International Journal of Telemedicine and Clinical Practices
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International Journal of Telemedicine and Clinical Practices (2 papers in press)
Systematic review of indoor fall detection systems for the elderly using Kinect by Amina BEN HAJ KHALED, A.L.I. KHALFALLAH, Mohamed Salim BOUHLEL Abstract: The fall of the elderly presents a major health problem as it may cause
fatal injuries. To improve the life quality of the elderly, researchers have
developed several fall detection systems. Several sensors have been used to
overcome this problem. So far, Microsoft Kinect has been the most used camerabased sensor for fall detection. This motion detector can interact with computers
through gestures and voice commands.
In this article, we presented a comprehensive survey of the latest fall detection
research using the Kinect sensor. We provide an overview of the main features
of the two Kinect versions V1 and V2 and compare their performances. Then we
detailed the method used for the articles selection.
We provided a classification of the fall detection techniques to highlight the main
differences between them. Finally, we concluded that it is not enough to evaluate
a system performance under simulated conditions. It is important to test these
approaches on old people who are likely to fall.
Keywords: Depth sensor; elderly health care; fall detection; Kinect V1; Kinect
V2; PRISMA; machine learning.
A Real Time Automated Epileptic Seizure Detection Model for Phenylketonuria Patients Using ANFIS,DWT,ST,CT and EGA by Sumant Mohapatra, Srikanta Patnaik Abstract: Background and objective: One of the most ARSG diseases is a Phenylketonuria (PKU). The patient suffered from the deficiency of blood circulation across brain which shows small epileptic seizure in EEG signal. rnMethods: In this work, three feature extraction methods (Discrete wavelet transform, shearlet transform and contourlet transform) have been used to classify epileptic seizure EEG(PKU-EEG) and raw EEG Signals (non epileptic seizure EEG).The classification between PKU-EEG and raw EEG signals are performed using 9-ruleAdaptive Neuro-fuzzy Inference System (ANFIS) trained with a new enhanced genetic algorithm(EGA). Results: The CT-ANFIS-EGA method outperforms than above methods for the classification of normal and PKU-EEG signals.rnConclusion: This study suggests that the proposed work could be effective for clinical classification of epileptic seizure by PKU in the children from their early childhood ages.rn Keywords: PKU-EEG Signal; Epilepsy; Single gene;; EEG Signal; ANFIS; DWT; ST; CT;EGA.