Abstract:
Physical activity is an important factor that is considered for the prevention of diseases like
diabetes or hypertension and for rehabilitation. Besides the advancement of technology and
availability of smart-phones creates the opportunity to utilize the power of smartphone's sen-
sors, for example, accelerometers, to support cost-e ective behavioral intervention to promote
physical activities. In this thesis, we attempt to identify basic physical activities of a user
from smartphone's 3D accelerometer data and then suggest the user through mobile phone
noti cations the recommended level of physical activities he/she should undergo.
In our work, we analyze an existing dataset containing accelerometer data with labeled
physical activities, namely standing, walking, stair-up and stair-down and a few others, and
learn the patterns identifying various activities. Once the patterns are learned, we identify series
of activities that a certain user performs from its mobile phone accelerometer data determining
what portion of time the user spends in what activities. Based on this information, we develop
suggestions of performing activities for that user by analyzing his/her current and required
amount of physical activities within a time window using some prede ned standard (amount of
activities he/she must undergoes to be t and healthy). These suggestions are propagated to
the user suggesting him/her to make further engagement in physical activities through mobile
noti cation system.
