Hemispheric Differences in Self-evaluation Errors of Upper Extremity Movement in Patients with Chronic Stroke.

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.

Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation.

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.

The Effect of the Physical and Chemical Properties of Synthetic Fabrics on the Release of Microplastics during Washing and Drying.

Synthetic fibers released during washing are the primary source of microplastic pollution. Hence, research on reducing the release of microplastic fibers during washing has recently attracted considerable attention. As a result of previous studies, there is a difference in the amount of microplastic emission according to various types of fabrics. To mitigate the release of microplastics, the study of the reason for the difference in the amount of microplastics is needed. Therefore, this study investigated different synthetic fabrics that release microplastics and the physical properties of the fabrics that affect the release of fibers. Three types of fabrics with different chemical compositions were analyzed. The washing and drying processes were improved by focusing on the mechanical factors that affected microplastic release. Furthermore, based on the mass of the collected microplastic fibers, it was found that the chemical compositions of the fabric can affect the microplastics released during washing and drying. This evaluation of physical properties helped to identify the physical factors that affect microplastic release. These results may provide a basis for reducing microplastic fiber types, thereby minimizing unintended environmental pollution.

Characterization of Microplastics Released Based on Polyester Fabric Construction during Washing and Drying.

With the increasing production of synthetic materials, more microplastic fibers are being generated while washing clothes. Consequently, these particles are increasingly detected in the aquatic environment. Synthetic fibers produced via washing have a relatively high contribution to microplastic pollution. Hence, recent research on reducing the release of microplastic fibers is attracting considerable attention. In this study, fabric-specific analysis was performed by strictly controlling various factors, and each washing and drying process was improved by focusing on the mechanical factors affecting microplastic release. Furthermore, the mass of the collected microplastic fibers and their length distribution were measured. Fabric construction, including chemical composition and yarn type, impacted the microplastics released during washing and drying. Differences in the mechanical factors during washing helped to identify the physical factors affecting microplastic release. These results on the release of microplastics may provide a basis for developing a filter system that can minimize the unintended environmental consequences.

Analysis of Microplastics Released from Plain Woven Classified by Yarn Types during Washing and Drying.

Microplastics reach the aquatic environment through wastewater. Larger debris is removed in sewage treatment plants, but filters are not explicitly designed to retain sewage sludge's microplastic or terrestrial soils. Therefore, the effective quantification of filtration system to mitigate microplastics is needed. To mitigate microplastics, various devices have been designed, and the removal efficiency of devices was compared. However, this study focused on identifying different fabrics that shed fewer microplastics. Therefore, in this study, fabric-specific analyses of microplastics of three different fabrics during washing and drying processes were studied. Also, the change in the generation of microplastics for each washing process of standard washing was investigated. The amount of microplastics released according to the washing process was analyzed, and the collected microplastics' weight, length, and diameter were measured and recorded. According to the different types of yarn, the amount of microplastic fibers produced during washing and drying varied. As the washing processes proceed, the amount of microplastics gradually decreased. The minimum length (>40 µm) of micro-plastics generated were in plain-woven fabric. These results will be helpful to mitigate microplastics in the production of textiles and in selecting built-in filters, and focusing on the strict control of other parameters will be useful for the development of textile-based filters, such as washing bags.