In clinical practice, upper extremity motor impairments are commonly assessed with disease-specific, subjectively scored and low-resolution rating scales that often do not consider the variations in tasks and environment that are essential aspects of daily life. Augmented reality (AR) systems with contactless tracking of the hand and upper body offer opportunities for objective quantification of motor (dys)function in a challenging, engaging and patient-tailored environment. In this study, we explore the potential of AR for evaluating 1) speed and goal-directedness of movements within the individually determined interaction space, 2) adaptation of hand opening to objects of different sizes, and 3) obstacle avoidance in healthy individuals (N = 10) and two highly prevalent neurological conditions (N = 10 patients with Parkinson’s Disease and N = 10 stroke patients). We successfully implemented three AR games to evaluate these key aspects of motor function. As expected, PD patients moved slower than controls and needed more time for task completion. No differences were observed between stroke patients and controls, perhaps because motor impairments in this patient group were relatively mild. Importantly, usability of our AR system was good and considerably improved compared to our previous study due to more natural and patient-tailored interaction. Although our findings testify to the potential of AR for assessing motor impairments in patients with neurological conditions and provide starting points for further improvement, there are still many steps to be taken towards application in clinical practice.Electronic supplementary materialThe online version of this article (10.1007/s10916-018-1100-9) contains supplementary material, which is available to authorized users.

For a better understanding of how different disorders affect motor function, a uniform, standardized and objective evaluation is a desirable goal for the clinical community. We explore the potential of Augmented Reality (AR) combined with serious gaming and free hand tracking to facilitate objective, cost-effective and patient-friendly methods for evaluation of upper extremity motor dysfunction in different patient groups. In this paper, we describe the design process of the game and the system architecture of the AR framework to meet these requirements. Furthermore, we report our findings from two pilot studies we conducted with healthy people aged over 50. First, we present a usability study (n = 5) on three different modalities of visual feedback for natural hand interaction with AR objects (i. e., no augmented hand, partial augmented hand and a full augmented hand model). The results show that a virtual representation of the fingertips or hand improves the usability of natural hand interaction. Secondly, a study about game engagement is presented. The results of this experiment (n = 8) show that there might be potential for engagement, but usability needs to be improved before it can emerge.

Speech signals have statistically nonstationary properties and cannot be processed properly by means of classical linear parametric models (AR, MA, ARMA). The neural network approach to time series prediction is suitable for learning and recognizing the nonlinear nature of the speech signal. We present a neural implementation of the NARMA model (nonlinear ARMA) and test it on a class of speech signals, spoken by both men and women in different dialects of the English language. The Akaike's information criterion is proposed for the selection of the parameters of the NARMA model.

Contact-free technology has a great potential in different medical areas such as personal health monitoring and telemedicine. One of the physiological parameters that can be measured with this type of technology is the heart rate. The pulse is proportional with the physical effort or mental stress, its measurement being an important issue in sport, medicine and psychology. In this paper we present an analysis of the accuracy of the heart rate detection using a high speed camera for recording a color video with the face of a person. The recordings were done both from frontal view and from profile and they were resampled to different frequencies between 10 and 240 frames per second. From our tests we may conclude that it was not the high frequency but the quality of the videos recorded with a high speed camera that allowed us to reduce the time needed to obtain the heart-rate up to 5 seconds. We have also noticed that the results are influenced by the errors generated in the resampling process of the video signal.