Dataset Information

Reshaping of Gait Coordination by Robotic Intervention in Myelopathy Patients After Surgery.

ABSTRACT: The Ossification of the Posterior Longitudinal Ligament (OPLL) is an idiopathic degenerative spinal disease which may cause motor deficit. For patients presenting myelopathy or severe stenosis, surgical decompression is the treatment of choice; however, despite adequate decompression residual motor impairment is found in some cases. After surgery, there is no therapeutic approach available for this population. The Hybrid Assistive Limb® (HAL) robot suit is a unique powered exoskeleton designed to predict, support, and enhance the lower extremities performance of patients using their own bioelectric signals. This approach has been used for spinal cord injury and stroke patients where the walking performance improved. However, there is no available data about gait kinematics evaluation after HAL therapy. Here we analyze the effect of HAL therapy in OPLL patients in acute and chronic stages after decompression surgery. We found that HAL therapy improved the walking performance for both groups. Interestingly, kinematics evaluation by the analysis of the elevation angles of the thigh, shank, and foot by using a principal component analysis showed that planar covariation, plane orientation, and movement range evaluation improved for acute patients suggesting an improvement in gait coordination. Being the first study performing kinematics analysis after HAL therapy, our results suggest that HAL improved the gait coordination of acute patients by supporting the relearning process and therefore reshaping their gait pattern.

SUBMITTER: Puentes S

PROVIDER: S-EPMC5840280 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Reshaping of Gait Coordination by Robotic Intervention in Myelopathy Patients After Surgery.

Puentes Sandra S Kadone Hideki H Kubota Shigeki S Abe Tetsuya T Shimizu Yukiyo Y Marushima Aiki A Sankai Yoshiyuki Y Yamazaki Masashi M Suzuki Kenji K

Frontiers in neuroscience 20180302

The Ossification of the Posterior Longitudinal Ligament (OPLL) is an idiopathic degenerative spinal disease which may cause motor deficit. For patients presenting myelopathy or severe stenosis, surgical decompression is the treatment of choice; however, despite adequate decompression residual motor impairment is found in some cases. After surgery, there is no therapeutic approach available for this population. The Hybrid Assistive Limb® (HAL) robot suit is a unique powered exoskeleton designed t ...[more]

PMID: 29551960

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Project description:BackgroundAging degrades the balance and locomotion ability due to frailty and pathological conditions. This demands balance rehabilitation and assistive technologies that help the affected population to regain mobility, independence, and improve their quality of life. While many overground gait rehabilitation and assistive robots exist in the market, none are designed to be used at home or in community settings.MethodsA device named Mobile Robotic Balance Assistant (MRBA) is developed to address this problem. MRBA is a hybrid of a gait assistive robot and a powered wheelchair. When the user is walking around performing activities of daily living, the robot follows the person and provides support at the pelvic area in case of loss of balance. It can also be transformed into a wheelchair if the user wants to sit down or commute. To achieve instability detection, sensory data from the robot are compared with a predefined threshold; a fall is identified if the value exceeds the threshold. The experiments involve both healthy young subjects and an individual with spinal cord injury (SCI). Spatial Parametric Mapping is used to assess the effect of the robot on lower limb joint kinematics during walking. The instability detection algorithm is evaluated by calculating the sensitivity and specificity in identifying normal walking and simulated falls.ResultsWhen walking with MRBA, the healthy subjects have a lower speed, smaller step length and longer step time. The SCI subject experiences similar changes as well as a decrease in step width that indicates better stability. Both groups of subjects have reduced joint range of motion. By comparing the force sensor measurement with a calibrated threshold, the instability detection algorithm can identify more than 93% of self-induced falls with a false alarm rate of 0%.ConclusionsWhile there is still room for improvement in the robot compliance and the instability identification, the study demonstrates the first step in bringing gait assistive technologies into homes. We hope that the robot can encourage the balance-impaired population to engage in more activities of daily living to improve their quality of life. Future research includes recruiting more subjects with balance difficulty to further refine the device functionalities.

Project description:BackgroundExcessive reliance on wheelchairs in individuals with tetraplegia or paraplegia due to spinal cord injury (SCI) leads to many medical co-morbidities, such as cardiovascular disease, metabolic derangements, osteoporosis, and pressure ulcers. Treatment of these conditions contributes to the majority of SCI health care costs. Restoring able-body-like ambulation in this patient population can potentially reduce the incidence of these medical co-morbidities, in addition to increasing independence and quality of life. However, no biomedical solution exists that can reverse this loss of neurological function, and hence novel methods are needed. Brain-computer interface (BCI) controlled lower extremity prostheses may constitute one such novel approach.MethodsOne able-bodied subject and one subject with paraplegia due to SCI underwent electroencephalogram (EEG) recordings while engaged in alternating epochs of idling and walking kinesthetic motor imagery (KMI). These data were analyzed to generate an EEG prediction model for online BCI operation. A commercial robotic gait orthosis (RoGO) system (suspended over a treadmill) was interfaced with the BCI computer to allow for computerized control. The subjects were then tasked to perform five, 5-min-long online sessions where they ambulated using the BCI-RoGO system as prompted by computerized cues. The performance of this system was assessed with cross-correlation analysis, and omission and false alarm rates.ResultsThe offline accuracy of the EEG prediction model averaged 86.30% across both subjects (chance: 50%). The cross-correlation between instructional cues and the BCI-RoGO walking epochs averaged across all subjects and all sessions was 0.812 ± 0.048 (p-value <10(-4)). Also, there were on average 0.8 false alarms per session and no omissions.ConclusionThese results provide preliminary evidence that restoring brain-controlled ambulation after SCI is feasible. Future work will test the function of this system in a population of subjects with SCI. If successful, this may justify the future development of BCI-controlled lower extremity prostheses for free overground walking for those with complete motor SCI. Finally, this system can also be applied to incomplete motor SCI, where it could lead to improved neurological outcomes beyond those of standard physiotherapy.

Dataset Information

Reshaping of Gait Coordination by Robotic Intervention in Myelopathy Patients After Surgery.

Publications

Reshaping of Gait Coordination by Robotic Intervention in Myelopathy Patients After Surgery.

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