Intelligent Assistive Knee Orthotic Device Utilizing Pneumatic Artificial Muscles
Thesis DisciplineMechanical Engineering
Degree GrantorUniversity of Canterbury
Degree NameDoctor of Philosophy
This thesis presents the development and experimental testing of a lower-limb exoskeleton system. The device supplies assistive torque at the knee joint to alleviate the loading at the knee, and thus reduce the muscular effort required to perform activities of daily living. The hypothesis is that the added torque would facilitate the execution of these movements by people who previously had limited mobility. Only four specific movements were studied: level-waking, gradient-walking, sit-to-stand-to-sit and ascending stairs.
All three major components of the exoskeleton system, i.e. the exoskeleton actuators and actuator control system, the user intention estimation algorithm, and the mechanical construction of the exoskeleton, were investigated in this work. A leg brace was fabricated in accordance with the biomechanics of the human lower-limb. A single rotational degree of freedom at the knee and ankle joints was placed to ensure that the exoskeleton had a high kinematic compliance with the human leg. The position of the pneumatic actuators and sensors were also determined after significant deliberation. The construction of the device allowed the real-world testing of the actuator control algorithm and the user intention estimation algorithms. Pneumatic artificial muscle actuators, that have high power to weight ratio, were utilized on the exoskeleton. An adaptive fuzzy control algorithm was developed to compensate for the inherent nonlinearities in the pneumatic actuators. Experimental results confirmed the effectiveness of the adaptive controller.
The user intention estimation algorithm is responsible for interpreting the user's intended movements by estimating the magnitude of the torque exerted at the knee joint. To accomplish this, the algorithm utilizes biological signals that emanate from the knee extensor and flexor muscles when they are activated. These signals combined with the knee angle data are used as inputs to the estimation algorithm. The output is the magnitude and direction of the estimated torque. This value is then scaled by an assistance ratio, which determines the intensity of the assistive torque provided to the user. The experiments conducted verify the robustness and predictability of the proposed algorithms.
Finally, experimental results from the four activities of daily living, affirm that the desired movements could be performed successfully in cooperation with the exoskeleton. Furthermore, muscle activity recorded during the movements show a reduction in effort when assisted by the exoskeleton.