  The research on human activity recognition has provided novel solutions to
many applications like healthcare, sports, and user profiling. Considering the
complex nature of human activities, it is still challenging even after
effective and efficient sensors are available. The existing works on human
activity recognition using smartphone sensors focus on recognizing basic human
activities like sitting, sleeping, standing, stair up and down and running.
However, more than these basic activities is needed to analyze human
behavioural pattern. The proposed framework recognizes basic human activities
using deep learning models. Also, ambient sensors like PIR, pressure sensors,
and smartphone-based sensors like accelerometers and gyroscopes are combined to
make it hybrid-sensor-based human activity recognition. The hybrid approach
helped derive more activities than the basic ones, which also helped derive
human activity patterns or user profiling. User profiling provides sufficient
information to identify daily living activity patterns and predict whether any
anomaly exists. The framework provides the base for applications such as
elderly monitoring when they are alone at home. The GRU model's accuracy of
95\% is observed to recognize the basic activities. Finally, Human activity
patterns over time are recognized based on the duration and frequency of the
activities. It is observed that human activity pattern, like, morning walking
duration, varies depending on the day of the week.
