In order to accurately evaluate the similarity of motions during daily rehabilitation training for stroke patients, this paper proposed a novel quantitative assessment method based on dynamic time warping (DTW) algorithm. Firstly, the raw accelerometer signals were preprocessed to eliminate the noise. Secondly, the similarity between the accelerometer signals and four standard task templates was calculated respectively, and then the motion was recognized based on the similarity measurements. Finally, the corresponding quantitative assessment model was used to compute the result. The clinical experimental results showed that there were significant differences in the shortest path distance (R value) of DTW between different tasks, and the classification accuracy could be up to 91% when the R value was selected as the classification feature. Additionally, with the process of rehabilitation, the R value decreased gradually, which means that the R value can be taken as the assessment index to evaluate the quality of designated tasks for stroke patients. It also indicated that the R value could be applied into the scene of automatic prescription generation and interactive gaming to determine whether it is needed to change the rehabilitation plan or adjust the game difficulty level, so as to implement the individualized rehabilitation services.
Objective To develop a behavioral assessment scale for medication management plans in women of childbearing age with epilepsy and to test its reliability and validity. Methods Based on the Theory of Planned Behavior, a pool of questionnaire items was initially drafted through literature review and focus group discussions. A two-round Delphi expert consultation was conducted with 15 experts to form a test version of the behavioral assessment scale for medication management plans in women of childbearing age with epilepsy (including 27 items and 5 dimensions). Convenience sampling was used to conduct surveys among women of childbearing age with epilepsy in some tertiary hospitals in Chuxiong, Shenzhen and Wuhan from February to May 2024 (the first time) and from June to October 2024 (the second time). ResultsThe effective recovery rates of the two rounds of questionnaires were 95.5% and 94.6%, respectively. The final scale included 24 items and 5 dimensions, with good reliability and validity: the content validity index (S-CVI) was 0.934, Cronbach's α coefficient was 0.876, split-half reliability was 0.819, and test-retest reliability was 0.901; exploratory factor analysis extracted 5 factors (cumulative variance explained rate 73.97%, item load 0.42~0.85), and confirmatory factor analysis showed that the model had good fit (χ2/df=1.849, RMSEA=0.075, CFI, GFI, AGFI, IFI, TLI all>0.85). Conclusion The scale meets the reliability and validity standards and can be used to assess the medication management plans and behaviors of women of childbearing age with epilepsy.