  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.
