[ARTICLE] Comparing Home Upper Extremity Activity with Clinical Evaluations of Arm Function in Chronic Stroke – Full Text PDF

Abstract

Objective

To determine if clinical evaluations of post-stroke arm function correspond to everyday motor performance indexed by arm accelerometers.

Design

Cross-sectional study analyzing baseline data from a larger trial (NCT02665052). Setting: Outpatient research center.

Participants

Twenty community-dwelling adults with chronic arm motor deficits (stroke≥6mo). Intervention: 72-hours of home wrist-worn accelerometry during normal routine.

Main Outcome Measures

Clinical evaluations included the Fugl-Meyer (FM), Action Research Arm Test (ARAT), Wolf Motor Function Test (WMFT), and two self-assessments: the Motor Activity Log (MAL) and hand motor subscale of the Stroke Impact Scale (SIS). Accelerometer-derived variables included quantifications of movement intensity (magnitude) and duration of arm use.

Results

Participants had moderate arm impairment (FM 36.1 ± 9.4). The accelerometer-derived mean magnitude ratio correlated significantly with the FM (ρ = 0.60, p < 0.01), WMFT functional score (ρ = 0.59, p < 0.01), and ARAT (ρ = 0.50, p < 0.05). The hours of use ratio correlated with the MAL amount of use (ρ = 0.58, p < 0.01) and quality of movement (ρ = 0.61, p < 0.01). Total paretic hours did not correlate with the FM, WMFT or ARAT, and intensity variables did not correlate with the MAL or SIS.

Conclusions

Participants with higher baseline function had greater intensity of paretic arm movement at home; similarly, those who perceived they had less disability used their paretic arm more relative to their non-paretic arm. However, some participants with higher clinical scores did not exhibit greater arm use in everyday life, possibly due to neglect and learned non-use. Therefore, individualized home accelerometry profiles could provide valuable insight to better tailor post-stroke rehabilitation.

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via Comparing Home Upper Extremity Activity with Clinical Evaluations of Arm Function in Chronic Stroke – ScienceDirect

[Abstract] The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination.

Source: The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

[ARTICLE] Clinical Assessments for Predicting Functional Recovery after Stroke – Full Text PDF

Abstract

Despite on-going technological developments, clinical assessment remains an essential tool to evaluate the effects of rehabilitation treatment and to predict functional recovery. This paper provides a review of clinical assessment for stroke patients focusing on predictive value of motor, function and participation assessment, taking into consideration some specific evaluations for upper and lower limb function, trunk control, balance and walking. In the future an increased integration between clinical assessment, neurophysiology and neuroimaging will be required, in order to apply specific evaluation pathways to reach a more accurate and customized prognostic stratification.

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[ARTICLE] Vision-based body tracking: turning Kinect into a clinical tool

Abstract

Purpose: Vision-based body tracking technologies, originally developed for the consumer gaming market, are being repurposed to form the core of a range of innovative healthcare applications in the clinical assessment and rehabilitation of movement ability. Vision-based body tracking has substantial potential, but there are technical limitations.

Method: We use our “stories from the field” to articulate the challenges and offer examples of how these can be overcome.

Results: We illustrate that: (i) substantial effort is needed to determine the measures and feedback vision-based body tracking should provide, accounting for the practicalities of the technology (e.g. range) as well as new environments (e.g. home). (ii) Practical considerations are important when planning data capture so that data is analysable, whether finding ways to support a patient or ensuring everyone does the exercise in the same manner. (iii) Home is a place of opportunity for vision-based body tracking, but what we do now in the clinic (e.g. balance tests) or in the home (e.g. play games) will require modifications to achieve capturable, clinically relevant measures.

Conclusions: This article articulates how vision-based body tracking works and when it does not to continue to inspire our clinical colleagues to imagine new applications.

Implications for Rehabilitation

• Vision-based body tracking has quickly been repurposed to form the core of innovative healthcare applications in clinical assessment and rehabilitation, but there are clinical as well as practical challenges to make such systems a reality.
• Substantial effort needs to go into determining what types of measures and feedback vision-based body tracking should provide. This needs to account for the practicalities of the technology (e.g. range) as well as the opportunities of new environments (e.g. the home).
• Practical considerations need to be accounted for when planning capture in a particular environment so that data is analysable, whether it be finding a chair substitute, ways to support a patient or ensuring everyone does the exercise in the same manner.
• The home is a place of opportunity with vision-based body tracking, but it would be naïve to think that we can do what we do now in the clinic (e.g. balance tests) or in the home (e.g. play games), without appropriate modifications to what constitutes a practically capturable, clinically relevant measure.

via Vision-based body tracking: turning Kinect into a clinical tool, Disability and Rehabilitation: Assistive Technology, Informa Healthcare.