Hybrid Approach for Lower Limb Joint Angle Estimation using Genetic Algorithm and Feed-forward Neural Network

In this study, we aim to develop a measurement system for evaluating walking ability in daily life. Health promotion is one of the most important tasks to improve quality of life and quality of community for elderly people. Disabilities related to loss of independence in performing activities of daily living can lead to their social isolation and loneliness that can induce immobility and depression, producing the vicious cycle. Various methods have been proposed to measure lower limb joint angles and positions by using wearable systems and motion capture systems. However, such systems are too expensive and big for elderly's daily self-monitoring. This paper presents a method of lower limb joint angle estimation using a Kinect sensor. The sensor has a built-in processor to detect joint positions. However, inverse kinematics problem is required to be addressed in order to derive the joint angles. We therefore propose a hybrid approach for lower limb joint angle estimation using genetic algorithm and feed forward neural network.