Wearable robotic devices have been demonstrated to reduce muscle activation and metabolic cost during walking, but conventional motorized systems often impose significant weight and bulk, leading to user discomfort and limited portability. To address these limitations, the Robotic Achilles Tendon (RAT) was developed as a lightweight, semi-passive spring system that delivers ankle assistance exclusively during the stance phase. The RAT integrates a double-acting pneumatic cylinder and a solenoid valve to emulate spring behavior when the valve is closed and to permit unrestricted ankle motion when the valve is open. Gait-phase detection is achieved via a single inertial measurement unit mounted on the wrist, exploiting the conserved angular momentum that couples arm and leg movements. System architecture was optimized by eliminating motors and minimizing sensor count, resulting in a device weight of 0.45 kg per leg and a total weight of 1.4 kg. Performance evaluation involved surface electromyography and metabolic cost measurements in a cohort of healthy young adults. Compared to unassisted walking, the RAT reduced plantar-flexor muscle activation by 16.9% and decreased metabolic cost by 10.6%. These findings confirm that intent-based actuation of a semi-passive spring can provide effective ankle assistance with minimal hardware complexity. Future work will investigate alternative sensor locations that remain synchronized with lower-limb kinematics, simplify battery and processing modules to further reduce device mass, and extend validation to elderly and pediatric populations.

Accurate identification of gait phases is critical in effectively assessing the assistance provided by lower limb exoskeletons. In this study, we propose a novel gait phase recognition system called ObsNet to analyze the gait of individuals with spinal cord injuries (SCI). To ensure the reliable use of exoskeletons, it is essential to maintain practicality and avoid exposing the system to unnecessary risks of fatigue, inaccuracy, or incompatibility with human-centered devices. Therefore, we propose a new approach to characterize exoskeletal-assisted gait by estimating forces on exoskeletal joints during walking. Although these estimated forces are potentially useful for detecting gait phases, their nonlinearities make it challenging for existing algorithms to generalize accurately. To address this challenge, we introduce a data-driven model that simultaneously captures both feature extraction and order dependencies, and enhance its performance through a threshold-based compensational method to filter out momentary errors. We evaluated the effectiveness of ObsNet through robotic walking experiments with two practical users with complete paraplegia. Our results indicate that ObsNet outperformed state-of-the-art methods that use joint information and other recurrent networks in identifying the gait phases of individuals with SCI ($\boldsymbol{p}< \mathbf{0.05}$). We also observed reliable imitation of ground truth after compensation. Overall, our research highlights the potential of wearable technology to improve the daily lives of individuals with disabilities through accurate and stable state assessment.

Wearable robots have been developed to aid or substitute the gait locomotion of humans. To assist gait locomotion based on the intention of a wearer, a gait pattern analysis is required with a wearable sensor by measuring body information, i.e., a joint angular velocity. However, measuring a precise joint angular velocity is difficult because the attachment position of a sensor has a curvature and an anatomical joint axis which is invisible. Therefore, a sensor calibration algorithm, which aligns a sensor axis into an anatomical joint axis, is required to provide an optimal assist for a wearer. Hence, in this paper, a new and simple sensor calibration algorithm is proposed with a unit sensor. Since a wearer shakes the body or collides with the ground when walking, the attachment position of a sensor may be changed. Thus, a continuous sensor compensation algorithm is also proposed. Additionally, the effectiveness of this new algorithm is demonstrated by gait locomotion experiments on various paths.

Powered exoskeletons for people with complete paraplegia have been controlled based on predefined joint-reference trajectories. As the target users of such robots may not realize any voluntary movement, the human body is fully constrained and follows the movement of the powered exoskeleton joints. The predefined gait pattern, however, may or may not be adequate for every user because the gait pattern is resulting from complex interactions between the body segments and environment, as well as dynamic characteristics of the body segments. As all the persons and their body segments have different dynamic characteristics, therefore, a bespoke tuning of gait parameters is necessary in order to realize the natural gait motion, which is optimal for each user. In this article, an adaptive gait pattern adjustment method is proposed. The proposed method observes the ground contact timing, which is directly related to the adequacy of the gait pattern for the user wearing a robot. Based on the ground contact timing, the joint-reference trajectories are adjusted, which are parameterized by the trunk inclination angle. The proposed method iteratively calculates an appropriate trunk inclination angle from the information of ground contact timing. In this article, the derivation of the proposed method and its experimental verification with WalkON Suit, a powered exoskeleton, are introduced. The proposed algorithm successfully worked for two practical users with complete paraplegia, and the adapted gait patterns showed excessive performance in walking speed, oxygen consumption, palm force on crutches, etc. The results were also verified by winning both gold and bronze medals in the global competition, Cybathlon 2020, while accomplishing the best records among all the teams.

Powered exoskeletons for people with paraplegia have been widely developed. To generate the basic but essential motions for daily human life, precise control algorithms to follow the joint reference trajectories are necessary. The dynamic characteristics of the exoskeletal joints, however, varies signifi-cantly during walking because the load side is exchanged from legs in the air to the wearer’s body. To ensure robustness and tracking performance for any case of gait even in the presence of exogenous disturbances such as human’s active movements and repeated ground contacts, customized robust control algorithms need to be developed. In this paper, therefore, hybrid model control of the powered exoskeleton, WalklON Suit, utilized with the disturbance observer is introduced. A hybrid nominal model, whose model parameters are interchanged between the gait phases, i.e., swing and stance, is developed by the parameter adatpation algorithm. By the proposed method, the disturbance observer can fully reject the exogenous disturbance during walking and achieve high-performance gait assistance to the people with complete paraplegia. In this paper, the experimental verification of the designed model and the controller with the WalkON Suit, are also introduced.

Several powered exoskeletons have been developed and commercialized to assist people with complete spinal cord injury. For motion control of a powered exoskeleton, a normal gait pattern is often applied as a reference. However, the physical ability of paraplegics and the degrees of freedom of powered exoskeletons are totally different from those of people without disabilities. Therefore, this paper introduces a novel gait pattern depart from the normal gait, which is proper to the paraplegics. Since a human is included, the system of the powered exoskeleton has lots of motion uncertainties that may not be perfectly predicted resulting from different physical properties of paraplegics (SCI level, muscular strength of the upper body, body parameters, inertia), actions from crutches (position and timing to put), several types of training (period, methodology), etc. Then, to find a stable and safe gait pattern adapted to the individual user, an iterative way to compensate the gait pattern is also required. In this paper, human iterative learning algorithm, which utilizes the accumulated data during walking to adjust the gait trajectories is proposed. Additionally, the effectiveness of the proposed gait pattern is verified by human walking experiments.