Machine learning comparative study for human posture classification using wearable sensors

Human posture classification plays important role in number of applications including elderly monitoring, workplace ergonomics, sleeping patterns studies, sports, fall detection, etc. Despite of the fact that the topic is well-studied in the literature, many studies utilise one to few models to investigate the classification reliability of different postures. In this paper we present a rich study of the problem with six primary machine learning algorithms and an overall of nine different models considered in training and testing the real world collected data of human subjects. In this study, six different postures are addressed namely: sleeping, sitting, standing, running, forward bending and backward bending. The study considered two categories of models, supervised and unsupervised learning algorithms. After intensive training and testing of all algorithms, multi-layer perceptron and K-Means outperformed other algorithms with an impressive classification accuracy of 99.88%.