Description of research
Physical activities play an important role in our physical and mental well-being. The lack of physical activities is one of the leading causes in many diseases these days. Though people know its importance, still they need regular motivational feedback to remain active in their daily life. To give them proper feedback, we need to recognize their activities first. This research is about recognizing human context (activity and situation) using various heterogeneous sensors such smartphone sensors, smart-watch sensors and ambient sensors. If recognized reliably, this context can enable novel well-being applications in different fields, for example, healthcare.
The goal of this research is to use various sensors to reliably detect different physical activities (additionally, the situation) of users, which can be used in giving them motivational feedback regarding their well-being.
To achieve this goal, we recognize various physical activities using smartphone sensors such as the accelerometer, gyroscope, and magnetometer. Moreover, we use smart watch and want to see the possibilities of activity recognition with upcoming smart watches. We want to reliably recognize physical activities using heterogeneous sensor information, that may be incomplete or unreliable. We are currently working on improving the existing work by investigating and solving the open challenges in activity recognition using smartphone sensors.
SWELL (Smart reasoning systems for well-being at work and home)
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