 96.32%) whether two people will meet or not using two days of historical data.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"7 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":"128816360","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.2015.7134037
Oliver Stecklina
All visions of pervasive computing share the idea of smart, small, and cheap devices that improve our everyday life. Their applications typically fall under sensor-based communication-enabled autonomous and deeply embedded monitor and control systems. But common smart sensors and deeply embedded controllers are also able to do many things that we do not want. In fact, they will be always vulnerable to doing the bidding of attackers, to the detriment of their owners. This work presents a concept of a security architecture for tiny scale devices, which are typically close to the physical elements and featured with very limited resources. The concept describes a compile- and run-time co-design process to bring a tailor-made implementation of well-understood technologies of desktop systems on this type of devices to enforce an adequate security level.
{"title":"A secure isolation of software activities in tiny scale systems","authors":"Oliver Stecklina","doi":"10.1109/PERCOMW.2015.7134037","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134037","url":null,"abstract":"All visions of pervasive computing share the idea of smart, small, and cheap devices that improve our everyday life. Their applications typically fall under sensor-based communication-enabled autonomous and deeply embedded monitor and control systems. But common smart sensors and deeply embedded controllers are also able to do many things that we do not want. In fact, they will be always vulnerable to doing the bidding of attackers, to the detriment of their owners. This work presents a concept of a security architecture for tiny scale devices, which are typically close to the physical elements and featured with very limited resources. The concept describes a compile- and run-time co-design process to bring a tailor-made implementation of well-understood technologies of desktop systems on this type of devices to enforce an adequate security level.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"11 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":"128442434","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.2015.7134074
J. Birjandtalab, Qingxue Zhang, R. Jafari
Miniaturization and form factor reduction in wearable computers leads to enhanced wearability. Power optimization typically translates to form factor reduction, hence of paramount importance. This paper demonstrates power consumption analysis obtained for various operating modes in circuits suitable for wearable computers which are typically equipped with sensors that provide time series data (e.g., acceleration, ECG). Dynamic time warping (DTW) is considered a suitable signal processing technique for wearable computers, particularly due to its lower computational complexity requirement and the robustness to speed variations (acceleration and de-acceleration) in time series data. Wearable computers usually have very low computational performance requirements, which is explored in this work to minimize the system level energy consumption. We provide a comparison among three modes of operations, namely minimum energy operating point (MEOP), minimum voltage operation point (MVOP) and nominal voltage operating point (NVOP) all leveraging sleep transistors when circuits are inactive. The results show that the MVOP, in conjunction with sleep transistors, provides the least energy budget and leads to a reduction in energy consumption compared to the MEO, which is known as a suitable operating mode for ultra-low power circuits.
{"title":"A case study on minimum energy operation for dynamic time warping signal processing in wearable computers","authors":"J. Birjandtalab, Qingxue Zhang, R. Jafari","doi":"10.1109/PERCOMW.2015.7134074","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134074","url":null,"abstract":"Miniaturization and form factor reduction in wearable computers leads to enhanced wearability. Power optimization typically translates to form factor reduction, hence of paramount importance. This paper demonstrates power consumption analysis obtained for various operating modes in circuits suitable for wearable computers which are typically equipped with sensors that provide time series data (e.g., acceleration, ECG). Dynamic time warping (DTW) is considered a suitable signal processing technique for wearable computers, particularly due to its lower computational complexity requirement and the robustness to speed variations (acceleration and de-acceleration) in time series data. Wearable computers usually have very low computational performance requirements, which is explored in this work to minimize the system level energy consumption. We provide a comparison among three modes of operations, namely minimum energy operating point (MEOP), minimum voltage operation point (MVOP) and nominal voltage operating point (NVOP) all leveraging sleep transistors when circuits are inactive. The results show that the MVOP, in conjunction with sleep transistors, provides the least energy budget and leads to a reduction in energy consumption compared to the MEO, which is known as a suitable operating mode for ultra-low power circuits.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"35 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":"116761183","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}
The past years has seen the emergence and fast growing of various wearable devices, such as smart watches, wrist bands, rings, glasses, jewelry, garments, etc. Although wearable devices have a wide usage scope, healthcare is one of the most promising field which is being exploited by a number of big enterprises and research institutes. However, existing healthcare related wearable products usually work individually. There is almost no data transmission or information exchange between different kinds of devices, which prevents data fusion of multiple devices and development of upper layer healthcare applications. In this demo, we present WearableHUB, a mobile phone based open pervasive wearable data fusion platform for personal health management. WearableHUB can transform multimodal data (motion data, physiological indices, etc.) into unified format according to predefined standard. Other than traditional wearable computing system, WearableHUB not only can get preloaded function results from different devices based on module library according to data type and device position, but also can trigger and get dynamic online downloaded APP function base on fusing multimodal data and extracting higher level information.
{"title":"Demo abstract: WearableHUB: An open pervasive wearable data fusion platform for personal health management","authors":"Yiqiang Chen, Wen Gao, Shuangquan Wang, Shuai Jiao","doi":"10.1109/PERCOMW.2015.7134027","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134027","url":null,"abstract":"The past years has seen the emergence and fast growing of various wearable devices, such as smart watches, wrist bands, rings, glasses, jewelry, garments, etc. Although wearable devices have a wide usage scope, healthcare is one of the most promising field which is being exploited by a number of big enterprises and research institutes. However, existing healthcare related wearable products usually work individually. There is almost no data transmission or information exchange between different kinds of devices, which prevents data fusion of multiple devices and development of upper layer healthcare applications. In this demo, we present WearableHUB, a mobile phone based open pervasive wearable data fusion platform for personal health management. WearableHUB can transform multimodal data (motion data, physiological indices, etc.) into unified format according to predefined standard. Other than traditional wearable computing system, WearableHUB not only can get preloaded function results from different devices based on module library according to data type and device position, but also can trigger and get dynamic online downloaded APP function base on fusing multimodal data and extracting higher level information.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"107 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":"122031367","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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