Position-Agnostic Smartphone Placement Detection for Improved Reliability in Human Activity Recognition

Abstract

This research aims to solve the problem of position-independent activity recognition, a critical aspect in accurately identifying human activities using smartphones. Our study addresses this challenge by employing Convolutional Neural Networks to classify activities such as walking, sitting, running, and more, regardless of the smartphone’s position on the body. Leveraging a real-world publicly available dataset, we demonstrate 98% accuracy obtained solely from accelerometer data, surpassing state-of-the-art techniques by 5.77%. This advancement holds promise for enhancing smartphone-based human activity recognition, particularly in security-related applications like adaptive user authentication. Overall, our research demonstrates progress toward improving the reliability and adaptability of activity recognition systems across diverse contexts.