The implementation of real-time gait mode detection is paramount in effectively providing tailored support for individuals utilizing actuated ankle-foot orthoses (AAFOs), thereby enhancing their walking capabilities and overall mobility. However, existing systems often rely on multiple sensors and struggle with accurate and prompt detection of gait transitions, especially in varied and challenging environments. This study aims to develop a novel real-time gait mode detection system that accurately identifies five essential daily living gait modes, namely level walking, ramp ascent/descent, and stair ascent/descent using only two foot-mounted inertial measurement units (IMUs). By using a long short-term memory (LSTM)-based algorithm trained on data collected from ten healthy subjects, the system extracts six kinematic features to predict gait modes with high accuracy. The proposed method integrates this detection system with a task-oriented control strategy to adapt the control of the AAFO based on the identified gait modes. The real-time experiments involving three healthy participants demonstrated robust gait mode detection, achieving an average estimation accuracy of$98 \pm 1$% across the five gait modes, even with the application of assistive torque. In cases mimicking abnormal gait, the system maintained an accuracy of$93 \pm 3$%. Additionally, each transition delay between gait modes was analyzed, showing that gait mode detection can occur between the transitions of the leading and trailing foot. The results of the control strategy showed a reduction in muscle activation of the dorsiflexor and plantarflexor muscles as measured by EMG, as well as improved tracking performance during the swing phase. Gait mode detection robustness was further evaluated by including walking with obstacles and changes in environmental dimensions.

This paper introduces a novel funnel-based adaptive ${{\mathcal{L}}_1}$ fuzzy control strategy for assisting ankle joint movement during walking with the use of an actuated ankle foot orthosis (AAFO). A projection-based adaptation mechanism employing a fuzzy system is used to estimate the unknown time-varying parameters of the ${{\mathcal{L}}_1}$ control law, ensuring precise tracking of the AAFO-wearer system by the state estimator. The projection operator guarantees the convergence of the parameters while offering a limited amount of assistance torque. Funnel-based feedback control is used to mitigate the typical time lag seen when using ${{\mathcal{L}}_1}$-based approaches due to the presence of a low-pass filter commonly used in this type of approach. The effectiveness of the proposed control strategy is demonstrated through real-time experiments involving five healthy subjects.

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement.

As a life-threatening disease, stroke can lead to long-term problems affecting the patients’ daily living ability. A common problem facing post-stroke patients is foot drop. An emerging modality of interest for correcting the foot drop is to combine both actuated ankle-foot orthosis (AAFO) and functional electrical stimulation (FES). Such hybrid assistive system not only ensure effective assistance but also can avoid fast muscular fatigue due to excessive muscular stimulation. Due to the significant changes in the ankle joint’s kinematics and kinetics with gait cycles, optimization control strategies for hybrid AAFO and FES systems are highly demanded. However, it is challenging to develop accurate gait phase detection algorithms to guide the control of AAFO and FES while ensuring robustness with respect to the diversity and variability of patients’ gaits. In this paper, we present a novel swing sub-phase detection algorithm based on a moving average convergence divergence (MACD) indicator. The proposed detection algorithm uses only information collected from the affected leg by means of two inertia measurement units (IMU) and the AAFO. Moreover, a gait-phase based control strategy is developed to optimize the assistive effect of a hybrid AAFO and FES system. Experimental results with five healthy show the potential of the proposed approaches in ensuring both satisfactory ankle joint trajectory tracking and effective reduction in stimulation intensity, compared to the use of conventional FES assistance.