Effects of trunk control robot training on balance and gait abilities in persons with chronic stroke

Lim, Chae-gil

doi:10.14474/ptrs.2020.9.2.105

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…24 Hemiplegia as the utmost stand out symptoms not only affect in limb activity but also has it effects on trunk muscle function both sides of the body affecting the proximal stabilization that eventually will influence the mobility of the distal part. 25,27 The trunk dysfunction in post stroke patient has been demonstrated in many studies. It is known that trunk musculature that build up postural stabilization received input from bilateral hemispheric, but still the effect of upper motor neuron lesion on functional changes in trunk control is noticeable.…”

Section: Stroke Impact On Postural Controlmentioning

confidence: 99%

Postural Dysfunction in Stroke Rehabilitation

Sumardi¹

2021

IJPMR

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Introduction: Trunk control ia a major part of postural control during human movement. This are commonly affected in stroke but mostly are being overlooked compared to limb impairment. Thisarticle objective is to review the current literature on postural dysfunction after stroke and rehabilitation principles implementation.Method: Literature or data sources comprised through computer-aided search of published papers with the key words of stroke or cerebrovascular accident, normal and pathological postural control, corestabilization exercise and trunk rehabilitation.Result: Thirty five papers were selected and were published from 2010-2 020.Conclusion: The abdominal muscles are held responsible to form a muscular c orset at the lumbar spine as proximal stabilization during any purposeful task. In stroke patients, the recruitment of posturalcontrol muscles is delayed with regard to the limbs activations. This will hamper the activities of daily living by disrupting the human postural adjustment mechanisms. The rehabilitation processes to regainpostural control are vital, and should involve exercises to imp rove trunk “core” muscle stability.Keywords: core stabilization, exercise, postural control, rehabilitation, strokeDepartment of Physical Medicine and Rehabilitation, Hasan Sadikin General HospitalFaculty of Medicine, Padjadjaran University, Bandung, Indonesia

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Section: Stroke Impact On Postural Controlmentioning

confidence: 99%

Postural Dysfunction in Stroke Rehabilitation

Sumardi¹

2021

IJPMR

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“…The flexibility, creativity, and repeatability of virtual reality technology in security control, scene planning, interaction frequency and intensity settings, etc. provide new ideas for the development of many fields [3,4]. Virtual reality technology has been initially applied in the fields of military training, psychotherapy and rehabilitation of the disabled.…”

Section: Introductionmentioning

confidence: 99%

Motion Balance Ability Detection Based on Video Analysis in Virtual Reality Environment

2020

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In recent years, smart camera devices under the Virtual Reality (VR) environment have been widely popularized. These devices can be equipped with fast and effective computer vision applications, including the detection of the balance ability of moving targets. Moving target balance ability detection plays an important role in public security, traffic monitoring and other fields, and is also a basic technology for many vision applications. Therefore, the requirements for accuracy and completeness of detection are getting higher and higher. This paper proposes a tracking method Motion Model and Model Updater (MMMU) based on the balance acquisition and model update and intelligent adjustment of the motion model. Improved Motion Model (IMM) is a background sample balance acquisition algorithm based on simple linear iterative clustering, completes the abstraction of background images. Different from other update strategies with a fixed number of frames, the update strategy based on image histogram contrast relies on the human selective forgetting mechanism to better avoid burst frames and process similar frames. Since the data used to detect the balance ability of moving targets is inherently unbalanced, the idea of dealing with imbalance in data mining is introduced into it, and the problem of balance ability detection of moving targets is studied from the perspectives of downsampling and oversampling. In addition, temporal and spatial oversampling of the foreground and selective downsampling of the background are performed to reduce the imbalance of the data set, and the resampled data set is used for modeling and classification. The feasibility of the MMMU algorithm is tested through experiments, and the motion balance ability of the foreground target is detected relatively completely.

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