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Table of Contents    
COMMENTARY
Year : 2019  |  Volume : 67  |  Issue : 4  |  Page : 1041-1042

Post-stroke Gait Analysis in Rehabilitation Set-up: Observational or Instrumental!


Department of Neurological Rehabilitation, National Institute of Mental Health and Neurosciences (NIMHANS, Institute of National Importance), Bengaluru, Karnataka, India

Date of Web Publication10-Sep-2019

Correspondence Address:
Dr. A B Taly
National Institute of Mental Health and Neurosciences (NIMHANS, Institute of National Importance), Bengaluru - 560 029, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.266276

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How to cite this article:
Gupta A, Taly A B. Post-stroke Gait Analysis in Rehabilitation Set-up: Observational or Instrumental!. Neurol India 2019;67:1041-2

How to cite this URL:
Gupta A, Taly A B. Post-stroke Gait Analysis in Rehabilitation Set-up: Observational or Instrumental!. Neurol India [serial online] 2019 [cited 2019 Nov 13];67:1041-2. Available from: http://www.neurologyindia.com/text.asp?2019/67/4/1041/266276




Impairment in walking ability is common among chronic stroke survivors and is a major reason for admission in specialized rehabilitation units. The factors contributing to gait abnormality include motor-sensory deficits, muscle weakness, spasticity, and impaired cognition. The changes are dynamic, but most of these patients have spastic equinus gait.[1],[2] More than two-thirds of stroke survivors are able to walk independently or with assistance at 6 months of post stroke, but poor gait speed and endurance and impaired balance limit their community ambulation, and participation in vocational activities, increase the risk of falls and fractures, and compromise overall health-related quality of life.[3] Thus, accurate assessment and documentation of gait abnormalities following stroke is thus essential for planning precise treatment and interventions.[4]

Walking is a very complex motion and the term 'gait analysis' refers to the scientific investigation of human locomotion. Simple clinic-based measures incorporate assessments of gait speed using the '10-metre walk test', and of gait endurance using '3-minute or 6-minute' walk tests' and, balance using 'Timed up and go test', Berg's Balance Scale, Performance-Oriented Mobility Assessment (POMA) Tinetti test, among others. These tests are reliable, quantitative and of functional significance but they do not provide the details of gait patterns, deviations, kinetics and kinematics that are essential for rehabilitation intervention.

A better understanding of the biomechanics of locomotion and availability of newer biomedical technology has led to significant advances in the assessment of gait and rehabilitation. GAITRite, a relatively new tool is considered valid and reliable for temporal and spatial evaluation of gait. It is a gait mat with a grid of over 18,000 pressure sensors. It allows for objective capture of temporal and spatial gait characteristics, values that can be immediately visualized graphically and compared to normative data.[5]

Virtual reality (VR), which is more commonly used as a 'gait and balance training module' in stroke survivors, is a computer-based technology that constructs a virtual environment simulating a scenario in the real world and provides multimodal cues to the patient. As the users interact with or manipulate the virtual objects, they receive instant visual, audio or haptic feedback of their performances. Depending on the extent of isolation of the user from the surroundings during an interaction, the visual stimuli can be classified into immersive, semi-immersive and non-immersive VR.[6]

2D or 3D analysis of the gait by 'Motion Capture Camera' is another technique used for observing kinematic and kinetic properties of the affected limb. More than one camera is required for 3D analysis of gait and usually 'Optoelectronic technology' is used for 3D capture of gait data. Here, the light emitting diode (LED) markers are attached at the point of interest on subject and signals are used to construct model for gait analysis. Electro goniometers, accelerometers and gyroscopes have also been used for the gait analysis.[7],[8],[9]

In the original article written in this issue by Arya et al., authors have carried out “observational” gait analysis in a select group of patients with unilateral stroke of at least six months duration.[10] They have used 'The Rivermead Visual Gait Assessment (RVGA) tool for collecting kinematic data and correlated with the other objective measures of the; Fugl–Meyer assessment (lower extremity), the 10m walk test, Time up and go test, and Berg balance scale (BBS). The authors videotaped walking of patients, coded and provided the tapes to four other research staff with varied experiences, from novice to experts in the field with 20 years of experience, for analysis. Each rater was blind to the side of hemiparesis and used an RVGA data sheet for record and analysis of their observations, at one month interval, on separate computers to avoid bias. The authors observed good-to-excellent inter and intra-rater agreement across the research staff. It is of interest to note that there was consensus on the interpretation of data, both by the 'novice' and the 'experienced' raters. While the total RVGA score showed significant positive relation with BBS for all the raters, it did not correlate significantly with Fugl–Meyer assessment (lower extremity), 10meter walk test and Time up and go test.

Comprehensive and in-depth recording, analysis and interpretation of data that are pertinent to the rehabilitation setting require establishment and maintenance of a dedicated advanced motion laboratory. Although considered “the gold standard”, the lab-based gait analysis is expensive, technologically demanding, and not available in most of the centers. Observational gait analysis is a commonly used approach to assess gait kinematics in clinical practice. It is inexpensive, does not require any device, can be carried out in any setting and involves the recording of events occurring at various joints and their spatiotemporal relationship. However, the results are qualitative and observer-dependent.

Arya et al. have established the reliability and validity of video-based RVGA tool in chronic stroke survivors. Easy availability of advanced smartphone technology may enable recording of gait kinematics that can be stored for evaluation by experts, training of research staff and therapist, and feedback to the patients in resource-limited setting.[11] However, the need of the hour is to establish the state of the art gait laboratories in our country for to better understand the spectrum of the gait variation at different time point following stroke and thus for the optimization of learning and maximization of care.



 
  References Top

1.
Sankarnarayanan H, Gupta A, Khanna M, Taly AB, Thennarasu K. Role of Ankle-foot-orthosis in improving locomotion and functional recovery in patients with stroke: A prospective rehabilitation study. J Neurosci Rural Pract 2016;7:545-9.  Back to cited text no. 1
    
2.
Jonsdottir J, Ferrarin M. Gait disorders in persons with stroke. In: Muller M, SI Wolf SI, editors. Hand Book of Human Motion. doi: 10.1007/978-3-319-30801-1_61-1.  Back to cited text no. 2
    
3.
Srivastava A, Gupta A, Taly AB, Murali T. Rehabilitation interventions to improve locomotor outcome in chronic stroke survivors: A prospective, repeated measure study. Neurol India 2015;63:347-52.  Back to cited text no. 3
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4.
Akhtaruzzaman M, Shafie AA, Khan MR. Gait analysis: Systems, technologies and importance. J Mechan Med Biol 2016;16:1-45.  Back to cited text no. 4
    
5.
Menz HB, Latt MD, Tiedemann A, Mun San Kwan M, Lord SR. Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. Gait Posture 2004;20:20-5.  Back to cited text no. 5
    
6.
Adamovich SV, Fluet GG, Tunik E, Merians AS. Sensorimotor training in virtual reality: A review. NeuroRehabilitation 2009;25:29-44.  Back to cited text no. 6
    
7.
Prakash C, Kumar R, Mittal N. Recent development in human gait research, parameters, approaches, applications, machine learning, technology, datasets and challenges. Artif Intell Rev 2016. doi: 10.1007/s10462-016-9514-6.  Back to cited text no. 7
    
8.
Nadeu S, Betschart M, Bethoux F. Gait analysis for post stroke rehabilitation: The relevance of Biomechanical analysis and the impact of gait speed. Phys Med Rehabil Clin N Am 2013;24:265-76.  Back to cited text no. 8
    
9.
Nadeau S, Duclos C, Bouyer L, Richards CL. Guiding task-oriented gait training after stroke or spinal cord injury (SCI) by means of a biomechanical gait analysis. In: Green AM, Chapman CE, Kalaskav J, et al., editors. Progress in Brain Research. Amsterdam; 2011. p. 161-80.  Back to cited text no. 9
    
10.
Arya KN, Pandian S, Kumar V, Agarwal GG, Asthana A. Post stroke visual gait measures for developing countries: A reliability and validity study. Neurol India 2019;67:1033-40.  Back to cited text no. 10
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11.
Swetha M, Sumanth V, Tekpal B, Umashankar, Vikas MR. Wireless IoT system towards gait detection in stroke patients. International Journal of Advances in Scientific Research and Engineering 2018;4:81-4.  Back to cited text no. 11
    




 

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