An alarm system has become essential to prevent someone from drowsiness while driving, considering the high incidence due to fatigue or drowsiness. This study offered an alternative to overcome all the limitations provided by the conventional system to detect sleepiness based on the driver's brain electrical activity using wearable electroencephalogram (EEG), which is lighter and easy to use. The EEG signals were collected using EMOTIV Epoc + and then were decomposed into narrowband frequency, such as delta, theta, alpha, and beta using DWT. The relative power, as the result of feature extraction, then were processed further by calculating its variance using the common spatial pattern (CSP) method to optimize the accuracy of extreme learning machine (ELM). Comparison of relative power between awake and drowsy state showed that during the drowsy state, theta-wave, alpha-wave, and beta-wave were tend to be higher than in the awake state. However, despite with the help of ELM, the accuracy was not too high (below 87%). The feature extraction which continued by calculating its variance using CSP algorithm before classified by ELM obtained a high accuracy, even with small amount of data training. This showed that CSP combining with ELM could be useful to shorten the time in training/calibration session, yet still, obtained high accuracy in classifying the awake state and drowsy state. The overall average accuracy of testing ranged from 91.67% to 93.75%. This study could increase the ability of EEG in detecting drowsiness that is important to prevent the risk caused by driving in a drowsy state.

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions – Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

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Post-stroke rehabilitation device is very important nowadays, considering the high rate of disability caused by stroke especially arm function. About 50% of stroke survivors experience the unilateral motor deficits which decreased upper extremity function. Therefore, hand and shoulders therapy are generally performed in advance to support patients' daily activities. Electromyograph (EMG) signals from selective muscles were proven to provide additional power for post-stroke rehabilitation device to recover more quickly because the patient participates actively in rehabilitation. This paper describes a preliminary prototype of upper limb exoskeleton for post-stroke therapy devices utilizes automatic control algorithm to control human arm movement with one degree of freedom based on a myoelectric signal of muscle biceps brachii from their unaffected side. This study used low-cost instruments and digital signal processing, such as IIR low pass filter followed by Kalman filter to generate the myoelectric signal that separated from noise as an input for controlling the DC motor which moved the exoskeleton of arm therapy mechanic. The accuracy of system performance in this study was 95%. Hopefully, this device can help stroke survivors to perform therapy independently without depending on therapists so that rehabilitation will be more effective and efficient.