Human Activity Recognition Using Heterogeneous Sensors

Physical activities play an important role in our physical and mental well-being. The lack of such activities can negatively affect our well-being. Though people know the importance of physical activities, still they need regular motivational feedback to remain active in their daily life. In order to give them proper feedback, we need to recognize their physical activities first. In our case, the main target group is knowledge workers. Therefore, this research is about recognizing human context (condition, activity and situation) using heterogeneous sensors. If recognized reliably, this context can enable novel well-being applications in different fields, for example, healthcare. As a first step to achieve this goal, we recognize some physical activities using smartphone sensors like the accelerometer, gyroscope, and magnetometer. Moreover, we are simulating a smartphone on a wrist position as a 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.