000			01802nam a22001817a 4500
003			OSt
005			20220107122853.0
008			190705b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aAnanthu M (92217002) _916141
245			_aHuman activity recognition based on sensor data
300			_aMSC DA 2017-2019
500			_aPhysical activities play a very important role in our physical and mental well-being. The lack of physical activities can negatively aect our wellbeing. 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 motivational feedback, we need to recognize their physical activates rst (in our case, the main target group is knowledge workers). Therefore, this research is about recognizing human context (condition, activity and situation etc.) using heterogeneous sensors. If recognized reliably, this context can enable novel well-being applications in dierent elds, for example, healthcare. As a rst step to achieve this goal, we recognize some physical activities using smartphone sensors like 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.
502			_bMSC DA _c2017-2019 _dINT _eDr T K Manoj Kumar
650			_aSENSOR DATA _916146
650			_aHUMAN ACTIVITY RECOGNITION _916147
942			_2ddc _cPR
999			_c6562 _d6562