Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark
This work addresses the lack of standardized evaluation for human activity recognition using wearable sensors, which is a problem for researchers and developers seeking fair comparisons of state-of-the-art techniques.
This paper reviews recent human activity recognition techniques using wearable sensors and applies a standardized evaluation benchmark across six public datasets. The authors also propose a hybrid approach combining handcrafted features and a neural network, which achieved superior performance on the MHealth, USCHAD, and UTD-MHAD datasets compared to existing top-performing methods.
Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.