Examining impairment of adaptive compensation for stabilizing motor repetitions in stroke survivors

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

Background

Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation.

Objective

This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation.

Methods

We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question.

Results

A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures.

Conclusions

This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.

Greater Reliance on Cerebral Palsy-Specific Muscle Synergies During Gait Relates to Poorer Temporal-Spatial Performance Measures

Children with cerebral palsy typically exhibit reduced complexity of muscle coordination patterns during walking; however, the specific patterns that characterize their gait abnormalities are still not well documented. This study aimed to identify the specific repertoire of muscle coordination patterns in children with CP during walking compared to same-aged peers without CP and their relationships to gait performance. To identify muscle coordination patterns, we extracted muscle synergies from 10 children with CP and 10 age-matched typically developing children (TD). K-mean clustering and discriminant analyses of all extracted synergies were used to group similar synergies. Then, weight-averaged z-scores were quantified for each cluster to determine their group-specific level. In this cohort, 10 of the 17 distinct clusters were largely CP-specific while six clusters were seen mainly in TD, and one was non-specific. CP-specific clusters generally showed merging of two TD synergies, excessive antagonist co-activation, decreased muscle activation compared to TD, and complex or atypical pattern. Significant correlations were found between weight-averaged z-scores and step length asymmetry, cadence asymmetry, self-selected treadmill speed and AP-COM displacement of the pelvis such that greater CP-specificity of muscle synergies was related to poorer performance, thus indicating that CP-specific synergies can influence motor dysfunction.

Technical development of transcutaneous electrical nerve inhibition using medium-frequency alternating current

Background

Innovative technical approaches to controlling undesired sensory and motor activity, such as hyperalgesia or spasticity, may contribute to rehabilitation techniques for improving neural plasticity in patients with neurologic disorders. To date, transcutaneous electrical stimulation has used low frequency pulsed currents for sensory inhibition and muscle activation. Yet, few studies have attempted to achieve motor nerve inhibition using transcutaneous electrical stimulation. This study aimed to develop a technique for transcutaneous electrical nerve inhibition (TENI) using medium-frequency alternating current (MFAC) to suppress both sensory and motor nerve activity in humans.

Methods

Surface electrodes were affixed to the skin of eight young adults to stimulate the median nerve. Stimulation intensity was increased up to 50% and 100% of the pain threshold. To identify changes in sensory perception by transcutaneous MFAC (tMFAC) stimulation, we examined tactile and pressure pain thresholds in the index and middle fingers before and after stimulation at 10 kHz. To demonstrate the effect of tMFAC stimulation on motor inhibition, stimulation was applied while participants produced flexion forces with the index and middle fingers at target forces (50% and 90% of MVC, maximum voluntary contraction).

Results

tMFAC stimulation intensity significantly increased tactile and pressure pain thresholds, indicating decreased sensory perception. During the force production task, tMFAC stimulation with the maximum intensity immediately reduced finger forces by ~ 40%. Finger forces recovered immediately after stimulation cessation. The effect on motor inhibition was greater with the higher target force (90% MVC) than with the lower target (50% MVC). Also, higher tMFAC stimulation intensity provided a greater inhibition effect on both sensory and motor nerve activity.

Conclusion

We found that tMFAC stimulation immediately inhibits sensory and motor activity. This pre-clinical study demonstrates a novel technique for TENI using MFAC stimulation and showed that it can effectively inhibit both sensory perception and motor activity. The proposed technique can be combined with existing rehabilitation devices (e.g., a robotic exoskeleton) to inhibit undesired sensorimotor activities and to accelerate recovery after neurologic injury.