How Well Do Automatically Evolved Neurons Perform? A Comparative Study in Human Activity Recognition

Neuroevolution, a subfield of AI leveraging evolutionary algorithms, offers an effective method for automatically generating data-adaptive equations that optimize fit for specific datasets while minimizing human design bias. This study investigates the efficacy of the WISDM-ARN, a specialized Adaptive Recurrent Neuron (ARN) synthesized via the Automatic Design of Algorithms through Evolution (ADATE) system, for Human Activity Recognition (HAR). In safety-critical applications, such as healthcare monitoring and industrial safety, the reliability of locomotion classification is paramount to prevent system failures and ensure user protection. We benchmark the evolved WISDMARN against three established architectures: Simple RNN, Long Short-Term Memory (LSTM), and Transformers. Experimental results demonstrate that the WISDM-ARN successfully adapts to the unique temporal characteristics of gyroscope data, achieving a testing accuracy of 97 %, which is comparable to the baseline models with resulting 70 %, 95 %, and 92 %, respectively. These findings highlight the potential of neuroevolutionary synthesis as a dependable alternative to manually engineered architectures. By integrating neuroevolution with modern deep learning, the ADATE-generated neuron represents a significant step toward automated reliable neural modeling for human activity recognition tasks and safety-enhanced time-series analysis.

Keywords: Evolved Recursive Neuron, Recurrent Neural Networks, Human Activity Recognition.