Pub Date : 2019-06-06DOI: 10.1109/PERCOMW.2019.8730658
Niek Zuidhof, S. B. Allouch, Oscar Peters, P. Verbeek
For further development of technologies but also for the implementation in real life contexts, it is important to understand users' perspectives on the anticipated use of innovative technologies in an early development phase. In addition, it is also important to get a better understanding of the explanation of this behavior towards technology use in later stages. Although Head Mounted Displays (HMDs) are not really new anymore, the uptake has been slow so far and people showed some extreme reactions. The objective of this study was to analyze the content of YouTube comments on videos of HMDs, in order to get a better understanding of relevant factors in this early phase of potential acceptance of HMDs. We analyzed 379 YouTube comments on HMDs using content analysis. Comments were divided into three groups: HMD, video, and miscellaneous. Comments about HMDs $mathrm{n}=24$ were further analyzed. Most of the commenters showed a positive attitude to HMDs. Within the positive attitude, the most expressed themes were comments about the type of use (gaming), positive evaluations (emotions, coolness) and perceived need for an HMD. Within the negative attitudes, negative evaluations (judgments, emotions) were showed most and negative comparisons to other products were made. In neutral attitudes, the main theme was the type of use (gaming). The results specify a couple of user needs and social norms and values which people attach in this early phase to HMDs. In this early phase of acceptance, some early adoption observations were found as in when someone talks about the type of use (felt needs) and positive judgments (social norms). Early signs of rejection were found by negative judgments (social norms) and comparisons with other products (previous practice).
{"title":"Anticipated Acceptance of Head Mounted Displays: a content analysis of YouTube comments","authors":"Niek Zuidhof, S. B. Allouch, Oscar Peters, P. Verbeek","doi":"10.1109/PERCOMW.2019.8730658","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730658","url":null,"abstract":"For further development of technologies but also for the implementation in real life contexts, it is important to understand users' perspectives on the anticipated use of innovative technologies in an early development phase. In addition, it is also important to get a better understanding of the explanation of this behavior towards technology use in later stages. Although Head Mounted Displays (HMDs) are not really new anymore, the uptake has been slow so far and people showed some extreme reactions. The objective of this study was to analyze the content of YouTube comments on videos of HMDs, in order to get a better understanding of relevant factors in this early phase of potential acceptance of HMDs. We analyzed 379 YouTube comments on HMDs using content analysis. Comments were divided into three groups: HMD, video, and miscellaneous. Comments about HMDs $mathrm{n}=24$ were further analyzed. Most of the commenters showed a positive attitude to HMDs. Within the positive attitude, the most expressed themes were comments about the type of use (gaming), positive evaluations (emotions, coolness) and perceived need for an HMD. Within the negative attitudes, negative evaluations (judgments, emotions) were showed most and negative comparisons to other products were made. In neutral attitudes, the main theme was the type of use (gaming). The results specify a couple of user needs and social norms and values which people attach in this early phase to HMDs. In this early phase of acceptance, some early adoption observations were found as in when someone talks about the type of use (felt needs) and positive judgments (social norms). Early signs of rejection were found by negative judgments (social norms) and comparisons with other products (previous practice).","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124617394","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 : 2019-06-06DOI: 10.1109/PERCOMW.2019.8730787
I. Hafeez, M. Antikainen, S. Tarkoma
Traffic analysis attacks allow an attacker to infer sensitive information about users by analyzing network traffic of user devices. These attacks are passive in nature and are difficult to detect. In this paper, we demonstrate that an adversary, with access to upstream traffic from a smart home network, can identify the device types and user interactions with IoT devices, with significant confidence. These attacks are practical even when device traffic is encrypted because they only utilize statistical properties, such as traffic rates, for analysis. In order to mitigate the privacy implications of traffic analysis attacks, we propose a traffic morphing technique, which shapes network traffic thus making it more difficult to identify IoT devices and their activities. Our evaluation shows that the proposed technique provides protection against traffic analysis attacks and prevent privacy leakages for smart home users.
{"title":"Protecting IoT-environments against Traffic Analysis Attacks with Traffic Morphing","authors":"I. Hafeez, M. Antikainen, S. Tarkoma","doi":"10.1109/PERCOMW.2019.8730787","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730787","url":null,"abstract":"Traffic analysis attacks allow an attacker to infer sensitive information about users by analyzing network traffic of user devices. These attacks are passive in nature and are difficult to detect. In this paper, we demonstrate that an adversary, with access to upstream traffic from a smart home network, can identify the device types and user interactions with IoT devices, with significant confidence. These attacks are practical even when device traffic is encrypted because they only utilize statistical properties, such as traffic rates, for analysis. In order to mitigate the privacy implications of traffic analysis attacks, we propose a traffic morphing technique, which shapes network traffic thus making it more difficult to identify IoT devices and their activities. Our evaluation shows that the proposed technique provides protection against traffic analysis attacks and prevent privacy leakages for smart home users.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254993","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 : 2019-06-06DOI: 10.1109/PERCOMW.2019.8730860
J. M. Quero, F. Cruciani, Lorenzo Seidenari, M. Espinilla, C. Nugent
In this work, we propose a method to create and synthesize a new set of virtual images of daily objects within a smart environment partially automating the labeling process. Proposed methods enable the generation of a large dataset from a set of few images using an ad hoc data augmentation, which increases the original dataset size, generating new items through partial modification of available images. The proposed method for data augmentation is accomplished through the following steps: (i) object tracking is proposed to identify and label static objects; and (ii) background subtraction is used to select the masked foreground object of moving objects, which are virtually projected with geometry transformation over room images used as background. Furthermore, a case study is carried out, where an inhabitant wears a wearable vision sensor in a daily scene. Eight objects are learned using the proposed methodology. Finally, obtained results and successful recognition rates are discussed.
{"title":"Straightforward Recognition of Daily Objects in Smart Environments from Wearable Vision Sensor","authors":"J. M. Quero, F. Cruciani, Lorenzo Seidenari, M. Espinilla, C. Nugent","doi":"10.1109/PERCOMW.2019.8730860","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730860","url":null,"abstract":"In this work, we propose a method to create and synthesize a new set of virtual images of daily objects within a smart environment partially automating the labeling process. Proposed methods enable the generation of a large dataset from a set of few images using an ad hoc data augmentation, which increases the original dataset size, generating new items through partial modification of available images. The proposed method for data augmentation is accomplished through the following steps: (i) object tracking is proposed to identify and label static objects; and (ii) background subtraction is used to select the masked foreground object of moving objects, which are virtually projected with geometry transformation over room images used as background. Furthermore, a case study is carried out, where an inhabitant wears a wearable vision sensor in a daily scene. Eight objects are learned using the proposed methodology. Finally, obtained results and successful recognition rates are discussed.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132294716","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730863
Chenguang Liu, Jie Hua, C. Julien
Mobile applications commonly use on-device sensors to continuously provide context: temperature, position, sound, etc. By collaborating to sense context, devices can save energy and share rare capabilities with minimal tradeoffs in sensing quality. Further, by leveraging already active communication behaviors, ambient context information can be collected at very little cost. We present a generic collaborative sensing framework, SCENTS, to support collective sensing for mobile IoT applications. SCENTS leverages two truths about IoT networks: (1) devices participate continuously in low-level device discovery mechanisms and (2) nearby devices tend to have similar values for many ambient context properties. We show that SCENTS balances sensing fulfillment and the fairness of energy consumption across devices. We measure the performance of SCENTS using real IoT devices and real world smart-city scenarios.
{"title":"SCENTS: Collaborative Sensing in Proximity IoT Networks","authors":"Chenguang Liu, Jie Hua, C. Julien","doi":"10.1109/PERCOMW.2019.8730863","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730863","url":null,"abstract":"Mobile applications commonly use on-device sensors to continuously provide context: temperature, position, sound, etc. By collaborating to sense context, devices can save energy and share rare capabilities with minimal tradeoffs in sensing quality. Further, by leveraging already active communication behaviors, ambient context information can be collected at very little cost. We present a generic collaborative sensing framework, SCENTS, to support collective sensing for mobile IoT applications. SCENTS leverages two truths about IoT networks: (1) devices participate continuously in low-level device discovery mechanisms and (2) nearby devices tend to have similar values for many ambient context properties. We show that SCENTS balances sensing fulfillment and the fairness of energy consumption across devices. We measure the performance of SCENTS using real IoT devices and real world smart-city scenarios.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"54 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120921773","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730728
Kosuke Watanabe, Nobuo Kawaguchi
Recently, an importance of location information has increased with the spread of smartphones. Our purpose is to estimate a smartphone position with a few centimeters error. In addition, we would like to recognize behavior patterns of people, and targets of their interest. These information will provide new services. For example, at an event venue, it will be possible to provide information about other exhibits according to their positions. There is a method that can estimate a position of a magnetic sensor with a few centimeters error is using dynamic magnetism. This method is available even if under strong environmental magnetism because it uses frequency of dynamic magnetism to positioning. However, it is difficult to apply these positioning methods to a smartphone, because a sampling frequency of a magnetic sensor mounted on a smartphone is dozen of hertz, whereas these methods use a dynamic magnetism with several kilohertz [1], [2]. Therefore, we developed a Spinning Magnetic Marker (SMM) which generates dynamic magnetism by spinning a strong magnet, and proposed a positioning method based on dynamic magnetism which can be applied to a smartphone [3], [4]. In this study, we propose a method to estimate a short-range path of a moving smartphone by the hyperbolic function, and curve fitting with magnetism equation.
{"title":"A Smartphone Short-Range Path Estimation with Hyperbolic Function for Spinning Magnet Marker","authors":"Kosuke Watanabe, Nobuo Kawaguchi","doi":"10.1109/PERCOMW.2019.8730728","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730728","url":null,"abstract":"Recently, an importance of location information has increased with the spread of smartphones. Our purpose is to estimate a smartphone position with a few centimeters error. In addition, we would like to recognize behavior patterns of people, and targets of their interest. These information will provide new services. For example, at an event venue, it will be possible to provide information about other exhibits according to their positions. There is a method that can estimate a position of a magnetic sensor with a few centimeters error is using dynamic magnetism. This method is available even if under strong environmental magnetism because it uses frequency of dynamic magnetism to positioning. However, it is difficult to apply these positioning methods to a smartphone, because a sampling frequency of a magnetic sensor mounted on a smartphone is dozen of hertz, whereas these methods use a dynamic magnetism with several kilohertz [1], [2]. Therefore, we developed a Spinning Magnetic Marker (SMM) which generates dynamic magnetism by spinning a strong magnet, and proposed a positioning method based on dynamic magnetism which can be applied to a smartphone [3], [4]. In this study, we propose a method to estimate a short-range path of a moving smartphone by the hyperbolic function, and curve fitting with magnetism equation.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411451","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730766
Stefan Herrnleben, Martin Pfannemüller, Christian Krupitzer, Samuel Kounev, Michele Segata, Felix Fastnacht, Magnus Nigmann
Recent developments in communication technology have led to cloud resources becoming ubiquitous. These resources enable many new applications by offering computational power for remote embedded devices. In combination with advances in the area of smart driving, this seems to be especially beneficial for applications such as remote maintenance of vehicles or integration with smart city services. As autonomous driving continues to gain traction, Car-to-Cloud communication can support transferring collected data to the cloud, e.g., for dynamic learning of new map information. Additionally, passengers can benefit from novel entertainment services. All these developments require a stable connection between a mobile vehicle and the cloud resources. In this vision paper, we survey Car-to-Cloud communication applications. Based on the analysis of the varying requirements for these applications, we formulate research questions and challenges. Further, we discuss how these challenges can be addressed by means of an adaptive Car-to-Cloud communication middleware. We conclude with an overview on our activities in this area and an outlook on our planned future work on adaptive communication.
{"title":"Towards Adaptive Car-to-Cloud Communication","authors":"Stefan Herrnleben, Martin Pfannemüller, Christian Krupitzer, Samuel Kounev, Michele Segata, Felix Fastnacht, Magnus Nigmann","doi":"10.1109/PERCOMW.2019.8730766","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730766","url":null,"abstract":"Recent developments in communication technology have led to cloud resources becoming ubiquitous. These resources enable many new applications by offering computational power for remote embedded devices. In combination with advances in the area of smart driving, this seems to be especially beneficial for applications such as remote maintenance of vehicles or integration with smart city services. As autonomous driving continues to gain traction, Car-to-Cloud communication can support transferring collected data to the cloud, e.g., for dynamic learning of new map information. Additionally, passengers can benefit from novel entertainment services. All these developments require a stable connection between a mobile vehicle and the cloud resources. In this vision paper, we survey Car-to-Cloud communication applications. Based on the analysis of the varying requirements for these applications, we formulate research questions and challenges. Further, we discuss how these challenges can be addressed by means of an adaptive Car-to-Cloud communication middleware. We conclude with an overview on our activities in this area and an outlook on our planned future work on adaptive communication.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121854740","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730818
Martin Pfannemüller, Markus Weckesser, R. Kluge, Janick Edinger, Manisha Luthra, Robin Klose, C. Becker, Andy Schürr
Today's pervasive communication systems are highly configurable to adapt themselves dynamically to continuously changing contexts of the system such as varying workloads and user preferences. For a particular context, usually numerous valid system configurations exist, and each configuration may perform differently in terms of nonfunctional properties like energy consumption or task throughput. For tackling these challenges, in previous work, we introduced Coala, a model-based adaptation approach to derive optimal system configurations considering multiple performance goals. In this paper, we present CoalaViz, a novel tool for visualizing the self-adaptive behavior of pervasive communication systems. With CoalaViz, we provide a tool for making adaptation decisions in self-adaptive pervasive communication systems traceable while being applicable for a wide range of use cases. CoalaViz (i) visualizes the system performance over time, (ii) visualizes the system state as context feature model and graph-based network view, (iii) allows the user to change priorities of performance goals interactively, and (iv) provides a modular, extensible design. We demonstrate the applicability of CoalaViz using three pervasive system use cases.
{"title":"CoalaViz: Supporting Traceability of Adaptation Decisions in Pervasive Communication Systems","authors":"Martin Pfannemüller, Markus Weckesser, R. Kluge, Janick Edinger, Manisha Luthra, Robin Klose, C. Becker, Andy Schürr","doi":"10.1109/PERCOMW.2019.8730818","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730818","url":null,"abstract":"Today's pervasive communication systems are highly configurable to adapt themselves dynamically to continuously changing contexts of the system such as varying workloads and user preferences. For a particular context, usually numerous valid system configurations exist, and each configuration may perform differently in terms of nonfunctional properties like energy consumption or task throughput. For tackling these challenges, in previous work, we introduced Coala, a model-based adaptation approach to derive optimal system configurations considering multiple performance goals. In this paper, we present CoalaViz, a novel tool for visualizing the self-adaptive behavior of pervasive communication systems. With CoalaViz, we provide a tool for making adaptation decisions in self-adaptive pervasive communication systems traceable while being applicable for a wide range of use cases. CoalaViz (i) visualizes the system performance over time, (ii) visualizes the system state as context feature model and graph-based network view, (iii) allows the user to change priorities of performance goals interactively, and (iv) provides a modular, extensible design. We demonstrate the applicability of CoalaViz using three pervasive system use cases.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121288247","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730667
Chenguang Liu, Jie Hua, Changyong Hu, C. Julien
Retrieving context information from on-board sensors is critical to many scenarios in the Internet of Things (IoT). The battery capacity and physical size of smart devices can restrict the on-board sensors that can be supported and thereby limit the potential of applications for multi-agent IoT systems. In this work, we propose a novel scheme for building a context neighborhood among nearby devices through infrastructure-less collaboration. A context neighborhood is an ad hoc grouping of sensing devices that can collaborate to sense physical attributes of the environment. In this way, not every device needs to directly sense every context attribute to be able to leverage the information in its applications. Our approach entails a distributed algorithm that dynamically adjusts a device's sensing and sharing strategy based on the heterogeneity of resources in the proximity. We develop a prototype system using off-the-shelf IoT sensor kits; our demonstration exploits the self-stabilization feature to show the benefits this system could bring to higher-layer applications.
{"title":"Stacon: Self-Stabilizing Context Neighborhood for Mobile IoT Devices","authors":"Chenguang Liu, Jie Hua, Changyong Hu, C. Julien","doi":"10.1109/PERCOMW.2019.8730667","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730667","url":null,"abstract":"Retrieving context information from on-board sensors is critical to many scenarios in the Internet of Things (IoT). The battery capacity and physical size of smart devices can restrict the on-board sensors that can be supported and thereby limit the potential of applications for multi-agent IoT systems. In this work, we propose a novel scheme for building a context neighborhood among nearby devices through infrastructure-less collaboration. A context neighborhood is an ad hoc grouping of sensing devices that can collaborate to sense physical attributes of the environment. In this way, not every device needs to directly sense every context attribute to be able to leverage the information in its applications. Our approach entails a distributed algorithm that dynamically adjusts a device's sensing and sharing strategy based on the heterogeneity of resources in the proximity. We develop a prototype system using off-the-shelf IoT sensor kits; our demonstration exploits the self-stabilization feature to show the benefits this system could bring to higher-layer applications.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127979342","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730850
Yingding Wang, Nikolai Fischer, François Bry
This article reports on coupled smartwatch and smartphone pervasive apps enabling stress self-regulation. Stress, the physiological responses of an organism to demanding conditions, can be both beneficial and harmful. Beneficial stress, or eustress, enhances physical or mental abilities. Harmful stress, or distress, can result in reduced abilities, anxiety, or depression. The potential of pervasive computing to enable stress self-regulation, that is, the ability to benefit from eustress while avoiding or limiting distress, is explored in this article. It first reports on Stila Computed Stress, a stress estimate computed after an original model from pulse rates delivered by smartwatches. This article then describes how Stila Computed Stress is combined with users' activity reports and pervasively delivered on their smartwatches and smartphones. It further reports on a real life evaluation pointing to the pervasive apps' persuasiveness, that is, the apps' capacity to increase subjective stress awareness so as to enhance stress self-regulation.
{"title":"Pervasive Persuasion for Stress Self-Regulation","authors":"Yingding Wang, Nikolai Fischer, François Bry","doi":"10.1109/PERCOMW.2019.8730850","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730850","url":null,"abstract":"This article reports on coupled smartwatch and smartphone pervasive apps enabling stress self-regulation. Stress, the physiological responses of an organism to demanding conditions, can be both beneficial and harmful. Beneficial stress, or eustress, enhances physical or mental abilities. Harmful stress, or distress, can result in reduced abilities, anxiety, or depression. The potential of pervasive computing to enable stress self-regulation, that is, the ability to benefit from eustress while avoiding or limiting distress, is explored in this article. It first reports on Stila Computed Stress, a stress estimate computed after an original model from pulse rates delivered by smartwatches. This article then describes how Stila Computed Stress is combined with users' activity reports and pervasively delivered on their smartwatches and smartphones. It further reports on a real life evaluation pointing to the pervasive apps' persuasiveness, that is, the apps' capacity to increase subjective stress awareness so as to enhance stress self-regulation.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121784285","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 : 2019-03-11DOI: 10.1109/PERCOMW.2019.8730648
Shinsuke Kajioka, Takuto Sakuma, I. Takeuchi
Many social issues are expected to be addressed by collecting human trajectory data and analyzing them. As a demonstration study, we need a continuous and instant localization and trajectory collection system. We have developed a localization system using Bluetooth Low Energy (BLE) beacons and smartphones in our college campus. The system has been established to realize automated student roll call with 1, 600 BLE beacon emitters installed on our campus. We can estimate the location of a smartphone in our campus by analyzing received BLE beacons and their RSSIs (Received Signal Strength Indicators). In this paper, we demonstrate how we collect human trajectory data and how we can detect specific human behaviors from the collected data. We have obtained human trajectory data from 169 research participants comprised of 671 trips during the study held as a college festival event. Each research participant walked around with his/her smartphone. The smartphone continuously received BLE beacons during the event and periodically sent them to the server as a trajectory. We apply comparative sequential pattern mining to the obtained trajectory data and extract sequential patterns that are different between male trajectories and female trajectories. This study demonstrates the effectiveness of human trajectory data collection by a BLE beacon system and data analysis by comparative sequential pattern mining.
{"title":"Comparative Sequential Pattern Mining of Human Trajectory Data Collected from a Campus-wide BLE Beacon System","authors":"Shinsuke Kajioka, Takuto Sakuma, I. Takeuchi","doi":"10.1109/PERCOMW.2019.8730648","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730648","url":null,"abstract":"Many social issues are expected to be addressed by collecting human trajectory data and analyzing them. As a demonstration study, we need a continuous and instant localization and trajectory collection system. We have developed a localization system using Bluetooth Low Energy (BLE) beacons and smartphones in our college campus. The system has been established to realize automated student roll call with 1, 600 BLE beacon emitters installed on our campus. We can estimate the location of a smartphone in our campus by analyzing received BLE beacons and their RSSIs (Received Signal Strength Indicators). In this paper, we demonstrate how we collect human trajectory data and how we can detect specific human behaviors from the collected data. We have obtained human trajectory data from 169 research participants comprised of 671 trips during the study held as a college festival event. Each research participant walked around with his/her smartphone. The smartphone continuously received BLE beacons during the event and periodically sent them to the server as a trajectory. We apply comparative sequential pattern mining to the obtained trajectory data and extract sequential patterns that are different between male trajectories and female trajectories. This study demonstrates the effectiveness of human trajectory data collection by a BLE beacon system and data analysis by comparative sequential pattern mining.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130877067","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：