Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

Understand

Human activity recognition (HAR) has become a popular topic in research because of its wide application.

• With the development of deep learning, new ideas have appeared to address HAR problems.
• Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) cells is proposed.
• The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state).