Stroke patients require a long recovery. One success of the treatment given is the evaluation and monitoring during recovery. One device for monitoring the development of post-stroke patients is Electroencephalogram (EEG). This research proposed a method for extracting variables of EEG signals for post-stroke patient analysis using Wavelet and Self-Organizing Map Kohonen clustering. EEG signal was extracted by Wavelet to obtain Alpha, beta, theta, gamma, and Mu waves. These waves, the amplitude and asymmetric of the symmetric channel pairs are features in Self Organizing Map Kohonen Clustering. Clustering results were compared with actual clusters of post-stroke and no-stroke subjects to extract significant variable. These results showed that the configuration of Alpha, Beta, and Mu waves, amplitude together with the difference between the variable of symmetric channel pairs are significant in the analysis of post-stroke patients. The results gave using symmetric channel pairs provided 54-74% accuracy.

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Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings.

Low back pain (LBP) is one of the most common musculoskeletal disorders that can impair daily activities. The causes of LBP are mostly non-specific, including the abnormalities in the soft tissues or form of muscles, ligament injuries, and muscle spasms or fat igue. One of the causes of LBP is carrying excess weight. If the load carried exceeds 15% of body weight, the person who carried the weight will experience musculoskeletal disorders. Students may carry a backpack with a weight that exceeds the recommended weight and this weight will put pressures on the muscles, ligaments, and tendons, causing tension and acute neck pain. These students then will have a higher risk of suffering LBP. The aimed of this study was to identify the relationship between the backpack load and the incidence of low back pain in students. Using the cross-sectional analytical method, this study was performed on a total of 63 students from a senior high school in Cimahi, Indonesia. Subjects were selected using the multistage random sampling and data on the sociodemographic, backpack load, and low back pain were collected and analyzed using the chi-square test. Results showed that 45 students (71.4%) experienced LBP, while the average weight of the backpack carried by these students 6.59 kg (p <0.001). Thus, there is a significant relationship between the excessive backpack weight and the incidence of LBP among students