Daily Activity Recognition has profound importance in our lives for applications
such as, behavior analysis at indoor, smart healthcare, entertainment and
surveillance applications. Smartphone based Human Activity Recognition (HAR)
frameworks provide a convenient and cost eective solution to the problems. Seamless
monitoring of elderly people living alone mostly at indoor has become feasible
applying smartphone based HAR with assured privacy compared to camera based
monitoring. Machine learning techniques are adopted as the problem needs to scale
well with varying datasets and conditions. The main challenges behind such a successful
HAR framework for smartphones include (i) stable recognition performance
irrespective of dierent hardware conguration and usage behavior of smartphones
and (ii) annotating data in real-life scenario, especially for activity transitions involving
composite activities. These are the main challenges addressed in this thesis.
The rst challenge is addressed in mainly two ways- (i) through designing ensemble
of classiers and (ii) fusion of sensors. Fusion of dierent smartphone sensors
and fusion of wearable sensing with smartphone sensors are explored for detailed
activity recognition. In order to address the second challenge of grossly labeled
datasets, Multiple Instance Multiple Label (MIML) learning methods are explored.
An ensemble of MIML-KNN classiers is designed that is found to predict activity
sequence as well as set of activity transitions performed for a given time period
with considerable accuracy. The framework is also found to detect unknown activity
combinations either partially or totally.