An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition

• Published in: The Journal of supercomputing Vol. 77; no. 11; pp. 12256 - 12279
• Main Authors: Semwal, Vijay Bhaskar, Gupta, Anjali, Lalwani, Praveen
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2021; Springer Nature B.V
• Subjects: Accuracy; Activity recognition; Artificial neural networks; Classification; Compilers; Computer Science; Datasets; Deep learning; Edge computing; Ensemble learning; Gait; Inertial platforms; Inertial sensing devices; Interpreters; Knee; Locomotion; Machine learning; Processor Architectures; Programming Languages; Scientific papers; Sensors; Thigh; Walking; Activity recognition; Hybrid deep learning; Wearable sensor; Ensemble learning; Gait analysis; Human robot interaction(HRI)

Recent advancements in edge computing devices motivate us to develop a sustainable and reliable technique for multiple gait activities recognition using wearable sensors. This research work presents the multitask human walking activities recognition using human gait patterns. Human locomotion is def...