Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456521
T. Sztyler, H. Stuckenschmidt
Human activity recognition using mobile device sensors is an active area of research in pervasive computing. In our work, we aim at implementing activity recognition approaches that are suitable for real life situations. This paper focuses on the problem of recognizing the on-body position of the mobile device which in a real world setting is not known a priori. We present a new real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different positions. Further, we introduce a device localization method that uses random forest classifiers to predict the device position based on acceleration data. We perform the most complete experiment in on-body device location that includes all relevant device positions for the recognition of a variety of different activities. We show that the method outperforms other approaches achieving an F-Measure of 89% across different positions. We also show that the detection of the device position consistently improves the result of activity recognition for common activities.
{"title":"On-body localization of wearable devices: An investigation of position-aware activity recognition","authors":"T. Sztyler, H. Stuckenschmidt","doi":"10.1109/PERCOM.2016.7456521","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456521","url":null,"abstract":"Human activity recognition using mobile device sensors is an active area of research in pervasive computing. In our work, we aim at implementing activity recognition approaches that are suitable for real life situations. This paper focuses on the problem of recognizing the on-body position of the mobile device which in a real world setting is not known a priori. We present a new real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different positions. Further, we introduce a device localization method that uses random forest classifiers to predict the device position based on acceleration data. We perform the most complete experiment in on-body device location that includes all relevant device positions for the recognition of a variety of different activities. We show that the method outperforms other approaches achieving an F-Measure of 89% across different positions. We also show that the detection of the device position consistently improves the result of activity recognition for common activities.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126673472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456520
Bo Zhou, Mathias Sundholm, Jingyuan Cheng, H. Cruz, P. Lukowicz
We present a wearable textile sensor system for monitoring muscle activity, leveraging surface pressure changes between the skin and an elastic sport support band. The sensor is based on an 8×16 element fabric resistive pressure sensing matrix of 1cm spatial resolution, which can be read out with 50fps refresh rate. We evaluate the system by monitoring leg muscles during leg workouts in a gym out of the lab. The sensor covers the lower part of quadriceps of the user. The shape and movement of the two major muscles (vastus lateralis and medialis) are visible from the data during various exercises. The system registers the activity of the user for every second, including which machine he/she is using, walking, relaxing and adjusting the machines; it also counts the repetitions from each set and evaluate the force consistency which is related to the workout quality. 6 people participated in the experiment of overall 24 leg workout sessions. Each session includes cross-trainer warm-up and cool-down, 3 different leg machines, 4 sets on each machine. Plus relaxing, adjusting machines, and walking, we perform activity recognition and quality evaluation through 2-dimensional mapping and the time sequence of the average force. We have reached 81.7% average recognition accuracy on a 2s sliding window basis, 93.3% on an event basis, and 85.6% spotting F1-score. We further demonstrate how to evaluate the workout quality through counting, force pattern variation and consistency.
{"title":"Never skip leg day: A novel wearable approach to monitoring gym leg exercises","authors":"Bo Zhou, Mathias Sundholm, Jingyuan Cheng, H. Cruz, P. Lukowicz","doi":"10.1109/PERCOM.2016.7456520","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456520","url":null,"abstract":"We present a wearable textile sensor system for monitoring muscle activity, leveraging surface pressure changes between the skin and an elastic sport support band. The sensor is based on an 8×16 element fabric resistive pressure sensing matrix of 1cm spatial resolution, which can be read out with 50fps refresh rate. We evaluate the system by monitoring leg muscles during leg workouts in a gym out of the lab. The sensor covers the lower part of quadriceps of the user. The shape and movement of the two major muscles (vastus lateralis and medialis) are visible from the data during various exercises. The system registers the activity of the user for every second, including which machine he/she is using, walking, relaxing and adjusting the machines; it also counts the repetitions from each set and evaluate the force consistency which is related to the workout quality. 6 people participated in the experiment of overall 24 leg workout sessions. Each session includes cross-trainer warm-up and cool-down, 3 different leg machines, 4 sets on each machine. Plus relaxing, adjusting machines, and walking, we perform activity recognition and quality evaluation through 2-dimensional mapping and the time sequence of the average force. We have reached 81.7% average recognition accuracy on a 2s sliding window basis, 93.3% on an event basis, and 85.6% spotting F1-score. We further demonstrate how to evaluate the workout quality through counting, force pattern variation and consistency.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125632252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456503
Zongqing Lu, G. Cao, T. L. Porta
In this paper, we investigate how to network smart-phones for providing communications in disaster recovery. By bridging the gaps among different kinds of wireless networks, we have designed and implemented a system called TeamPhone, which provides smartphones the capabilities of communications in disaster recovery. Specifically, TeamPhone consists of two components: a messaging system and a self-rescue system. The messaging system integrates cellular networking, ad-hoc networking and opportunistic networking seamlessly, and enables communications among rescue workers. The self-rescue system energy-efficiently groups the smartphones of trapped survivor and sends out emergency messages so as to assist rescue operations. We have implemented TeamPhone as a prototype application on the Android platform and deployed it on off-the-shelf smartphones. Experiment results show that TeamPhone can properly fulfill communication requirements and greatly facilitate rescue operations in disaster recovery.
{"title":"Networking smartphones for disaster recovery","authors":"Zongqing Lu, G. Cao, T. L. Porta","doi":"10.1109/PERCOM.2016.7456503","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456503","url":null,"abstract":"In this paper, we investigate how to network smart-phones for providing communications in disaster recovery. By bridging the gaps among different kinds of wireless networks, we have designed and implemented a system called TeamPhone, which provides smartphones the capabilities of communications in disaster recovery. Specifically, TeamPhone consists of two components: a messaging system and a self-rescue system. The messaging system integrates cellular networking, ad-hoc networking and opportunistic networking seamlessly, and enables communications among rescue workers. The self-rescue system energy-efficiently groups the smartphones of trapped survivor and sends out emergency messages so as to assist rescue operations. We have implemented TeamPhone as a prototype application on the Android platform and deployed it on off-the-shelf smartphones. Experiment results show that TeamPhone can properly fulfill communication requirements and greatly facilitate rescue operations in disaster recovery.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132368025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456501
Nguyen Cong Thuong, Vu Nguyen, Flora D. Salim, Dinh Q. Phung
Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture highorder and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.
{"title":"SECC: Simultaneous extraction of context and community from pervasive signals","authors":"Nguyen Cong Thuong, Vu Nguyen, Flora D. Salim, Dinh Q. Phung","doi":"10.1109/PERCOM.2016.7456501","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456501","url":null,"abstract":"Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture highorder and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117001617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456499
C. Mascolo
Summary form only given. With the advent of powerful and inexpensive sensing technology the ability to study human behaviour and activity at large scale and for long periods is becoming a firm reality. Wearables and mobile devices further allow the continuous physical colocation with the users. This reality generates new challenges but also opens the door to potentially innovative ways of understanding our daily lives. In this talk we will discuss our experience in large mobile sensor deployments and in using complex network science for the analysis of mobile sensing data. We will discuss the issues raised by mobile sensing big data in terms of data crowdsourcing, continuous sensing challenges, data analysis, privacy, user feedback. Examples will be drawn from our healthcare, transport, urban planning and organization analytics studies.
{"title":"Studying human behavior at the intersection of mobile sensing and complex networks (Keynote abstract)","authors":"C. Mascolo","doi":"10.1109/PERCOM.2016.7456499","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456499","url":null,"abstract":"Summary form only given. With the advent of powerful and inexpensive sensing technology the ability to study human behaviour and activity at large scale and for long periods is becoming a firm reality. Wearables and mobile devices further allow the continuous physical colocation with the users. This reality generates new challenges but also opens the door to potentially innovative ways of understanding our daily lives. In this talk we will discuss our experience in large mobile sensor deployments and in using complex network science for the analysis of mobile sensing data. We will discuss the issues raised by mobile sensing big data in terms of data crowdsourcing, continuous sensing challenges, data analysis, privacy, user feedback. Examples will be drawn from our healthcare, transport, urban planning and organization analytics studies.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121130716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456509
Milan Jain, Amarjeet Singh, V. Chandan
Residential buildings account for a significant proportion of overall energy consumption across the world. Decentralized room level Air Conditioners (ACs) are a commonplace in developing countries such as India, contributing a major share (34% in India) of the total residential energy consumption. Option to independently control each AC presents a prime opportunity for an energy saving system. Thus, we propose PACMAN to non-intrusively (using only the temperature information) predict AC energy consumption prior to usage and estimate energy consumption post-usage. We discuss various possible applications and use cases of such feedback for the occupants. To empirically validate the performance of PACMAN, we conducted an in-situ study across seven homes in Delhi (India). We collected around 2200 hours of usage data from the different ACs, room types, and thermostat temperatures. We achieved an average accuracy of 85.3% and 83.7% with the best accuracy of 97.0% and 93.3% for the estimation and prediction of AC energy consumption respectively, across all homes. Towards the end, we discuss various outlier scenarios, opening up multiple directions for further research in this domain.
{"title":"Non-intrusive estimation and prediction of residential AC energy consumption","authors":"Milan Jain, Amarjeet Singh, V. Chandan","doi":"10.1109/PERCOM.2016.7456509","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456509","url":null,"abstract":"Residential buildings account for a significant proportion of overall energy consumption across the world. Decentralized room level Air Conditioners (ACs) are a commonplace in developing countries such as India, contributing a major share (34% in India) of the total residential energy consumption. Option to independently control each AC presents a prime opportunity for an energy saving system. Thus, we propose PACMAN to non-intrusively (using only the temperature information) predict AC energy consumption prior to usage and estimate energy consumption post-usage. We discuss various possible applications and use cases of such feedback for the occupants. To empirically validate the performance of PACMAN, we conducted an in-situ study across seven homes in Delhi (India). We collected around 2200 hours of usage data from the different ACs, room types, and thermostat temperatures. We achieved an average accuracy of 85.3% and 83.7% with the best accuracy of 97.0% and 93.3% for the estimation and prediction of AC energy consumption respectively, across all homes. Towards the end, we discuss various outlier scenarios, opening up multiple directions for further research in this domain.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123384684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456505
Landu Jiang, Xi Chen, Wenbo He
A large number of car accidents occur at intersections every year mainly due to drivers' "illegal maneuver" or "unsafe behavior". To promote traffic safety, we present SafeCam, a smartphone-based system that jointly leverages vehicle dynamics and the real-time traffic control information (e.g., traffic signals) to detect and study driver dangerous behaviors at intersections. In particular, SafeCam uses embedded sensors (i.e., inertial sensors) on the phone to generate soft hints tracking different driving conditions while at the same time adopts vision-based algorithms to recognize intersection-related critical driving events including unsafe turns, running stop signs and running red lights. In order to improve the system efficiency, we utilize adaptive color filtering under two lighting conditions (e.g., sunny and cloudy) and deploy the subsampling methods to make a trade off between the detection rate and the processing latency. In the evaluation, we conduct real-road driving experiments involving 15 drivers and 6 vehicles. The experiment results demonstrate that SafeCam is robust and effective in real-road driving environments, and has great potential to alert drivers for their dangerous behaviors at intersections and at the same time help them shape safe driving habits. Our experiments also reveal several interesting findings. 1) On average a driver failed to fully stop at stop signs 3 times in a trip of 3.5 km. 2) 11 out of 15 participants have lane drifting problems when they are making turns in the test. 3) Drivers took longer braking time when they approached a stop sign than a red light.
{"title":"SafeCam: Analyzing intersection-related driver behaviors using multi-sensor smartphones","authors":"Landu Jiang, Xi Chen, Wenbo He","doi":"10.1109/PERCOM.2016.7456505","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456505","url":null,"abstract":"A large number of car accidents occur at intersections every year mainly due to drivers' \"illegal maneuver\" or \"unsafe behavior\". To promote traffic safety, we present SafeCam, a smartphone-based system that jointly leverages vehicle dynamics and the real-time traffic control information (e.g., traffic signals) to detect and study driver dangerous behaviors at intersections. In particular, SafeCam uses embedded sensors (i.e., inertial sensors) on the phone to generate soft hints tracking different driving conditions while at the same time adopts vision-based algorithms to recognize intersection-related critical driving events including unsafe turns, running stop signs and running red lights. In order to improve the system efficiency, we utilize adaptive color filtering under two lighting conditions (e.g., sunny and cloudy) and deploy the subsampling methods to make a trade off between the detection rate and the processing latency. In the evaluation, we conduct real-road driving experiments involving 15 drivers and 6 vehicles. The experiment results demonstrate that SafeCam is robust and effective in real-road driving environments, and has great potential to alert drivers for their dangerous behaviors at intersections and at the same time help them shape safe driving habits. Our experiments also reveal several interesting findings. 1) On average a driver failed to fully stop at stop signs 3 times in a trip of 3.5 km. 2) 11 out of 15 participants have lane drifting problems when they are making turns in the test. 3) Drivers took longer braking time when they approached a stop sign than a red light.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127156612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456507
Hien To, Liyue Fan, Luan Tran, C. Shahabi
Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.
{"title":"Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints","authors":"Hien To, Liyue Fan, Luan Tran, C. Shahabi","doi":"10.1109/PERCOM.2016.7456507","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456507","url":null,"abstract":"Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127333398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456526
Meera Radhakrishnan, S. Eswaran, Archan Misra, D. Chander, K. Dasgupta
We investigate the possibility of using a combination of a smartphone and a smartwatch, carried by a shopper, to get insights into the shopper's behavior inside a retail store. The proposed IRIS framework uses standard locomotive and gestural micro-activities as building blocks to define novel composite features that help classify different facets of a shopper's interaction/experience with individual items, as well as attributes of the overall shopping episode or the store. Besides defining such novel features, IRIS builds a novel segmentation algorithm, which partitions the duration of an entire shopping episode into atomic item-level interactions, by using a combination of feature-based landmarking, change point detection and variable-order HMM-based sequence prediction. Experiments with 50 real-life grocery shopping episodes, collected from 25 shoppers, we show that IRIS can demarcate item-level interactions with an accuracy of approx. 91%, and subsequently characterize item-and-episode level shopper behavior with accuracies of over 90%.
{"title":"IRIS: Tapping wearable sensing to capture in-store retail insights on shoppers","authors":"Meera Radhakrishnan, S. Eswaran, Archan Misra, D. Chander, K. Dasgupta","doi":"10.1109/PERCOM.2016.7456526","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456526","url":null,"abstract":"We investigate the possibility of using a combination of a smartphone and a smartwatch, carried by a shopper, to get insights into the shopper's behavior inside a retail store. The proposed IRIS framework uses standard locomotive and gestural micro-activities as building blocks to define novel composite features that help classify different facets of a shopper's interaction/experience with individual items, as well as attributes of the overall shopping episode or the store. Besides defining such novel features, IRIS builds a novel segmentation algorithm, which partitions the duration of an entire shopping episode into atomic item-level interactions, by using a combination of feature-based landmarking, change point detection and variable-order HMM-based sequence prediction. Experiments with 50 real-life grocery shopping episodes, collected from 25 shoppers, we show that IRIS can demarcate item-level interactions with an accuracy of approx. 91%, and subsequently characterize item-and-episode level shopper behavior with accuracies of over 90%.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129970135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-14DOI: 10.1109/PERCOM.2016.7456518
Rui Liu, Jiannong Cao, S. VanSyckel, Wenyu Gao
Mobile participatory sensing systems allow people with mobile devices to collect, interpret, and share data from their respective environments. One of the main obstacles for long-term participation in such systems is the users' privacy concerns. Due to the nature of these systems, users have to agree to provide some personalized information. Typically, however, people are reluctant to share any information, as it may be sensitive. This is especially the case if the content of the data in question is not completely transparent. In order to increase users' willingness to participate in such systems, we should help users identify which data they can share without violating their personal privacy policies. However, the perception of how sensitive a piece of information is may differ from user to user. In this paper, we propose the human-centric privacy measurement method PriMe, which quantifies privacy risks based on user preferences towards data sharing in participatory sensing systems. Further, we implemented and deployed PriMe in the real world as a user study for evaluation. The study shows that PriMe provides accurate ratings that fit users' individual perceptions of privacy, and is accepted by users as a trustworthy tool.
{"title":"PriMe: Human-centric privacy measurement based on user preferences towards data sharing in mobile participatory sensing systems","authors":"Rui Liu, Jiannong Cao, S. VanSyckel, Wenyu Gao","doi":"10.1109/PERCOM.2016.7456518","DOIUrl":"https://doi.org/10.1109/PERCOM.2016.7456518","url":null,"abstract":"Mobile participatory sensing systems allow people with mobile devices to collect, interpret, and share data from their respective environments. One of the main obstacles for long-term participation in such systems is the users' privacy concerns. Due to the nature of these systems, users have to agree to provide some personalized information. Typically, however, people are reluctant to share any information, as it may be sensitive. This is especially the case if the content of the data in question is not completely transparent. In order to increase users' willingness to participate in such systems, we should help users identify which data they can share without violating their personal privacy policies. However, the perception of how sensitive a piece of information is may differ from user to user. In this paper, we propose the human-centric privacy measurement method PriMe, which quantifies privacy risks based on user preferences towards data sharing in participatory sensing systems. Further, we implemented and deployed PriMe in the real world as a user study for evaluation. The study shows that PriMe provides accurate ratings that fit users' individual perceptions of privacy, and is accepted by users as a trustworthy tool.","PeriodicalId":275797,"journal":{"name":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126028237","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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