Mobile WellBeing

Citysense aims to let users find the most popular night spots in San Francisco and the most efficient ways to get to them. Does Healthcare has something to do with this similar real-time location data analysis? The next stage of projects/products will be to enable users to social network, using data from sensors as an input. And sensors we have in mHealth.

Citysense is heading in this new direction. With the next release of its product aiming to guide ‘tribes’ of people together using location data. It will soon be able to show not only where anonymous groups of people are in real time, but where people with similar behavior patterns or health conditions to you are. To do this, Citysense will categorize people into “tribes”. So far, 20 tribes have been identified, including “young and edgy,” “business traveler,” “weekend mole,” and “homebody.” It will use not only GPS (location) data from mobile phones and taxis, but also publicly available company address data and demographic data from the U.S. Census Bureau.

Sensors have become much more prevelant in mobile devices. This means that when we talk about sensors, we’re not necessarily talking about the microchip embedded in your fridge door. Increasingly, sensors are attached to a human via their mobile phone.

One application for sensors in social networks is to help people to meet others, using alerts based on their location at a particular time. A recent W3C Workshop paper entitled Integrating Social Networks and Sensor Networks contains two suggestions using sensor-enabled portable devices:

“Social networks and sensor networks can be combined to support independent living and health support for elders. By deriving semantic presence based on context from sensor-enabled social networking devices, we can carry out useful tasks for the elderly. For example for daily living purposes, we can check the status of the friends and find shopping or walking buddies to promote the mobility of elders. By using semantic representations of information from sensors, we can build on the idea of connecting people through shared activities and interests. More importantly, we can send alerts based on abnormal activity patterns. Through sensor readings of body position or health measurements, we can issue requests for attention not just to carers or clinicians but to nearby friends in the elder’s social network.

“More and more portable devices are supporting sensor-based interactions, from peripherals (Nike+iPod) to integrated sensors (the original iPhone made good use of its accelerometer, while the latest iPhone 3G has added various proximity and light sensors). We can make use of the Social Web and Sensor Networks to create collaborative applications for portable devices to encourage exercise, à la the Wii. As an example of how this could be done, we could begin by finding contacts on the social network with similar interests or by GPS location. This social network of friends can then be used to power collaborative applications where progress can be made by the group when a certain level of exercise has been achieved. Then, as a final step, the resulting sensor data is sent to physicians for analysis.”

The conclusion of the W3C paper is that “the integration of sensor networks with social networks leads to applications that can sense the context of a user in much better ways and thus provides more personalized and detailed solutions.”