Deep Ontology-Based Human Locomotor Activity Recognition System via Multisensory Devices

Recognition of human locomotor activities is crucial for monitoring the motion patterns. Current studies for human locomotor activities recognition focused on detecting basic motion patterns. In this study, we proposed a four-modules-based human locomotor recognition model via deep learning, which will support in identifying two signal patterns including static and kinematic motions and classifying the daily activities across different subjects. These motion patterns have been monitored through visual devices along with physical and ambient sensors to extract the complex and basic motion from distinct data forms. The four modules include processing, extraction, optimization, and recognition. Each module focuses on certain processing elements for human locomotion recognition. The processing module represents the pre-processing and segmentation stages for motion and ambient-based data along with extraction of human skeleton points from the visual data. Next, the extraction phase focuses on motion patterns identification and features extraction from the multisensors-based data. Then, the optimization module helps in prominent features selection via genetic algorithm. Furthermore, the recognition module utilized a deep learning technique called hidden Markov model to detect human locomotor activities. The average accuracy rates of 73.05% and 71.14% have been achieved for high-level and atomic-level activities over both datasets. The experimental results have shown that the proposed model outperforms the conventional multisensory systems based on deep classifiers via confidence levels for each skeleton point extracted.