Cloud-based non-invasive tele-rehabilitation exercise monitoring

Exercise based rehabilitation plays a vital role in the recovery of various conditions, such as stroke, Parkinson’s disease (PD), chronic pain, and so on. Recently, telerehabilitation has become increasingly popular quantitative nature in assessments particularly for systematic monitoring of progress as well as cost saving for the patients as well as for the health care sector at large. However, challenges do exist in implementing a distributed bio-feedback in a cost-effective and efficient way. In this paper, we present the associated conceptual framework of cloud-based tele-rehabilitation system employing affordable non-invasive Microsoft Kinect® allowing patients to perform rehabilitation exercises in non-clinical setting such as home environments without loosing the quality of patients care. More importantly, different from existing tele-rehabilitation systems, our system not only measures whether patients can perform rehabilitation tasks, but also how well they can finish the tasks. Preliminary experiments validate its potential in training healthy subject to perform exercise motions emulating the physical rehabilitation process.

Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.