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. 2014 Sep 12:11:137.
doi: 10.1186/1743-0003-11-137.

Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke: a review

Nurdiana Nordin  1 , Sheng Quan Xie, Burkhard Wünsche
Affiliations

Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke: a review

Nurdiana Nordin et al. J Neuroeng Rehabil. .

Abstract

: Studies of stroke patients undergoing robot-assisted rehabilitation have revealed various kinematic parameters describing movement quality of the upper limb. However, due to the different level of stroke impairment and different assessment criteria and interventions, the evaluation of the effectiveness of rehabilitation program is undermined. This paper presents a systematic review of kinematic assessments of movement quality of the upper limb and identifies the suitable parameters describing impairments in stroke patients. A total of 41 different clinical and pilot studies on different phases of stroke recovery utilizing kinematic parameters are evaluated. Kinematic parameters describing movement accuracy are mostly reported for chronic patients with statistically significant outcomes and correlate strongly with clinical assessments. Meanwhile, parameters describing feed-forward sensorimotor control are the most frequently reported in studies on sub-acute patients with significant outcomes albeit without correlation to any clinical assessments. However, lack of measures in coordinated movement and proximal component of upper limb enunciate the difficulties to distinguish the exploitation of joint redundancies exhibited by stroke patients in completing the movement. A further study on overall measures of coordinated movement is recommended.

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Figures

Figure 1
Figure 1
The continuum of stroke recovery stages.
Figure 2
Figure 2
Center-out point-to-point movement adapted from Rohrer et al.[1].
Figure 3
Figure 3
Overview of parameters used for kinematic assessment in robot-assisted upper limb rehabilitation.
Figure 4
Figure 4
Point-to-Point movement following square path and diamond path adapted from Panarese et al. [62]. The segments of diamond path are classified to within (1, 2, 3, 4, 7, 10, 13 and 16) and outside trained workspace (5, 6, 8, 9, 11, 12, 14 and 15), proximal (2, 6, 7, 8, 11, 12, 14 and 15) and distal (4, 5, 9, 10, 13 and 16), dominant (3, 7, 10, 11, 15 and 16) and non-dominant (1, 5, 6, 8, 9, 12, 13 and 14).
See this image and copyright information in PMC

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