Machine Learning Prediction of Human Activity Recognition

Abstract

Wearable computation is getting integrated into our daily life. It has got wide acceptance due to their small sizes, and reasonable computation power. These wearable devices loaded with sensors are good candidates to monitor user’s daily behavior (walking, jogging, sleeping…). Human Activity Recognition (HAR) has the potential to benefit the development of assistive technologies in order to support care of the chronically ill and people with special needs. Activity recognition can be used to provide information about patients’ routines to support the development of e-health systems, like Ambient Assisted Living (AAL). Despite human activity recognition being an active field for more than a decade; the development of context-aware systems, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. The study discusses the principal issues and challenges of HAR systems. A general and data acquisition architecture for HAR systems are presented. HAR systems made use of machine learning techniques and tools, which are helpful to build patterns to describe, analyze, and predict data. Since a human activity recognition system should return a label such as walking, sitting, running, etc., most HAR systems work in a supervised fashion. The objective of proposed study is applying multiple machine learning algorithms on the HAR dataset from Groupware. Out of the 5 machine learning algorithms that random forest yields the highest accuracy in predicting activities correctly, results showed the accuracy of 100%. All the models were also ensembled to improve overall accuracy.