Daily human activities recognition using heterogeneous sensors from smart devices
Devika B Kumar (92217009)
Daily human activities recognition using heterogeneous sensors from smart devices - MSC DA 2017-2019
Physical 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.
RANDOM FOREST
DECISION TREE
NAIVE BAYES
MACHINE LEARNING
Daily human activities recognition using heterogeneous sensors from smart devices - MSC DA 2017-2019
Physical 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.
RANDOM FOREST
DECISION TREE
NAIVE BAYES
MACHINE LEARNING