Activity Recognition of Delivery Personnel by Integrating LSTM and CNN on Wearable Sensory Data
Apei Li, Linhui Cheng, Xiao Zhang, Yuchen Wang · 2024
Activity recognition for delivery personnel is growing in significance, as it empowers the quantification and evaluation of their performance by learning their operational behavior related to energy expenditure. This paper presents a deep learning model integrating LSTM and CNN, automating feature extraction and refining activity classification. LSTM captures temporal dependencies, while CNN handles spatial sensitivity, enabling the model to identify complex patterns. The model has demonstrated impressive accuracy, reinforcing its position as a tool for enhancing efficiency and well-being in logistics distribution.