Towards advancements in gait assistance: development and testing of a novel two degrees of freedom AI controlled ankle exoskeleton

Gait disorders, affecting millions globally, stem from neurological, orthopedic, and medical conditions, with age as a significant risk factor. Stroke, a leading cause of adult disability, often results in lower limb impairments that impact gait symmetry, stride, and efficiency, frequently causing drop foot and push-off deficiencies. Such disorders limit daily activities, increase health risks, and necessitate assistance, especially among older adults. Rehabilitation is essential for stroke recovery; however, it remains time-consuming and costly. Robotic systems, such as exoskeletons, present promising advancements in mobility assis- tance and rehabilitation by addressing specific impairments. Consequently, joint-targeted devices are emerging as specialized tools for both rehabilitation and assistance. The complexity of the ankle joint poses significant challenges, often resulting in kinematic oversimplifications that limit its physiological functionality. In response, we developed a novel two-degree-of-freedom (DoF) ankle exoskeleton that provides active assistance for dor- siflexion and plantarflexion while allowing passive inversion and eversion through a custom joint mechanism. Three joint configurations were initially designed to evaluate how differ- ent kinematic setups influence force exchange at the user-device interface. An experimental trial with ten healthy subjects demonstrated significant differences in maximum pressure variation across the gait cycle among the joint configurations. An AI-based control algorithm was also developed, employing a convolutional recurrent neural network (CRNN) to estimate the continuous gait phase (GP) based on signals from a single inertial measurement unit (IMU). The neural model was validated in 14 healthy subjects and compared with a previously validated algorithm in detecting four main gait events: heel strike, mid stance, toe off, and mid swing. The trained model for continuous GP estimation was then used to compute control torque, with the exoskeleton programmed to assist with 12% of the user’s body weight in Newton meters (Nm). The device’s performance was evaluated with twelve healthy subjects. Lower limb kinematics were captured using an IMU, while muscle activity from twelve muscles in the right leg was recorded via surface electromyography (EMG) during a treadmill walking task at three different speeds. The study assessed kinematic impact using statistical parametric mapping (SPM) to compare joint movements with and without the exoskeleton. Additionally, EMG curve area variations were analyzed to evaluate changes in muscle activation. The results demonstrated that the exoskeleton significantly impacted joint kinematics during the push-off phase. Specifically, the ankle flexion angle increased during terminal swing, indicating enhanced dorsiflexion provided by the device. EMG analysis revealed a significant reduction in the activation of the soleus and medial gastrocnemius muscles during push-off, while an increase in activation was observed in the biceps femoris and tibialis anterior during the same phase.