Training for Walking Efficiency With a Wearable Hip-Assist Robot in Patients With Stroke: A Pilot Randomized Controlled Trial.

Background and Purpose- The purpose of this study was to investigate the effects of gait training with a newly developed wearable hip-assist robot on locomotor function and efficiency in patients with chronic stroke. Methods- Twenty-eight patients with stroke with hemiparesis were initially enrolled, and 26 patients completed the randomized controlled trial (14 in the experimental and 12 in the control groups). The experimental group participated in a gait training program over a total of 10 sessions, including 5 treadmill sessions and 5 over-ground gait training sessions while wearing a hip-assist robot, the Gait Enhancing and Motivating System (GEMS, Samsung Advanced Institute of Technology, Suwon, Republic of Korea). The control group received gait training without Gait Enhancing and Motivating System. Primary outcome measured locomotor function and cardiopulmonary metabolic energy efficiency. Also, secondary outcome measured motor function and balance parameter. Results- Compared with the control group, the experimental group had significantly greater improvement in spatiotemporal gait parameters and muscle efforts after the training intervention (P<0.05). The net cardiopulmonary metabolic energy cost (mL·kg-1·min-1) was also reduced by 14.71% in the experimental group after the intervention (P<0.01). Significant group×time interactions were observed for all parameters (P<0.05). Cardiopulmonary metabolic efficiency was strongly correlated with gait symmetry ratio in the experimental group (P<0.01). Conclusions- Gait training with Gait Enhancing and Motivating System was effective for improving locomotor function and cardiopulmonary metabolic energy efficiency during walking in patients with stroke. These findings suggest that robotic locomotor training can be adopted for rehabilitation of patients with stroke with gait disorders. Clinical Trial Registration- URL: https://clinicaltrials.gov. Unique identifier: NCT02843828.

RNN-Based On-Line Continuous Gait Phase Estimation from Shank-Mounted IMUs to Control Ankle Exoskeletons.

Several research groups have developed and studied powered ankle exoskeletons to improve energetics of healthy subjects and the mobility of elderly subjects, or to reduce asymmetry in gaits induced by strokes. To achieve optimal effect, the timing of assistive torque has been proved to be of crucial importance. Previous studies estimated the onset timings mostly by extrapolating the time horizon from past gait events observed with sensors. Such methods have inherently limited performance when subjects are not walking at steady frequencies. To overcome such limitation and allow the use of exoskeletons in various scenarios in a daily life, we propose to estimate the gait phase as a continuous variable progressing over a gait cycle, hence allowing immediate response to frequency changes rather than iteratively correcting it after each cycle. Our method uses recurrent neural networks to estimate gait phases out of an inertial measurement unit (IMU) every 10 ms. By replacing foot sensors with an IMU we can obtain rich enough information to estimate gait phase continuously as well as avoid physical damage in sensors from ground impacts. Our preliminary tests with 2 healthy subjects showed qualitatively positive outcomes regarding the gait phase estimation and the assistive torque control.

Compact Hip-Force Sensor for a Gait-Assistance Exoskeleton System.

In this paper, we propose a compact force sensor system for a hip-mounted exoskeleton for seniors with difficulties in walking due to muscle weakness. It senses and monitors the delivered force and power of the exoskeleton for motion control and taking urgent safety action. Two FSR (force-sensitive resistors) sensors are used to measure the assistance force when the user is walking. The sensor system directly measures the interaction force between the exoskeleton and the lower limb of the user instead of a previously reported force-sensing method, which estimated the hip assistance force from the current of the motor and lookup tables. Furthermore, the sensor system has the advantage of generating torque in the walking-assistant actuator based on directly measuring the hip-assistance force. Thus, the gait-assistance exoskeleton system can control the delivered power and torque to the user. The force sensing structure is designed to decouple the force caused by hip motion from other directional forces to the sensor so as to only measure that force. We confirmed that the hip-assistance force could be measured with the proposed prototype compact force sensor attached to a thigh frame through an experiment with a real system.

Autonomous hip exoskeleton saves metabolic cost of walking uphill.

We have developed a hip joint exoskeleton to boost gait function in the elderly and rehabilitation of post-stroke patients. To quantitatively evaluate the impact of the power and mass of the exoskeleton, we measured the metabolic cost of walking on slopes of 0, 5, and 10% grade, once not wearing the exoskeleton and then wearing it. The exoskeleton reduced the metabolic cost by 13.5,15.5 and 9.8% (31.9, 51.6 and 45.6 W) at 0, 5, and 10% grade, respectively. The exoskeleton performance index was computed as 0.97, 1.24, and 1.24 at each grade, implicating that the hip exoskeleton was more effective on slopes than level ground in saving the metabolic cost.

Effects of assistance timing on metabolic cost, assistance power, and gait parameters for a hip-type exoskeleton.

There are many important factors in developing an exoskeleton for assisting human locomotion. For example, the weight should be sufficiently light, the assist torque should be high enough to assist joint motion, and the assistance timing should be just right. Understanding how these design parameters affect overall performance of a complex human-machine system is critical for the development of these types of systems. The present study introduces an assistance timing controller that regulates assistance timing such that peak joint velocity and peak assistance power are offset by a reference value for our hip-type exoskeleton. This is followed by measuring the manner in which various assistance timing references affect an important metric for performance, namely metabolic cost. The results indicate that net metabolic cost exhibits a concave up pattern with the most reduction of 21%, when compared to walking without the exoskeleton, at 0% assistance timing reference. The study also examines assistance timing's effect on gait parameters; increase in assistance timing reference increases step length, decreases cadence, and increases walk ratio (i.e. step length/cadence ratio) during treadmill walking.

A Wearable Hip Assist Robot Can Improve Gait Function and Cardiopulmonary Metabolic Efficiency in Elderly Adults.

The aims of this paper were to investigate the effectiveness of a newly developed wearable hip assist robot, that uses an active assist algorithm to improve gait function, muscle effort, and cardiopulmonary metabolic efficiency in elderly adults. Thirty elderly adults (15 males/ 15 females) participated in thispaper. The experimental protocol consisted of overground gait at comfortable speed under three different conditions: free gait without robot assistance, robot-assisted gait with zero torque (RAG-Z), and full RAG. Under all conditions, muscle effort was analyzed using a 12-channel surface electromyography system. Spatio-temporal data were collected at 120 Hz using a 3-D motion capture system with six infrared cameras. Metabolic cost parameters were collected as oxygen consumption per unit (ml/min/kg) and aerobic energy expenditure (Kcal/min). In the RAG condition, participants demonstrated improved gait function, decreased muscle effort, and reduced metabolic cost. Although the hip assist robot only provides assistance at the hip joint, our results demonstrated a clear reduction in knee and ankle muscle activity in addition to decreased hip flexor and extensor activity. Our findings suggest that this robot has the potential to improve stabilization of the trunk during walking in elderly adults.