A Multi-Label Based Physical Activity Recognition via Cascade Classifier

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 5; p. 2593
• Main Authors: Mo, Lingfei, Zhu, Yaojie, Zeng, Lujie
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26-02-2023; MDPI
• Subjects: Abdomen; Activity recognition; Algorithms; cascade classifier; Data mining; Datasets; Deep learning; Discriminant analysis; Exercise; human activity recognition; Labels; Machine learning; Musculoskeletal system; Neural networks; Pattern recognition; Performance evaluation; Physical fitness; Physiology; Power; Sensors; Smartphones; wearable devices; Wearable technology; cascade classifier; wearable devices; machine learning; human activity recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23052593

Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods.