The project proposed an image-based 3D human joint angle estimation system that was trained on accurate 3D joint angles for our daily activities. We built a new 3D joint angle dataset that provides videos captured by 28 digital cameras and accurate annotations of 3D joint angles and 3D joint positions extracted from a motion capture system. We assume that the system is provided just a single image from the frontal view or the side view, then is asked to estimate the 3D angles of the lower limb joints.

Publications: (Update soon)

• Classified three kinds of foot groups (Normal, abnormal, and athletes)

• Early detected senile disorders (Sarcopenia, cognition, depression, frailty, and falling experience).

Publications:

• [1] “Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks”, in IEEE Transactions on Neural Systems and Rehabilitation Engineering, doi: 10.1109/TNSRE.2020.2977049;

• [2] “IMU-based Spectrogram Approach with Deep Convolutional Neural Networks for Gait Classification”, 2020 ICCE (Las Vegas, USA);

• [3] “Walking-in-Place Characteristics-Based Geriatric Health Assessment Using Deep Convolutional Neural Networks”, 2020 EMBC (Canada);

• [4] “Deep Neural Network-based gait classification using wearable inertial sensor data”, 2019 EMBC, in Berlin, Germany;

Detected dangerous situations of elderly people in the bedroom based on 2D human body key points in images extracted by using the OpenPose algorithm. The system could raise a warning bell and send messages to their family members within 2-3 seconds.

The dangerous situation: Standing on the bed.