The same factors that allowed us predict the advent of life-logging point also to the rise of thing-logging -- a precursor to the complete Internet of Things vision. Most objects will go through a progression of being logged, being tracked, and being a peripheral, before they become fully connected. Trends in wealth and technology will fuel this progression, but ultimately adoption will be driven by value to the consumer. Experience with life-logging and thing-logging gives us an idea of what that value proposition will be, and shows us some key technical challenges ahead.
{"title":"Life-logging, thing-logging and the internet of things","authors":"J. Gemmell","doi":"10.1145/2611264.2611276","DOIUrl":"https://doi.org/10.1145/2611264.2611276","url":null,"abstract":"The same factors that allowed us predict the advent of life-logging point also to the rise of thing-logging -- a precursor to the complete Internet of Things vision. Most objects will go through a progression of being logged, being tracked, and being a peripheral, before they become fully connected. Trends in wealth and technology will fuel this progression, but ultimately adoption will be driven by value to the consumer. Experience with life-logging and thing-logging gives us an idea of what that value proposition will be, and shows us some key technical challenges ahead.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128403396","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}
Can a smartphone learn our eating habits without the user being in the loop? Clearly, the phone could use checkins based on location to infer that if you were in a cafe, for example, there is a good possibility you might eat or drink something. In this paper, we use inferred behavioral data and location history to predict if you are going to eat or not in the near future. These predictors could serve as a basis for future eating trackers that work unobtrusively in the background of your phone rather than relying on burdensome user input. In this paper, we report on a simple model that predicts the food purchases of a group of undergraduate college students (N=25) using inferred behavioral and location data from smartphones. The 10-week study uses the dining related purchase records from student college cards as ground-truth to validate our prediction model. Initial results show that we can predict food and drink purchases with an accuracy of 74% using three weeks of training data.
{"title":"My smartphone knows i am hungry","authors":"Fanglin Chen, Rui Wang, Xia Zhou, A. Campbell","doi":"10.1145/2611264.2611270","DOIUrl":"https://doi.org/10.1145/2611264.2611270","url":null,"abstract":"Can a smartphone learn our eating habits without the user being in the loop? Clearly, the phone could use checkins based on location to infer that if you were in a cafe, for example, there is a good possibility you might eat or drink something. In this paper, we use inferred behavioral data and location history to predict if you are going to eat or not in the near future. These predictors could serve as a basis for future eating trackers that work unobtrusively in the background of your phone rather than relying on burdensome user input. In this paper, we report on a simple model that predicts the food purchases of a group of undergraduate college students (N=25) using inferred behavioral and location data from smartphones. The 10-week study uses the dining related purchase records from student college cards as ground-truth to validate our prediction model. Initial results show that we can predict food and drink purchases with an accuracy of 74% using three weeks of training data.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123846224","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 rich context provided by smartphones has enabled many new context-aware applications. However, these applications still need to provide their own mechanisms to interpret low-level sensing data and generate high-level user states. In this paper, we propose the idea of building a personal analytics (PA) layer that will use inputs from multiple lower layer sources, such as sensor data (accelerometers, gyroscopes, etc.), phone data (call logs, application activity, etc.), and online sources (Twitter, Facebook posts, etc.) to generate high-level user contextual states (such as emotions, preferences, and engagements). Developers can then use the PA layer to easily build a new set of interesting and compelling applications. We describe several scenarios enabled by this new layer and present a proposed software architecture. We end with a description of some of the key research challenges that need to be solved to achieve this goal.
{"title":"The case for human-centric personal analytics","authors":"Youngki Lee, R. Balan","doi":"10.1145/2611264.2611267","DOIUrl":"https://doi.org/10.1145/2611264.2611267","url":null,"abstract":"The rich context provided by smartphones has enabled many new context-aware applications. However, these applications still need to provide their own mechanisms to interpret low-level sensing data and generate high-level user states. In this paper, we propose the idea of building a personal analytics (PA) layer that will use inputs from multiple lower layer sources, such as sensor data (accelerometers, gyroscopes, etc.), phone data (call logs, application activity, etc.), and online sources (Twitter, Facebook posts, etc.) to generate high-level user contextual states (such as emotions, preferences, and engagements). Developers can then use the PA layer to easily build a new set of interesting and compelling applications. We describe several scenarios enabled by this new layer and present a proposed software architecture. We end with a description of some of the key research challenges that need to be solved to achieve this goal.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124618978","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}
With a growing number of mobile consumers routinely carrying at least one Wi-Fi enabled smartphone or tablet and the popularity of Wi-Fi as a preferred network access medium, Wi-Fi analytics can provide major insights into consumer behavior to businesses across different industry verticals. The intelligence gained from these new insights can be leveraged to spawn new applications in the fields of business strategy (e.g., real world A/B testing), marketing (e.g., location-based services, customer engagement) and operations (e.g., staffcasting. In this presentation I'll provide several examples from live deployments of AirTight Wi-Fi Analytics. Using data from a case study, I'll show how we found many unexpected uses of analytics reports based on: 1) the Wi-Fi devices that are detected by or associate with an AirTight AP; and 2) Wi-Fi users that opt into sharing personal information for an incentive.
{"title":"Wi-Fi analytics for business intelligence","authors":"P. Bhagwat","doi":"10.1145/2611264.2611274","DOIUrl":"https://doi.org/10.1145/2611264.2611274","url":null,"abstract":"With a growing number of mobile consumers routinely carrying at least one Wi-Fi enabled smartphone or tablet and the popularity of Wi-Fi as a preferred network access medium, Wi-Fi analytics can provide major insights into consumer behavior to businesses across different industry verticals. The intelligence gained from these new insights can be leveraged to spawn new applications in the fields of business strategy (e.g., real world A/B testing), marketing (e.g., location-based services, customer engagement) and operations (e.g., staffcasting. In this presentation I'll provide several examples from live deployments of AirTight Wi-Fi Analytics. Using data from a case study, I'll show how we found many unexpected uses of analytics reports based on: 1) the Wi-Fi devices that are detected by or associate with an AirTight AP; and 2) Wi-Fi users that opt into sharing personal information for an incentive.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129334366","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}
{"title":"Session details: Health and wellness","authors":"D. Estrin","doi":"10.1145/3255790","DOIUrl":"https://doi.org/10.1145/3255790","url":null,"abstract":"","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128989162","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}
Wi-Fi-based tracking systems have recently appeared. By collecting radio signals emitted by Wi-Fi enabled devices, those systems are able to track individuals. They basically rely on the MAC address to uniquely identify each individual. If retailers and business have high expectations for physical tracking, it is also a threat for citizens privacy. We analyse the privacy policies used by the current tracking companies then we show the pitfalls of hash-based anonymization. More particularly we demonstrate that the hash-based anonymization of MAC address used in many Wi-Fi tracking systems can be easily defeated using of-the-shelf software and hardware. Finally we discuss possible solutions for MAC address anonymization in Wi-Fi tracking systems.
{"title":"Analysing the privacy policies of Wi-Fi trackers","authors":"Levent Demir, M. Cunche, C. Lauradoux","doi":"10.1145/2611264.2611266","DOIUrl":"https://doi.org/10.1145/2611264.2611266","url":null,"abstract":"Wi-Fi-based tracking systems have recently appeared. By collecting radio signals emitted by Wi-Fi enabled devices, those systems are able to track individuals. They basically rely on the MAC address to uniquely identify each individual. If retailers and business have high expectations for physical tracking, it is also a threat for citizens privacy. We analyse the privacy policies used by the current tracking companies then we show the pitfalls of hash-based anonymization. More particularly we demonstrate that the hash-based anonymization of MAC address used in many Wi-Fi tracking systems can be easily defeated using of-the-shelf software and hardware. Finally we discuss possible solutions for MAC address anonymization in Wi-Fi tracking systems.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134061747","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}
Sedentary lifestyles have become ubiquitous in modern societies. Sitting, watching television and using the computer are sedentary behaviors that are now common worldwide. Research studies have shown that how often and how long a person is sedentary is linked with an increased risk of obesity, diabetes, cardiovascular disease, and all-cause mortality. Effective strategies for motivating people to become more active are now crucial. In this paper, we present a smartphone application called 'On11', which runs in the background of users' smartphones and monitors their daily physical activity continuously. Unlike traditional pedometers that only passively count steps and estimate burnt calories, On11 also detects sedentary behaviors (sitting, lying down). It presents 'at-a-glance' summaries of what percentage of the user's day have been spent sitting, walking, and running, and total calories burnt thus far that day so that the user can self-reflect. It records the intensity, duration, and type of activities performed and recommends personalized short walks and detours to users' regular routes such as home to workplace. The user can set performance goals, which allows On11 to suggest activities to help them meet their goals. The results of our preliminary user study were encouraging.
{"title":"On11: an activity recommendation application to mitigate sedentary lifestyle","authors":"Qian He, E. Agu","doi":"10.1145/2611264.2611268","DOIUrl":"https://doi.org/10.1145/2611264.2611268","url":null,"abstract":"Sedentary lifestyles have become ubiquitous in modern societies. Sitting, watching television and using the computer are sedentary behaviors that are now common worldwide. Research studies have shown that how often and how long a person is sedentary is linked with an increased risk of obesity, diabetes, cardiovascular disease, and all-cause mortality. Effective strategies for motivating people to become more active are now crucial. In this paper, we present a smartphone application called 'On11', which runs in the background of users' smartphones and monitors their daily physical activity continuously. Unlike traditional pedometers that only passively count steps and estimate burnt calories, On11 also detects sedentary behaviors (sitting, lying down). It presents 'at-a-glance' summaries of what percentage of the user's day have been spent sitting, walking, and running, and total calories burnt thus far that day so that the user can self-reflect. It records the intensity, duration, and type of activities performed and recommends personalized short walks and detours to users' regular routes such as home to workplace. The user can set performance goals, which allows On11 to suggest activities to help them meet their goals. The results of our preliminary user study were encouraging.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115626305","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 people-aware computing group at Cornell has been developing techniques to cheaply, accurately, and continuously collect data on daily human behavior, social interactions, and context. This data is subsequently leveraged to provide targeted, personalized and effective feedback to promote better mental and physical health in individuals. In this talk I will give an overview of our work on turning sensor-enabled mobile phones into well-being monitors and instruments for administering real-time/real-place interventions.
{"title":"Using smartphones to sense, assess, and improve well-being","authors":"Tanzeem Choudhury","doi":"10.1145/2611264.2611275","DOIUrl":"https://doi.org/10.1145/2611264.2611275","url":null,"abstract":"The people-aware computing group at Cornell has been developing techniques to cheaply, accurately, and continuously collect data on daily human behavior, social interactions, and context. This data is subsequently leveraged to provide targeted, personalized and effective feedback to promote better mental and physical health in individuals. In this talk I will give an overview of our work on turning sensor-enabled mobile phones into well-being monitors and instruments for administering real-time/real-place interventions.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131647079","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}
Wireless signals are typically used for data communication between an RF transmitter and an RF receiver. Recent advances in wireless technologies, however, have demonstrated that a person's motion can modulate the wireless signal, enabling the transfer of information from a human to an RF transceiver, even when the person does not carry a transmitter. This leads to new wireless systems in which a user communicates directly with remote devices using gestures. It also allows for using wireless signals to learn about the environment. For example, one may track objects and people as they move around, purely based on how their motion modulates the wireless signal. This could lead to new video games and virtual reality applications that work in non-line-of-sight and across rooms. It can also be used for health-care monitoring in hospitals or at home, and for intrusion detection or search-and-rescue operations. In this talk, I will present sensing technologies that pinpoint people's locations based purely on RF reflections off their bodies. They can further track a person's breathing and heartbeat remotely, without requiring any body contact. They operate by transmitting a low-power wireless signal and monitoring its reflections. They use these reflections to track body motion as well as minute movements associated with breathing and heartbeat (e.g., the chest movements caused by the inhale-exhale process). We envision that such technologies can enable truly smart homes that learn people's habits and monitor their vital signs to adapt the environment and actively contribute to their inhabitants' well-being.
{"title":"Tracking people and monitoring their vital signs using body radio reflections","authors":"D. Katabi","doi":"10.1145/2611264.2611271","DOIUrl":"https://doi.org/10.1145/2611264.2611271","url":null,"abstract":"Wireless signals are typically used for data communication between an RF transmitter and an RF receiver. Recent advances in wireless technologies, however, have demonstrated that a person's motion can modulate the wireless signal, enabling the transfer of information from a human to an RF transceiver, even when the person does not carry a transmitter. This leads to new wireless systems in which a user communicates directly with remote devices using gestures. It also allows for using wireless signals to learn about the environment. For example, one may track objects and people as they move around, purely based on how their motion modulates the wireless signal. This could lead to new video games and virtual reality applications that work in non-line-of-sight and across rooms. It can also be used for health-care monitoring in hospitals or at home, and for intrusion detection or search-and-rescue operations. In this talk, I will present sensing technologies that pinpoint people's locations based purely on RF reflections off their bodies. They can further track a person's breathing and heartbeat remotely, without requiring any body contact. They operate by transmitting a low-power wireless signal and monitoring its reflections. They use these reflections to track body motion as well as minute movements associated with breathing and heartbeat (e.g., the chest movements caused by the inhale-exhale process). We envision that such technologies can enable truly smart homes that learn people's habits and monitor their vital signs to adapt the environment and actively contribute to their inhabitants' well-being.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128816499","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}
It is our great pleasure to welcome you to the Workshop on Physical Analytics at ACM MobiSys 2014. This is a new workshop, inspired by explosion in the volume and diversity of physical data pertaining to users. There is much to be learnt about users, and to serve users, from analyzing this data --- their activities, interests, health, possessions, and more. Besides being significant on its own, such physical analytics also has the potential to augment existing user analytics based on online signals. The workshop program includes an interesting mix of papers and talks on three broad themes: health and wellness, human and social sensing, and wireless tracking. The program includes two keynote talks, three other invited talks, and six refereed paper presentations, covering a mix of blue sky research and systems in deployment by startups. We have included a discussion period at the end of each session, with a moderator who will engage the speakers and the audience in a discussion. We have also set aside time at the end of the workshop program to discuss topics outside of the session themes, including the future direction for the workshop.
{"title":"Proceedings of the 2014 workshop on physical analytics","authors":"D. Estrin, V. Padmanabhan","doi":"10.1145/2611264","DOIUrl":"https://doi.org/10.1145/2611264","url":null,"abstract":"It is our great pleasure to welcome you to the Workshop on Physical Analytics at ACM MobiSys 2014. This is a new workshop, inspired by explosion in the volume and diversity of physical data pertaining to users. There is much to be learnt about users, and to serve users, from analyzing this data --- their activities, interests, health, possessions, and more. Besides being significant on its own, such physical analytics also has the potential to augment existing user analytics based on online signals. \u0000 \u0000The workshop program includes an interesting mix of papers and talks on three broad themes: health and wellness, human and social sensing, and wireless tracking. The program includes two keynote talks, three other invited talks, and six refereed paper presentations, covering a mix of blue sky research and systems in deployment by startups. We have included a discussion period at the end of each session, with a moderator who will engage the speakers and the audience in a discussion. We have also set aside time at the end of the workshop program to discuss topics outside of the session themes, including the future direction for the workshop.","PeriodicalId":131326,"journal":{"name":"Proceedings of the 2014 workshop on physical analytics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120959363","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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