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Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults.

ABSTRACT: BACKGROUND:A robotic exoskeleton device is an intelligent system designed to improve gait performance and quality of life for the wearer. Robotic technology has developed rapidly in recent years, and several robot-assisted gait devices were developed to enhance gait function and activities of daily living in elderly adults and patients with gait disorders. In this study, we investigated the effects of the Gait-enhancing Mechatronic System (GEMS), a new wearable robotic hip-assist device developed by Samsung Electronics Co, Ltd., Korea, on gait performance and foot pressure distribution in elderly adults. METHODS:Thirty elderly adults who had no neurological or musculoskeletal abnormalities affecting gait participated in this study. A three-dimensional (3D) motion capture system, surface electromyography and the F-Scan system were used to collect data on spatiotemporal gait parameters, muscle activity and foot pressure distribution under three conditions: free gait without robot assistance (FG), robot-assisted gait with zero torque (RAG-Z) and robot-assisted gait (RAG). RESULTS:We found increased gait speed, cadence, stride length and single support time in the RAG condition. Reduced rectus femoris and medial gastrocnemius muscle activity throughout the terminal stance phase and reduced effort of the medial gastrocnemius muscle throughout the pre-swing phase were also observed in the RAG condition. In addition, walking with the assistance of GEMS resulted in a significant increase in foot pressure distribution, specifically in maximum force and peak pressure of the total foot, medial masks, anterior masks and posterior masks. CONCLUSION:The results of the present study reveal that GEMS may present an alternative way of restoring age-related changes in gait such as gait instability with muscle weakness, reduced step force and lower foot pressure in elderly adults. In addition, GEMS improved gait performance by improving push-off power and walking speed and reducing muscle activity in the lower extremities. TRIAL REGISTRATION:NCT02843828 .

SUBMITTER: Lee SH

PROVIDER: S-EPMC5706419 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults.

Lee Su-Hyun SH Lee Hwang-Jae HJ Chang Won Hyuk WH Choi Byung-Ok BO Lee Jusuk J Kim Jeonghun J Ryu Gyu-Ha GH Kim Yun-Hee YH

Journal of neuroengineering and rehabilitation 20171128 1

<h4>Background</h4>A robotic exoskeleton device is an intelligent system designed to improve gait performance and quality of life for the wearer. Robotic technology has developed rapidly in recent years, and several robot-assisted gait devices were developed to enhance gait function and activities of daily living in elderly adults and patients with gait disorders. In this study, we investigated the effects of the Gait-enhancing Mechatronic System (GEMS), a new wearable robotic hip-assist device ...[more]

PMID: 29183379

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Project description:BackgroundRobot-assisted ankle-foot-orthosis (AFO) can provide immediate powered ankle assistance in post-stroke gait training. Our research team has developed a novel lightweight portable robot-assisted AFO which is capable of detecting walking intentions using sensor feedback of wearer's gait pattern. This study aims to investigate the therapeutic effects of robot-assisted gait training with ankle dorsiflexion assistance.MethodsThis was a double-blinded randomized controlled trial. Nineteen chronic stroke patients with motor impairment at ankle participated in 20-session robot-assisted gait training for about five weeks, with 30-min over-ground walking and stair ambulation practices. Robot-assisted AFO either provided active powered ankle assistance during swing phase in Robotic Group (n = 9), or torque impedance at ankle joint as passive AFO in Sham Group (n = 10). Functional assessments were performed before and after the 20-session gait training with 3-month Follow-up. Primary outcome measure was gait independency assessed by Functional Ambulatory Category (FAC). Secondary outcome measures were clinical scores including Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), Timed 10-Meter Walk Test (10MWT), Six-minute Walk Test (SMWT), supplemented by gait analysis. All outcome measures were performed in unassisted gait after patients had taken off the robot-assisted AFO. Repeated-measures analysis of covariance was conducted to test the group differences referenced to clinical scores before training.ResultsAfter 20-session robot-assisted gait training with ankle dorsiflexion assistance, the active ankle assistance in Robotic Group induced changes in gait pattern with improved gait independency (all patients FAC ≥ 5 post-training and 3-month follow-up), motor recovery, walking speed, and greater confidence in affected side loading response (vertical ground reaction force + 1.49 N/kg, peak braking force + 0.24 N/kg) with heel strike instead of flat foot touch-down at initial contact (foot tilting + 1.91°). Sham Group reported reduction in affected leg range of motion (ankle dorsiflexion - 2.36° and knee flexion - 8.48°) during swing.ConclusionsRobot-assisted gait training with ankle dorsiflexion assistance could improve gait independency and help stroke patients developing confidence in weight acceptance, but future development of robot-assisted AFO should consider more lightweight and custom-fit design.Trial registrationClinicalTrials.gov NCT02471248 . Registered 15 June 2015 retrospectively registered.

Project description:IntroductionRobot-assisted gait training has been reported to improve gait in individuals with hemiparetic stroke. Ideally, the gait training program should be customized based on individuals' gait characteristics and longitudinal changes. However, a gait robot that uses gait characteristics to provide individually tailored gait training has not been proposed. The new gait training robot, "Welwalk WW-2000," permits modification of various parameters, such as time and load of mechanical assistance for a patient's paralyzed leg. The robot is equipped with sensors and a markerless motion capture system to detect abnormal hemiparetic gait patterns during robot-assisted gait training. Thus, it can provide individually tailored gait training. This study aimed to investigate the criterion validity of the gait analysis system in the Welwalk WW-2000 in healthy adults.Materials and methodsTwelve healthy participants simulated nine abnormal gait patterns that were often manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform a total of 36 gait trials, with four levels of severity for each abnormal gait pattern. Fifteen strides for each gait trial were recorded using the markerless motion capture system in the Welwalk WW-2000 and a marker-based three-dimensional (3D) motion analysis system. The abnormal gait pattern index was then calculated for each stride from both systems. The correlation of the index values between the two methods was evaluated using Spearman's rank correlation coefficients for each gait pattern in each participant.ResultsUsing the participants' index values for each abnormal gait pattern obtained using the two motion analysis methods, the median Spearman's rank correlation coefficients ranged from 0.68 to 0.93, which corresponded to moderate to very high correlation.ConclusionThe gait analysis system in the Welwalk WW-2000 for real-time detection of abnormal gait patterns during robot-assisted gait training was suggested to be a valid method for assessing gait characteristics in individuals with hemiparetic stroke.Clinical trial registration[https://jrct.niph.go.jp], identifier [jRCT 042190109].

Project description:Many power-assist wearable exoskeletons have been developed to provide walking support and gait rehabilitation for elderly subjects and gait-disorder patients. Most designers have focused on a direct power-assist to the wearer's lower limbs. However, gait is a coordinated rhythmic movement of four limbs controlled intrinsically by central pattern generators, with the upper limbs playing an important role in walking. Maintaining a normal gait can become difficult as a person ages, because of decreases in limb coordination, stride length, and gait speed. It is known that coordination mechanisms can be governed by the principle of mutual entrainment, in which synchronization develops through the interaction between nonlinear phase oscillators in biological systems. This principle led us to hypothesize that interactive rhythmic stimulation to upper-limb movements might compensate for the age-related decline in coordination, thereby improving the gait in the elderly. To investigate this hypothesis, we developed a gait-assist wearable exoskeleton that employs interactive rhythmic stimulation to the upper limbs. In particular, we investigated the effects on spatial (i.e., hip-swing amplitude) and temporal (i.e., hip-swing period) gait parameters by conducting walking experiments with 12 healthy elderly subjects under one control condition and five upper-limb-assist conditions, where the output motor torque was applied at five different upper-limb swing positions. The results showed a statistically significant increase in the mean hip-swing amplitude, with a mean increment of about 7% between the control and upper-limb-assist conditions. They also showed a statistically significant decrease in the mean hip-swing period, with a mean decrement of about 2.3% between the control and one of the upper-limb-assist conditions. Although the increase in the hip-swing amplitude and the decrease in the hip-swing period were both small, the results indicate the possibility that interactive rhythmic stimulation to the upper limbs might have a positive effect on the gait of the elderly.

Project description:BackgroundMany patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users' movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total).MethodsTo predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification.ResultsTesting five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of [Formula: see text] and [Formula: see text] are achieved on testing using LOPOCV and test data ([Formula: see text], participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = [Formula: see text]).ConclusionsA neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.

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Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults.

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Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults.

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