Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Abstract

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upperlimb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS

Application of Multi-Dimensional Intelligent Visual Quantitative Assessment System to Evaluate Hand Function Rehabilitation in Stroke Patients

“…Brunnstrom Scale [ 7 , 17 ]: upper limb and hand parts; each is divided into stage I–VI, and the higher the level, the better the motor function. Stage I: no exercise; Stage II: slight flexion; Stage III: flexion but not extension; Stage IV: the thumb can be pinched and loosened, and the fingers can be extended semi-randomly in a small area; Stage V: can do spherical or cylindrical grip, and can be free to extend the whole finger, but the range of size is not equal; Stage VI: full range extension of various grips, but with less speed and accuracy than the healthy side.…”

“…The above evaluation methods have been widely used in clinical practice for a long time, and their reliability and validity are also widely recognized. However, the actual application is easily influenced by personal experience [ 6 , 7 , 8 , 9 ]. Therefore, there is great interest in developing an automated system to achieve intelligent, objective and quantitative assessment of hand function rehabilitation after stroke.…”

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

“…Therefore, timely rehabilitation assessment of rehabilitation training can effectively reduce the impact of motion compensation and muscle fatigue. Presently, upper limb rehabilitation assessment methods, including Brunnstrom Recovery Stage ( Meng et al, 2022 ), Fugl-Meyer Assessment ( DJ, 2002 ), and Modified Ashworth Scale ( Ansari et al, 2012 ), possess comprehensive evaluation criteria. They heavily rely on clinical expertise and possess subjectivity.…”

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

“…Doherty et al [ 28 ] used the context provided by cameras to identify the specific classed of activity once an accelerometer had identified the level of activity being undertaken. Meng et al [ 29 ] collected acceleration, angular velocity, and surface electromyography data synchronously from 5 upper-limb-worn sensor modules to evaluate the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing, and drinking). Second, several studies have revealed that using a single accelerometer might not be sufficient for activity classification [ 24 , 30 ].…”