Gait parameters, including walking speed and stride length, are crucial indicators of health status and rehabilitation progress for individuals using wearable robots for exercise or rehabilitation. These metrics play a crucial role in monitoring progress and adjusting training programs, thereby fostering greater engagement in the training. In this paper, we present methods for estimating walking speed and stride length using sensors in wearable hip exoskeleton GEMS-H. Our study collected data from 79 middle-aged healthy individuals walking on a treadmill while wearing GEMS-H under various assistance conditions. To estimate walking speed, we evaluated linear regression models, deep neural networks, and ensemble models using different combinations of joint encoders and an IMU in the GEMS-H hip exoskeleton to form various sets of features. The ensemble of deep neural networks using only 6-DOF IMU signals as features achieved the lowest root-mean-square error (RMSE) for walking speed estimation, which was 0.066 m/s. We also present an algorithm for real-time stride length estimation, building on one of the speed estimation models. The speed and stride length estimation model was tested on 12 middle-aged healthy subjects walking in GEMS-H overground, yielding an RMSE of 0.060 m/s for speed and 7.1 cm for stride length.

In this paper, we propose a new and simple control strategy for gait assistance with a hip exoskeleton robot. This controller is based on the time delayed, self-feedback known for stabilizing oscillatory systems under certain conditions. In this controller, there are no separate estimators for the gait phase nor the environment, yet the controller can be generalized to operate under various walking conditions (e.g., stair and ramp walking). We first define a state variable representing the current leg's movement with hip joint angles. A simple assistance control can be described in closed-loop form with the delayed state feedback. By assigning the appropriate time-delay and self-feedback gain, we can generate assistive torques stably under the interaction between human and exoskeleton. The controller provides immediate and smooth assistance to user movement by reflecting the change of leg motion at every control period. The proposed joint-angle-based delayed-feedback assistance controller can operate under various walking speeds and environmental changes (e.g., stairs and ramps) without the need for additional sensors, computational processing, and parameter adjustment. Using a simple leg swing model, we perform a stability analysis under a simplified condition to provide insights into the effects of the time-delayed feedback in oscillatory systems. Then, we experimentally validate the efficacy of the proposed assistance controller by measuring the metabolic energy expenditure for level treadmill walking. We also test and analyze the generated assistive torques and power under the different walking conditions to show the generalizability of the controller.

We developed a robotic lower-limb exoskeleton for those who have weakened muscle due to aging and experience difficulty in walking or getting up without help. The exoskeleton covering both limbs from the feet to the waist has 6 electric actuators in the hip abduction/adduction, hip extension/flexion and knee extension/flexion joints. For users with volitional motion, delivering assistance power according to their intention is a challenging task. We propose an adaptive oscillator-based controller to assist users walk in the lower-limb exoskeleton. To adapt to changes in walking speed and environment, motion command from the controller is modulated by estimate walking speed and walking environment recognized as one of the following categories: level ground, stairs up/down and slope up/down. Experimental results demonstrate the feasibility of the proposed environment recognition method and the impact of assistance on the metabolic cost of walking on level and inclined treadmills.

We propose a simulation framework for gait assistance with ankle pathologies. We first construct the neu-romuscular walking model, then design the parameters for assistance torques for stance and swing legs. The parameter values are determined by performing dynamic optimizations which takes into account the human-exoskeleton interactive dynamics. The simulated energy expenditure and kinematic data are compared with the real data. Case studies involve abnormal gaits with 1) foot drop, 2) foot drop and plantarflexion failure. We evaluate the gait efficiency and walking speed for the different gait types. Our result shows that each gait type should have a different assistance strategy (timing and magnitude) compared to the assistance strategy of a normal gait.

We have developed a hip exoskeleton for seniors with difficulties in walking due to muscle weakness. The exoskeleton is lightweight and moderate in assistance power compared to other hip exoskeletons in the literature. Its controller estimates user gait phase, walking speed, and ground inclinations to generate assistance torque adaptively. To assess the physiological effect of the gait assistance, we compared metabolic energy consumption for 5 adults for walking on a treadmill with and without the exoskeleton at the same speed: the exoskeleton reduced metabolic cost of walking by 13% (p = 0:0024). The step length and the stride time increased under the assistance. Our analysis for the result suggests that the efficiency of hip exoskeletons on saving metabolic energy can be twice as high as that of ankle exoskeletons possibly because muscle-tendon unit in the hip joint is less energy-efficient than in the ankle joint.

To control exoskeletons for walking gait assistance, it is of primary importance to control them to act synchronously with the gaits of users. To effectively estimate the gait cycle (or the phase within a stride) of users, we propose a new adaptive frequency oscillator (AFO). While previous AFOs successfully estimated the walking frequency from joint angles as inputs, the new AFO, called particularly-shaped adaptive oscillator (PSAO) can estimate gait cycle from the same inputs, which would have required foot contact sensors in previous approaches. To predict the effects of PSAO-based gait assistance on human walking, it has been tested with neuromuscular walking simulation. In the simulation, the gait assistance system reduced the metabolic cost of walking for some assistance patterns. The walk ratio (step length per step rate) also changed as assistance patterns shifted in phase, which is meaningful because metabolic cost of walking in general is minimal at specific walk ratio. For a prototype exoskeleton we developed, the effect of gait assistance was experimented on a human subject walking on level ground and inclining slopes to verify the predictions from the simulation: (1) physiological cost index computed from heart rate significantly decreased indicating reduction in metabolic energy expenditure; (2) walk ratio was in fact controllable to an extent.

We propose a control framework for dynamic bipedal locomotion with compliant joints. A novel 3D dynamic walking is achieved by utilizing natural dynamics of the system. It is done by 1) driving robot joints directly with the posture-based state machine and 2) controlling tendon-driven compliant actuators. To enlarge gait's basin attraction for stable walking, we also adaptively plan step-to-step motion and compensate stance/swing motion. Final joint input is described by a superposition of state machine control torques and compensation torques of balancers. Various walking styles are easily generated by composing straight and turning gait-primitives and such walking is effectively able to adapt on various environments. Our proposed method is applied to a torque controlled robot platform, Roboray. Experimental results show that gaits are able to traverse inclined and rough terrains with bounded variations, and the result gaits are human-like comparing the conventional knee bent walkers.

This paper gives an overview of the development of a novel biped walking machine for a humanoid robot, Roboray. This lower-limb robot is designed as an experimental system for studying biped locomotion based on force and torque controlled joints. The robot has 13 actuated DOF and torque sensors are integrated at all the joints except the waist joint. We designed a new tendon type joint modules as a pitch joint drive module, which is highly back-drivable and elastic. We also built a decentralized control system using the small controller boards named Smart Driver. The forward walking experiment with this lower limbs was conducted to test the mechanical structure and control system.

To achieve dynamic balancing and natural walking for a bipedal robot we propose a novel force-based control framework. Given 6-dimensional pose vector representing robot's posture and attitude, desired force and moment in the task space are computed. To generate the force and moment as desired, we propose the use of virtual gravity compensation (VGC), essentially a dynamic controller that outputs joint torques. By using the VGC-based balancing controller, the robot can maintain a desired pose stably even on a tilting plate. We also propose to extend the VGC-based balancing controller to implement a walking algorithm that controls the desired pose in terms of capture point using a finite state machine. The control algorithm was tested with torque-controlled humanoid platforms developed by our group to demonstrate robust and natural gaits under various walking environments. The robot walked robustly on irregular surfaces and recovered from external pushes. The robot also exhibited natural walking motions such as pendulum-like leg swings and heel-to-toe transitions, a characteristic feature of human gait, all without explicitly designating joint angle trajectories.

We propose a balancing control framework for a torque-controlled biped robot, Roboray. Roboray has two 6 DOF legs and torque sensors are integrated at all the leg joints. It has a new cable-driven joint module as a pitch joint drive, which is highly back-drivable and elastic. Using these hardware characteristics, we propose a new balancing control algorithm. This algorithm is the combination of gravity compensation, virtual gravity control and damping control. A friction compensation technique is also introduced in order to eliminate the nonlinearity of damping and to improve the performance of torque tracking. Our proposed method is applied to a simple inverted pendulum system and Roboray. Experimental results show that these two system keep their balance when they are pushed slightly.

Various forms of animal locomotion have been studied in the biological literature. Neuroscience research suggests the existence of central pattern generators (CPGs), neural networks that generate periodic signals for locomotion. We study simplified modular architectures based on CPGs for robotic applications, and show their global exponential stability using partial contraction analysis. The proposed architectures can reproduce periodic CPG signals for swimming or walking motion of various animals. They can be combined towards increasingly complex behaviors while preserving stability

Introduces an evolutionary computation (EC)-based identification method of a Takagi-Sugeno-Kang (TSK) fuzzy system constrained by a monotonic input-output relationship. The differentiation of a TSK fuzzy system output with respect to its input yields a sufficient condition of the fuzzy system parameters that makes the fuzzy system monotonic. By using the derived condition, we suggest a new EC-based fuzzy system identification method whose fuzzy model preserves monotonicity at every identification stage by means of modified representation and mutation paradigms. Simulation results show that the proposed identification technique is better than conventional methods in its convergence rate, generalization characteristic, and robustness.