Abstract:
Activity recognition has gained attention due to the rapid development of microelectromechanical sensors. Numerous human-centric applications in healthcare, security, and smart environments can benefit from an efficient human activity recognition system. In this paper, we demonstrate the use of a seismic sensor for human activity recognition. Traditionally, researchers have relied on handcrafted features to identify the target activity, but these features may be inefficient in complex and noisy environments. The proposed framework employs an autoencoder to map the activity into a compact representative descriptor. Further, an Artificial Neural Network (ANN) classifier is trained on the extracted descriptors. We compare the proposed framework with multiple machine learning classifiers and a state-of-the-art framework on different evaluation metrics. On 5-fold cross-validation, the proposed approach outperforms the state-of-the-art in terms of precision and recall by an average of 10.68 and 23.36%, respectively. We also collected a dataset to assess the efficacy of the proposed seismic sensor-based activity recognition. The dataset is collected in a variety of challenging environments, such as variable grass length, soil moisture content, and the passing of unwanted vehicles nearby.
