RapidHARe: A computationally inexpensive method for real-time human activity recognition from wearable sensors

TL;DR

RapidHARe is a fast, computationally inexpensive method for real-time human activity recognition from wearable sensors, outperforming traditional models in speed while maintaining high accuracy, making it suitable for mobile applications.

Contribution

The paper introduces RapidHARe, a novel approach that models raw sensor data with dynamic Bayesian networks without feature extraction, achieving superior speed and comparable accuracy for real-time HAR.

Findings

RapidHARe is 1.5 times faster than ANN methods.

It is over 8 times faster than RNNs and HMMs.

Achieves 94.27% F1 score and 98.94% accuracy.

Abstract

Recent human activity recognition (HAR) methods, based on on-body inertial sensors, have achieved increasing performance; however, this is at the expense of longer CPU calculations and greater energy consumption. Therefore, these complex models might not be suitable for real-time prediction in mobile systems, e.g., in elder-care support and long-term health-monitoring systems. Here, we present a new method called RapidHARe for real-time human activity recognition based on modeling the distribution of a raw data in a half-second context window using dynamic Bayesian networks. Our method does not employ any dynamic-programming-based algorithms, which are notoriously slow for inference, nor does it employ feature extraction or selection methods. In our comparative tests, we show that RapidHARe is an extremely fast predictor, one and a half times faster than artificial neural networks (ANNs) methods, and more than eight times faster than recurrent neural networks (RNNs) and hidden Markov models (HMMs). Moreover, in performance, RapidHare achieves an F1 score of 94.27\% and accuracy of 98.94\%, and when compared to ANN, RNN, HMM, it reduces the F1-score error rate by 45\%, 65\%, and 63\% and the accuracy error rate by 41\%, 55\%, and 62\%, respectively. Therefore, RapidHARe is suitable for real-time recognition in mobile devices.