Dynamics of post-stroke hand paresis kinematic pattern during rehabilitation

Abstract

According to the literature data, only 5–20% of post-stroke patients are able to restore the hand motor function completely. Correct goal setting and individual approach to the patient's functional recovery are important. Our study aimed to develop an algorithm of impaired hand motor functioning assessment for post-stroke patients and to determine the principles of the rehabilitation tactics choosing based on the biomechanical analysis. Twenty five patients with hemispheric stroke and 10 healthy volunteers participated in the study. Formal clinical observation scales (Fugl-Meyer Assessment, Ashworth Scale, ARAT) and video motion analysis were used for evaluation of the hand motor function. Patients were divided into 2 groups according to the hand paresis severity (mild/moderate and pronounced/severe). Rehabilitation was carried out in both groups, including mechanotherapy, massage and physical therapy. It was revealed that in the 1st group of patients the motor function recovery in the paretic hand was due to movement performance recovery: biomechanical parameters restoration directly correlated with a decrease in the paresis degree according to the Fugl-Meyer Assessment Scale (r = 0.94; p = 0.01). In the 2nd group of patients, the motor function recovery in the paretic hand was due to motor deficit compensation: according to biomechanical analysis, the pathological motor synergies inversely correlated with a decrease in the paresis degree (r = –0.9; p = 0.03). As a result of the study, an algorithm for selecting the patient management tactics based on the baseline clinical indicators was developed.

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

“…This model (29/63, 46% studies) measures awkward movements of the shoulder of the affected UE [ 15 ], involving complex movements of the shoulder girdle and shoulder joint. The most observed shoulder compensation was shoulder elevation (17/29, 59% studies), followed by shoulder abduction [ 27 , 28 , 64 , 70 , 71 ], shoulder girdle compensatory movement [ 29 - 31 , 64 , 72 ], shoulder forward (protraction) [ 26 , 32 ], and shoulder overflexion [ 22 , 33 , 73 ]. The most commonly used task was reaching task (18/29, 62% studies).…”

“…The most commonly used task was reaching task (18/29, 62% studies). Other tasks involved hand-to-mouth tasks [ 33 , 70 , 73 ], drinking tasks [ 66 , 72 ], elbow flexion-extension task [ 59 , 71 ], daily life activities [ 64 , 65 ], counterclockwise cyclic motions [ 31 ], FMA items [ 22 ], and GRASP [ 69 ]. In all, 12 studies were conducted using a robot-assisted device and 1 with an MR training system [ 26 ].…”

“…Shoulder abduction compensation was assessed by acceleration of shoulder joint motion [ 27 , 28 , 64 ], shoulder abduction angle [ 70 ], and functional magnetic resonance imaging (fMRI) [ 71 ]. Shoulder girdle compensatory movement measurements included acceleration of shoulder joint motion [ 29 , 64 ], sEMG signals [ 30 ], shoulder position [ 31 ], and the coefficient of the elbow joint extension to the shoulder joint flexion ratio [ 72 ]. A total of 3 studies [ 22 , 33 , 73 ] used the shoulder flexion angle to assess the shoulder overflexion compensation.…”

“…Several interesting findings were reported using marker-based motion capture systems: (1) pre- and posttests showed that both robotic [ 32 , 38 , 63 ] and MR therapies [ 26 ] elicited benefits on reducing trunk compensatory movements, and stroke survivors showed less trunk compensatory movements during VR reaching [ 24 ]. However, Belfatto et al [ 33 ] argued that robotic therapy promoted the adoption of compensatory movements when stroke survivors performed training tasks; (2) therapist-based therapy [ 62 ] or a combination of robotic therapy and constraint-induced therapy [ 46 ] demonstrated more significant improvements in reducing trunk compensatory movements compared with robot-assisted therapy; (3) trunk displacement and shoulder elevation compensatory movements could discriminate between mild and moderate stroke paresis [ 66 ], whereas shoulder girdle compensatory movement could differentiate between mild or moderate and severe or pronounced stroke impairments [ 72 ].…”

“…Marker-based motion capture systems yield accurate and robust real time motion tracking and have been used as ground truth to verify the effectiveness of other sensors for compensation assessment and detection [ 42 , 43 , 45 ]. In our reviewed studies, marker-based motion capture systems were used to detect various compensations, including arm nonuse [ 86 , 87 ], trunk compensation [ 26 , 45 ], shoulder compensation [ 66 , 72 ] and interlimb coordination [ 36 ]. The drawbacks of these systems include but are not limited to the cost of both hardware and software, complicated setup, and the need for professionals to operate the systems [ 50 ].…”