Real-time measurement and gait detection algorithm for motion control of active ankle foot orthosis / Aminuddin Hamid

Human gaits are complicated motion that required synergy of muscle coordination, timing and balance. Normally, walking gait pattern is essentially a casual footstep, i.e. one foot moving forward and then followed by another foot stepping ahead of the first foot at the same distance. However, many people cannot walk with normal gait pattern due to neuromuscular disorder affecting the lower limbs leading to weakening of the dorsiflexor muscles of the foot and ankle. In order to support the weak muscle on Ankle Foot Orthosis (AFO) device is worn on the lower leg and foot to provide permanent assistance and control the motion of the foot from dragging during walking. But a typical AFO device is a passive one which tends to be rigid and fail to provide dorsi/plantar flexion motion during walking. The Active ankle foot orthosis (AAFO) has been developed to overcome this problem by assisting the motion of the ankle complex based on force controlled actuator. One of the challenges that researchers have to face with the development of AAFO is providing an efficient transmission and producing continuous and smooth gait cycle. This thesis proposes a real time gait phase detection system to control AAFO for rehabilitation and assist ankle motion. Therefore, the information of real-time human gait phase is important for active control of AFO motion. In this thesis, the real-time gait phase is obtained by measuring ground reaction force (GRF). The
proposed system consists of an AFO equipped with ball screw actuator that provides direct assistance to control ankle joint during dorsiflexion and plantarflexion motion. The position of the ball screw actuators is controlled by using motion controller based on the inputs received from three force sensors embedded in the insole and an encoder attached at the ankle joint. The data acquired from force sensors during walking condition is transferred to the host computer powered by LabVIEW software for visualization and analysis. From the result analysis, the developed control algorithm shows that the realtime GRF measurement has the ability to enhance the AAFO functional performance and improve the patient gait.