Dataset Information

Immediate effects of hybrid assistive limb gait training on lower limb function in a chronic myelopathy patient with postoperative late neurological deterioration.

ABSTRACT:

Objective

The Hybrid Assistive Limb (HAL) has recently been used to treat movement disorders. Although studies have shown its effectiveness for chronic myelopathy, the immediate effects of HAL gait training on lower limb function have not been clarified. We conducted HAL gait training and examined its immediate effects on a 69-year-old man with re-deterioration of myelopathy in the chronic phase after surgery for compression myelopathy. The HAL intervention was performed every 4 weeks for 10 total sessions. Immediately before and after each session, we analyzed the patient's walking ability using the 10-m walk test. In the 4th HAL session, the gastrocnemius muscle activity was measured bilaterally using a synchronized motion capture-electromyogram system.

Results

The training effects became steady after the 2nd session. In sessions 2-10, the step length increased from 0.56 to 0.63 m (mean: 0.031 m) immediately after HAL training. The motion capture-electromyogram analyses showed that considerable amounts of gastrocnemius muscle activity were detected during the stance and swing phases before HAL training. During and immediately after HAL training, gastrocnemius activity during the swing phase was diminished. HAL gait training has an immediate effect for inducing a normal gait pattern with less spasticity in those with chronic myelopathy.

SUBMITTER: Kubota S

PROVIDER: S-EPMC8896224 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Immediate effects of hybrid assistive limb gait training on lower limb function in a chronic myelopathy patient with postoperative late neurological deterioration.

Kubota Shigeki S Kadone Hideki H Shimizu Yukiyo Y Koda Masao M Takahashi Hiroshi H Miura Kousei K Eto Fumihiko F Furuya Takeo T Sankai Yoshiyuki Y Yamazaki Masashi M

BMC research notes 20220304 1

<h4>Objective</h4>The Hybrid Assistive Limb (HAL) has recently been used to treat movement disorders. Although studies have shown its effectiveness for chronic myelopathy, the immediate effects of HAL gait training on lower limb function have not been clarified. We conducted HAL gait training and examined its immediate effects on a 69-year-old man with re-deterioration of myelopathy in the chronic phase after surgery for compression myelopathy. The HAL intervention was performed every 4 weeks fo ...[more]

PMID: 35246256

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Project description:Sensory ataxia due to posterior cord syndrome is a relevant, disabling condition in nontraumatic spinal cord dysfunction. Ataxic gait is a common symptom of sensory ataxia that restricts activities of daily living. A 70-year-old woman with severe sensory disturbance was diagnosed with intradural extramedullary spinal cord tumors found in the thoracic spine region (T8). Surgical management of the tumors was performed. The patient received gait training 20 days after surgery (postoperative acute phase) using a hybrid assistive limb (HAL). HAL is a wearable exoskeleton cyborg that provides real-time assistance to an individual for walking and limb movements through actuators mounted on the bilateral hip and knee joints. Walking ability was assessed using the 10 m walking test, which included evaluating walking speed, step length, and cadence in every session. To evaluate the immediate effects of HAL training, walking speed and step length were measured before and after the training in each session. During the 10 m walking test, gait kinematics and lower muscle activity were recorded using a motion capture system and wireless surface electromyography before the first session and after completion of all HAL sessions. After the HAL training sessions, improvement in the patient's gait performance was observed in the gait joint angles and muscle activity of the lower limb. After 10 training sessions, we observed the following changes from baseline: walking speed (from 0.16 m/s to 0.3 m/s), step length (from 0.19 m to 0.37 m), and cadence (from 50.9 steps/min to 49.1 steps/min). The average standard deviations of the knee (from right, 7.31; left, 6.75; to right, 2.93; p < 0.01, left, 2.63; p < 0.01) and ankle joints (from right, 6.98; left, 5.40; to right, 2.39; p < 0.01, left, 2.18; p < 0.01) were significantly decreased. Additionally, walking speed and step length improved immediately after completing all the HAL training sessions. This suggests that HAL gait training might be a suitable physical rehabilitation program for patients with sensory ataxia causing dysfunctional movement of the lower limb.

Project description:IntroductionThe wearable cyborg Hybrid Assistive Limb (HAL) is a therapeutic exoskeletal device that provides voluntary gait assistance using kinematic/kinetic gait data and bioelectrical signals. By utilizing the gait data automatically measured by HAL, we are developing a system to analyze the wearer's gait during the intervention, unlike conventional evaluations that compare pre- and post-treatment gait test results. Despite the potential use of the gait data from the HAL's sensor information, there is still a lack of analysis using such gait data and knowledge of gait patterns during HAL use. This study aimed to cluster gait patterns into subgroups based on the gait data that the HAL automatically collected during treatment and to investigate their characteristics.MethodsGait data acquired by HAL, including ground reaction forces, joint angles, trunk angles, and HAL joint torques, were analyzed in individuals with progressive neuromuscular diseases. For each measured item, principal component analysis was applied to the gait time-series data to extract the features of the gait patterns, followed by hierarchical cluster analysis to generate subgroups based on the principal component scores. Bayesian regression analysis was conducted to identify the influence of the wearer's attributes on the clustered gait patterns.ResultsThe gait patterns of 13,710 gait cycles from 457 treatments among 48 individuals were divided into 5-10 clusters for each measured item. The clusters revealed a variety of gait patterns when wearing the HAL and identified the characteristics of multiple sub-group types. Bayesian regression models explained the influence of the wearer's disease type and gait ability on the distribution of gait patterns to subgroups.DiscussionThese results revealed key differences in gait patterns related to the wearer's condition, demonstrating the importance of monitoring HAL-assisted walking to provide appropriate interventions. Furthermore, our approach highlights the usefulness of the gait data that HAL automatically measures during the intervention. We anticipate that the HAL, designed as a therapeutic device, will expand its role as a data measurement device for analysis and evaluation that provides gait data simultaneously with interventions, creating a novel cybernics treatment system that facilitates a multi-faceted understanding of the wearer's gait.

Project description:IntroductionThis study aimed to develop machine learning models to predict neurological outcomes in patients with degenerative cervical myelopathy (DCM) after surgical decompression and identify key factors that contribute to a better outcome, providing a reference for patient consultation and surgical decision-making.MethodsThis retrospective study reviewed 1,895 patients who underwent cervical decompression surgery for DCM at Peking University Third Hospital from 2011 to 2020, with 672 patients included in the final analysis. Five machine learning methods, namely, linear regression (LR), support vector machines (SVM), random forest (RF), XGBoost, and Light Gradient Boosting Machine (LightGBM), were used to predict whether patients achieved the minimal clinically important difference (MCID) in the improvement in the Japanese Orthopedic Association (JOA) score, which was based on basic information, symptoms, physical examination signs, intramedullary high signals on T2-weighted (T2WI) magnetic resonance imaging (MRI), and various scale scores. After training and optimizing multiple ML algorithms, we generated a model with the highest area under the receiver operating characteristic curve (AUROC) to predict short-term outcomes following DCM surgery. We evaluated the importance of the features and created a feature-reduced model. The model's performance was assessed using an external dataset.ResultsThe LightGBM algorithm performed the best in predicting short-term neurological outcomes in the testing dataset, achieving an AUROC value of 0.745 and an area under the precision-recall curve (AUPRC) value of 0.810. The important features influencing performance in the short-term model included the preoperative JOA score, age, SF-36-GH, SF-36-BP, and SF-36-PF. The feature-reduced LightGBM model, which achieved an AUROC value of 0.734, also showed favorable performance. Moreover, the feature-reduced model showed an AUROC value of 0.785 for predicting the MCID of postoperative JOA in the external dataset, which included 58 patients from other hospitals.ConclusionWe developed models based on machine learning to predict postoperative neurological outcomes. The LightGBM model presented the best predictive power regarding the surgical outcomes of DCM patients. Feature importance analysis revealed that variables, including age, preoperative JOA score, SF-36-PF, SF-36-GH, and SF-36-BP, were essential factors in the model. The feature-reduced LightGBM model, designed for ease of application, achieved nearly the same predictive power with fewer variables.

Project description:Study Design Retrospective study. Objective We previously reported that the long-term neurologic outcomes of C3-C6 laminoplasty for cervical spondylotic myelopathy (CSM) are satisfactory, with reduced frequencies of postoperative axial neck pain and kyphotic deformity. However, only 20 patients were included, which is a limitation in that study. The present study investigated the incidence of late neurologic deterioration (LND) of myelopathic symptoms after C3-C6 laminoplasty for CSM and attempted to identify significant risk factors for LND in a larger patient population. Methods Subjects comprised 137 consecutive patients with CSM who underwent C3-C6 laminoplasty (bilateral open-door laminoplasty, n = 85; unilateral open-door laminoplasty, n = 52) and were followed for >24 months (mean follow-up, 70 months; range, 25 to 124 months). The patients' medical records were examined for evidence of LND due to cervical myelopathy. The age at time of surgery, sex, surgical procedures, anteroposterior spinal canal diameter at the C7 level, type of C6 spinous process, pre- and postoperative C2-C7 angle, C3-C6 range of motion (ROM), and disk height at the C6-C7 level were analyzed to identify risk factors for LND. Results Three patients (2.2%) developed LND of myelopathic symptoms due to caudal segment pathology adjacent to the C3-C6 laminoplasty (LND group). In these three patients, mean Japanese Orthopaedic Association (JOA) score improved from 10.2 before surgery to 12.2 at the time of maximum recovery, and declined to 9.7 just before additional surgery. On the other hand, in 134 patients without LND (non-LND group), the mean JOA score significantly improved from 10.2 before surgery to 13.4 at the time of maximum recovery and was maintained by the final follow-up (13.2). Compared with the non-LND group, the LND group showed significantly smaller anteroposterior spinal canal diameter at C7, more restricted postoperative C3-C6 ROM, and greater postoperative decrease in disk height at C6-C7, although a logistic regression analysis showed no significant differences. Conclusions In patients with CSM with more severe developmental spinal canal stenosis at C7, accelerated degeneration at the caudal segment resulting from restricted C3-C6 ROM after C3-C6 laminoplasty might lead to LND.

Project description:BackgroundMany lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user.MethodsIn addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user's intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller.Results291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user's movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper.ConclusionsBy listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

Dataset Information

Immediate effects of hybrid assistive limb gait training on lower limb function in a chronic myelopathy patient with postoperative late neurological deterioration.

Objective

Results

Publications

Immediate effects of hybrid assistive limb gait training on lower limb function in a chronic myelopathy patient with postoperative late neurological deterioration.

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