Objective Assessment of Upper Limb Mobility for Post-stroke Rehabilitation

Abstract

The assessment of the limb mobility of stroke patients is an essential part of poststroke rehabilitation. Conventionally, the assessment is manually performed by clinicians using chart-based ordinal scales, which can be subjective and inefficient. By introducing quantitative evaluation measures, the sensitivity and efficiency of the assessment process can be significantly improved. In this paper, a novel single-index-based assessment approach for quantitative upper-limb mobility evaluation has been proposed for poststroke rehabilitation. Instead of the traditional human-observation-based measures, the proposed assessment system utilizes the kinematic information automatically collected during a regular rehabilitation training exercise using a wearable inertial measurement unit. By calculating a single index, the system can efficiently generate objective and consistent quantitative results that can reflect the stroke patient's upper-limb mobility. In order to verify and validate the proposed assessment system, experiments have been conducted using 145 motion samples collected from 21 stroke patients (12 males, nine females, mean age 58.7±19.3) and eight healthy participants. The results have suggested that the proposed assessment index can not only differentiate the levels of limb function impairment clearly (p < 0.001, two-tailed Welch's t-test), but also strongly correlate with the Brunnstrom stages of recovery (r = 0.86, p < 0.001). The assessment index is also proven to have great potential in automatic Brunnstrom stage classification application with an 82.1% classification accuracy, while using a K-nearest-neighbor classifier.

A review of computational approaches for evaluation of rehabilitation exercises

“…For example, Saraee et al [137] used the DTW distance and speed ratio of key body joints for an exercise to directly evaluate the quality of performance with respect to reference template sequences. In [138] and [123], movement quality scores were derived by scaling the DTW distance values in the [0, 1] range. In the former, the DTW distance errors were scaled by the lower bound and upper bound, based on a scoring function introduced in [142].…”

“…The studies in the literature based on evaluation using distance functions reported a high correlation between patient's performance and clinicians' evaluation. For instance, DTW-based evaluation achieved high posture monitoring accuracy (91.9%) and exercise monitoring accuracy (95.2%) in comparison to clinicians' annotated rehabilitation data [55], high correlation with the Brunnstrom stages of recovery (86% at p<0.001) [138], and high predictive score accuracy (80%) [57] in comparison to clinical evaluation.…”

“…One shortcoming of the probabilistic models is that the movements are represented at a single level of movement abstraction, and it is difficult to implement probabilistic modeling at multiple levels of movement abstraction. [128] Kinect v2 S Hand-crafted MA Saraee et al [156] Kinect v2 S Hand-crafted MA Taylor et al [45] Inertial sensor I Hand-crafted MC Zhang et al [48] Inertial sensor I Cross-correlation function MC Lin and Kulić [148] Inertial sensor S None MS Chen et al [183] Inertial sensor I Hand-crafted MC Zhang et al [138] Inertial sensor I None MA Houmanfar et al [53] Inertial sensor I Hand-crafted + LASSO MA Msayib et al [184] Inertial sensor S Hand-crafted MA Um et al [185] Inertial sensor I None DA Um et al [186] Inertial sensor I None MC Um et al [46] Inertial Table 4. Summary of approaches for evaluation of rehabilitation movements.…”

SOPHIA: Soft Orthotic Physiotherapy Hand Interactive Aid

“…Afterward, two advanced classification techniques were applied to distinguish hand movements: k-nearest neighbor (k-NN) and SVM. These types of techniques have been used previously to classify signals for stroke rehabilitation (Munoz et al, 2014;Zhang et al, 2015). We provide links to our software that implements these algorithms in Supplementary Material.…”

Effect of Hand Dominance on Functional Status and Recovery of Hand in Stroke Patients

“…The Brunnstrom approach is focused on the progressive development of the motion synergic pattern during the rehabilitation process and divides the motor recovery into six stages from the period of complete flaccidity to the disappearance of spasticity when the patient is able to perform near-normal to normal movement. The simplicity makes the Brunnstrom classification one of the most favorable measures as a repetitive follow-up test during rehabilitation program [9,10].…”