Distilling the knowledge from handcrafted features for human activity recognition

• Main Authors: Chen, Zhenghua, Zhang, Le, Cao, Zhiguang, Guo, Jing
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering; Human Activity Recognition; Deep Long Short-term Memory (LSTM) Network
• Online Access: https://hdl.handle.net/10356/86019; http://hdl.handle.net/10220/48267
• Institution: Nanyang Technological University

Human activity recognition is a core problem in intelligent automation systems due to its far-reaching applications including ubiquitous computing, health-care services, and smart living. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of hu...