Although we are seldom aware of it, we constantly make decisions to use one arm or the other in daily activities. Here, we studied whether these decisions change following stroke. Our results show that effort, success, and side of lesion determine arm choice in a reaching task: whereas left-paretic individuals modified their arm choice in response to failures in reaching the target, right-paretic individuals showed a pattern of choice independent of failures.

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non-intrusive wearable sensors provides context for arm use and is essential for the development of a home rehabilitation system. However, the movement classification of stroke patients poses unique challenges, including variability and sparsity. To address these challenges, we collected movement data from 15 hemiparetic stroke patients (Stroke group) and 29 non-disabled individuals (ND group). The participants performed two different tasks, the range of motion (14 movements) task and the activities of daily living (56 movements) task, wearing five inertial measurement units in a home setting. We trained a 1D convolutional neural network and evaluated its performance for different training groups: ND-only, Stroke-only, and ND and Stroke jointly. We further compared the model performance with data augmentation from axis rotation and investigated how the performance varied based on the asymmetry of movements. The joint training of ND + Stroke yielded an increased F1-score by a margin of 31.6% and 10.6% compared to ND-only training and Stroke-only training, respectively. Data augmentation further enhanced F1-scores across all conditions by an average of 11.3%. Finally, asymmetric movements decreased the F1-score by 25.9% compared to symmetric movements in the Stroke group, indicating the importance of asymmetry in movement classification.

BACKGROUND Measuring the use of an affected limb in a home setting after hospital discharge is crucial for stroke rehabilitation. Classifying movements using non-intrusive wearable sensor data provides a context for arm use, and it is essential for the development of a home rehabilitation system with monitoring and feedback functions. However, classification of the movements of stroke patients poses some challenges, including variability in duration and patterns, as well as data sparsity. OBJECTIVE We aimed to investigate methods for the preprocessing, training, and evaluation of a deep learning model to effectively classify the movements of stroke patients with hemiparesis. To address the abovementioned challenges, we adopted linear interpolation in preprocessing, compared the model performance using different training groups (non-disabled individuals and patients with stroke), tested the effect of data augmentation, and observed how the asymmetry of movements affected the model performance. METHODS Fifteen patients with subacute to chronic stroke (Stroke group) and 29 non-disabled individuals (ND group) participated in two different tasks, a range of motion (ROM; 14 movements) task and activities of daily living (ADL; 56 movements) task, wearing five inertial measurement unit sensors in a home setting. We trained a one-dimensional convolutional neural network (1D-CNN) and evaluated its performance using the F1-score for different training conditions, including ND only, Stroke only, and combined ND+Stroke. In addition, we tested the effect of axis rotation as a data augmentation technique. The model performance was further investigated with different asymmetricities of movement. RESULTS Training with combined ND+Stroke data demonstrated the best results (ROM, F1=0.721; ADL, F1=0.603), followed by Stroke only (ROM, F1=0.676; ADL, F1=0.526) and ND only (ROM, F1=0.553; ADL, F1=0.454) for the original and augmented data. Axis rotation consistently boosted performance in all training conditions (ROM, P=.014; ADL, P<.001). For the Stroke group, bimanual symmetric movements showed significantly better performance (F1=0.757) than bimanual asymmetric (F1=0.541) and unimanual (F1=0.581) movements (P<.001) in the ADL task. CONCLUSIONS Effectively training a deep learning model for the classification of the movements of stroke patients requires adjusting the data length to a fixed size. Despite the distinct and diverse patterns of stroke movements, it is still beneficial to train a model using combined ND+Stroke data. Proper data augmentation, such as axis rotation, further enhances the model performance. Finally, the result on stroke data was differently affected by the asymmetry of movements, with better performance for symmetric than asymmetric movements. These findings may provide the best practices and processes for classifying the movements of stroke patients when training and evaluating deep learning models.

Jae-hyeon Kim, PT, BHSc, Yeon-woo Jeong, PT, BHSc, Su-jin Kim, PT, PhD. Phys. Ther. Korea 2020;27:278-85. https://doi.org/10.12674/ptk.2020.27.4.278

Objective: The purpose of this study was to evaluate the relations among the combined therapy with steroid and the detoxification enzyme gene polymorphisms in patients with sudden sensorineural hearing loss (SSNHL). The pathogenetic mechanism of inner ear dysfunction could involve an increase in lipid peroxidation and a decrease in cellular antioxidant defense. Glutathione S-transferases (GSTs) and cytochrome P450 (CYP) belong to a system of detoxification and antioxidant enzymes that have been demonstrated in the inner ear. Study Design: A prospective study in patients with SSNHL. Patients and Methods: All 441 subjects were genotyped for GSTM1, GSTT1, and CYP1A1 polymorphisms. The polymorphisms were analyzed by polymerase chain reaction amplification, restriction enzyme digestion, and deoxyribonucleic acid fragment separation by electrophoresis. Results: No significant difference was observed between SSNHL patients and controls in 3 polymorphisms. However, the prevalence of the partial recovery group in patients with the CC genotype of CYP1A1 (22%) was higher than that in the complete recovery (7.4%) or no recovery group (12.5%) for the subjects classified according to modified Siegel's criteria but were not statistically significant. Conclusion: This is the first approach to analyze gene polymorphism and efficacy of clinical treatment of patients with SSNHL, although the observations do not confirm the effect of the GSTM1/T1 and CYP1A1 genotypes as a risk factor for SSNHL.

Objective: There is growing evidence that the side of brain lesions results in distinct upper extremity deficits in motor control, movement behavior, and emotional and cognitive function poststroke. We investigated self-evaluation errors, which are the differences in scores between patient self-evaluation and clinician evaluations, and compared patients with left hemisphere damage (LHD) and right hemisphere damage (RHD) poststroke.Method: Twenty-eight patients with chronic stroke (LHD = 16) performed the actual amount of the test twice with a one-week interval. We videotaped the participants' movements, and participants with stroke and evaluators graded the quality of movement scores by watching video recordings.Results: Self-evaluation errors were significantly lower in patients with LHD than in those with RHD (t= 2.350,p= .019). Interestingly, this error did not change after the clinician provided the correct score as feedback. Chi-squared analysis revealed that more patients with LHD underestimated their movements (χ²= 9.049,p= .002), while more patients with RHD overestimated (χ²= 7.429,p= .006) in the send evaluation. Furthermore, there were no correlations between self-evaluation error and age, cognitive function, physical impairment, ability to control emotions, or onset months poststroke.Conclusions: Patients with stroke and therapists evaluated the same movements differently, and this can be dependent on hemispheric damage. Therapists might need to encourage patients with LHD who underestimate their movement to ensure continuous use of their more-affected arm. Patients with RHD who overestimate their movement might need treatment to overcome impaired self-awareness, such as video recordings, to protect from unexpected dangerous situations.