Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-todeploy real-world systems to detect the onset of drowsiness. In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset 1 of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model 2 for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.

Multiple sclerosis (MS) is a disease that affects the central nervous system, which consists of the brain and spinal cord. Although this condition cannot be cured, proper treatment of persons with MS (PwMS) can help control and manage the relapses of several symptoms. In this survey article, we focus on the different technologies used for the assessment and rehabilitation of motor impairments for PwMS. We discuss sensor-based and robot-based solutions for monitoring, assessment and rehabilitation. Among MS symptoms, fatigue is one of the most disabling features, since PwMS may need to put significantly more intense effort toward achieving simple everyday tasks. While fatigue is a common symptom across several neurological chronic diseases, it remains poorly understood for various reasons, including subjectivity and variability among individuals. To this end, we also investigate recent methods for fatigue detection and monitoring. The result of this survey will provide both clinicians and researchers with valuable information on assessment and rehabilitation technologies for PwMS, as well as providing insights regarding fatigue and its effect on performance in daily activities for PwMS.

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