Comparison of Sensor-Based Datasets for Human Activity Recognition in Wearable IoT

Human activity recognition (HAR) has made a significant impact in a variety of applications, not least of which includes wearable Internet of Things (IoT). Considerable research is currently underway in the field of machine learning, deep learning and neural networks, an objective of which is to improve the efficiency of relevant algorithms, as well as to increase the truthfulness of the recognition model. For purposes of analysis and consistency, much of the published research uses either self-collected sensor-based databases or publicly available databases for potential research objectives. There exist diverse open source sensor-based time-series datasets that are applied to the testing of algorithm efficiency, which is then used for human activity detection. Essential to algorithm performance testing are datasets that consist of balanced data and labels. Such datasets vary in size, labeling, accuracy, number of activities performed by each user, type of sensor data included, and various other elements. In this paper, we evaluate two popular datasets by comparing selected factors; we also demonstrate our experimental results. We implement minimal data processing by extracting five basic features - mean, standard deviation, absolute mean error, mean squared error, and histogram plots – and perform experiments on both datasets to study their outcomes. We compare the UCI HAR and WISDM (Wireless Sensor Data Mining) datasets, based on their accuracy, precision, recall, and F1-score on nine different machine learning algorithms. Both datasets contain tri-axial sensor data. Experimental results show that the UCI HAR dataset is the more promising of the two. Additionally, we show that with basic data processing, simple activity recognition is possible with current state-of-the-art algorithms. Specifically, as we increase the type of sensor data, the accuracy of simple human activity detection is improved. We conclude with a discussion of results, as well as study limitations, challenges, and future research.