Window Selection Impact in Human Activity Recognition

Signal segmentation is usually applied in the pre-processing step to make the data analysis easier. Windowing approach is commonly used for signal segmentation. However, it is unclear which type of window should be used to get optimum accuracy in human activity recognition. This study aimed to evaluat e which window type yields the optimum accuracy in human activity recognition. The acceleration data of walking, jogging, and running were collected from 20 young adults. Then, the recognition accuracy of each window types is evaluated and compared to determine the impact of window selection in human movement data. From the evaluation, the overlapping 75% window with 0.1 s length provides the highest accuracy with mean, standard deviation, maximum, minimum, and energy as the features. The result of this study could be used for future researches in relation to human activity recognition.&nbsp

Implicit night sleep monitoring using smartphones

Recent studies show that many people nowadays suffer from sleep disorders, which can severely threaten the public health. Sleep monitoring could play an important role; since it makes it possible to recognize them at the early stages and prevent them. Moreover, there are sort of methods, devices and special sensors as well as mobile phone applications, which try to realize the demand for sleep monitoring. Although all of these techniques require either a special device or sensor to be used or some user interactions, no approach has been proposed, that tracks sleep either in an unobtrusive way or without using any extra sensor. To put it in a nut shell, we have tried in this work to figure out if it is viable, and if so, how efficient it could be to monitor the nightly sleep using smartphones without any need to interact with the phone or without using separate devices and/or sensors.Bisherige Studien zeigen, dass immer mehr Menschen unter Schlafstörungen leiden, was sich negativ auf die Gesundheit auswirken kann. Schlafüberwachung bietet eine Möglichkeit, Schlafstörungen in einer früheren Entstehungsphase zu erkennen und ihnen vorzubeugen. Es existieren bereits zahlreiche Methoden, Geräte und Sensoren, sowie Smartphone Anwendungen mit welchen die Schlafüberwachung durchgeführt werden kann. Der Nachteil aller dieser Techniken liegt darin, dass diese zur Schlafüberwachung entweder einen zusätzlichen Sensor benötigen oder das Eingreifen des Benutzers erfordern. In dieser Arbeit wird die Frage untersucht, ob man mit Hilfe des Smartphones den Schlaf unauffällig überwachen kann, bzw. ob eine Schlafüberwachung ohne Bedarf von zusätzlichen Sensoren und/oder Interaktion mit dem Benutzer möglich ist