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The University of Dhaka mobility data set (DU-MD) is a human action recognition (HAR) data set consisting of 10 classes and 5000 observations from 50 subjects recorded using wrist-mounted sensors embracing accelerometry. The data set exhibits sufficient statistical diversity in physiological parameters and a noteworthy correlation between similar activities with coveted quantitative and qualitative features, suitable for training machine learning models. On the other hand, the wrist-mounted approach parallels the future commercial scenarios. In this paper, we explore how the quantitative features of the DU-MD have been extracted and selected. Existing machine learning models used in HAR, in particular, support vector machines, ensemble of classifiers, and subspace K-nearest neighbours have been applied to our data set for activity and fall classification, with outcomes being compared with benchmark and similar data sets. With a HAR classification accuracy of 93%, fall detection accuracy of 97% and fall classification of 68.3%, quantitative performance metrics have either approached or outperformed other data sets, making this data set suitable for application in hardware-independent healthcare monitoring systems. Finally, we construct an algorithm with our data set based on performance metrics, and suggest some strategies for large-scale commercial implementation.

Feature Extraction, Performance Analysis and System Design Using the DU Mobility Dataset