Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection

TL;DR

This paper presents a hybrid-sensor-based deep learning framework for recognizing complex human activities in smart homes, enabling detailed user profiling and anomaly detection, with applications in elderly monitoring.

Contribution

It introduces a hybrid sensor approach combined with deep learning to recognize a broader range of activities and analyze behavioral patterns for user profiling.

Findings

GRU model achieves 95% accuracy in basic activity recognition

Hybrid sensors enable detection of more complex activities

Activity patterns vary with time and day of the week

Abstract

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.