Design and Assist-as-Needed Control of an Intrinsically Compliant Robotic Orthosis for Gait Rehabilitation

Abstract

Neurologic injuries, such as stroke and spinal cord injuries (SCI), cause damage to neural systems and motor function, which results in lower limb impairment and gait disorders. Subjects with gait disorders require specific training to regain functional mobility. Traditionally, manual physical therapy is used for the gait training of neurologically impaired subjects which has limitations, such as the excessive workload and fatigue of physical therapists. The rehabilitation engineering community is working towards the development of robotic devices and control schemes that can assist during the gait training. The initial prototypes of these robotic gait training orthoses use conventional, industrial actuators that are either extremely heavy or have high endpoint impedance (stiffness). Neurologically impaired subjects often suffer from severe spasms. These stiff actuators may produce forces in response to the undesirable motions, often causing pain or discomfort to patients. The control schemes used by the initial prototypes of robotic gait training orthoses also have a limited ability to provide seamless, adaptive, and customized robotic assistance. This requires new design and control methods to be developed to increase the compliance and adaptability of these automated gait training devices. This research introduces the development of a new robotic gait training orthosis that is intrinsically compliant. Novel, assist-as-needed (AAN) control strategies are proposed to provide adaptive and customized robotic assistance to subjects with different levels of neurologic impairments. The new robotic gait training orthosis has six degrees of freedom (DOFs), which is powered by pneumatic muscle actuators (PMA). The device provides naturalistic gait pattern and safe interaction with subjects during gait training. New robust feedback control schemes are proposed to improve the trajectory tracking performance of PMAs. A dynamic model of the device and a human lower limb musculoskeletal model are established to study the dynamic interaction between the device and subjects. In order to provide adaptive, customized robot assisted gait training and to enhance the subject's voluntary participation in the gait training process, two new control schemes are proposed in this research. The first control scheme is based on the impedance control law. The impedance control law modifies the robotic assistance based on the human subject's active joint torque contributions. The levels of robot compliance can be selected by the physical therapist during the impedance control scheme according to the disability level and stage of rehabilitation of neurologically impaired subjects. The second control scheme is proposed to overcome the shortcomings of impedance control scheme and to provide seamless adaptive, AAN gait training. The adaptive, AAN gait training scheme is based on the estimation of the disability level of neurologically impaired subjects based on the kinematic error and adapts the robotic assistance accordingly. All the control schemes have been evaluated on neurologically intact subjects and the results show that these control schemes can deliver their intended effects. Rigorous clinical trials with neurologically impaired subjects are required to prove the therapeutic efficacy of the proposed robotic orthosis and the adaptive gait training schemes. The concept of intrinsically compliant robotic gait training orthosis, together with the trajectory tracking and impedance control of robotic gait training orthosis are the important contributions of this research. The algorithms and models developed in this research are applicable to the development of other robotic devices for rehabilitation and assistive purposes. The major contribution of the research lies in the development of a seamless, adaptive AAN gait training strategy. The research will help in evolving the field of compliant actuation of rehabilitation robots along with the development of new control schemes for providing seamless, adaptive AAN gait training.