Fitness bands and smart watches can do much more than just measure steps. The motion detecting sensors in these devices generate an enormous amount of data, and if analyzed correctly, this haystack of information can yield all sorts of valuable needles of actionable insight.

Consider smoking, for example. The actions of lighting a cigarette and smoking it require certain hand motions that can be detected and tracked. Somatix is a company built on Big Data analysis. They have turned their attention to smoking cessation with their SmokeBeat product. This uses off-the-shelf activity monitors — such as fitness bands and smart watches — to track the activities of subjects who are trying to quit smoking. It passively collects raw motion data and then uses predictive analytics to deliver insights to users and their healthcare providers. In one study, users in an experimental group were notified whenever the program detected smoking activity, and asked to confirm or deny it. Users in a control group were told that they were on a wait list for the study. Both groups completed questionnaires at the start and end of the study. After 30 days, the experimental group reported a significant reduction in their smoking rate, while there was no change for the control group. The system is unobtrusive and requires no actions on the part of the subject. Coupled with cognitive behavior therapy (CBT) techniques such as support information and coping tactics, the system can be a powerful tool to help smokers quit.

Somatix is applying the same tools to other problems. For example, the company is developing a SafeBeing system that uses off-the-shelf activity monitors to track the daily activities of elderly subjects. The system is designed to detect sudden or gradual impairments, and monitors activities of daily living (ADL) without the need for cameras or other intrusive devices. It can generate alerts as well as provide overall activity data, such as eating, drinking, and walking. The result is that Big Data analytics can use relatively simple, low-cost devices to provide complex analysis of daily behaviors to address a wide range of health issues.