WPA '14: Proceedings of the 2014 workshop on physical analytics

Full Citation in the ACM Digital Library

SESSION: Health and wellness

Session details: Health and wellness

Physical analytics to model health behaviors

Mobile phones are a pervasive platform for opportunistic sensing of social and health related behaviors. In this talk, I discuss how sensor data from mobile phones can be used to model and predict health outcomes. The talk starts with a review of ...

On11: an activity recommendation application to mitigate sedentary lifestyle

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 ...

My smartphone knows i am hungry

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 ...

Using smartphones to sense, assess, and improve well-being

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, ...

SESSION: Human and social sensing

Session details: Human and social sensing

Life-logging, thing-logging and the internet of things

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 ...

Socio-physical analytics: challenges & opportunities

In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and ...

The case for human-centric personal analytics

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 ...

GlimpseData: towards continuous vision-based personal analytics

Emerging wearable devices provide a new opportunity for mobile context-aware applications to use continuous audio/video sensing data as primitive inputs. Due to the high-datarate and compute-intensive nature of the inputs, it is important to design ...

SESSION: Wireless tracking

Session details: Wireless tracking

Wi-Fi analytics for business intelligence

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 ...

Analysing the privacy policies of Wi-Fi trackers

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 ...

Tracking people and monitoring their vital signs using body radio reflections

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 ...