Forthcoming articles

International Journal of Telemedicine and Clinical Practices

International Journal of Telemedicine and Clinical Practices (IJTMCP)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Telemedicine and Clinical Practices (3 papers in press)

Regular Issues

  • Systematic review of indoor fall detection systems for the elderly using Kinect   Order a copy of this article
    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   Order a copy of this article
    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.

  • Voice analysis rehabilitation platform based on LSTM algorithm   Order a copy of this article
    by Alessandro Massaro, Giacomo Meuli, Nicola Savino, Angelo Maurizio Galiano 
    Abstract: The proposed work discusses the results of a research project based on the recognition of correct pronounced words and phrases by implementing a web platform implementing an acoustic training model. The acoustic training model is performed by a long short-term memory LSTM algorithm, able to recognise the speech disorder by assigning a score for each test type. The paper discusses the platform design and implementation. The tests are performed for different kind of exercises in rehabilitation patterns. The adopted approach is based on the formulation of acoustic model integrating a training dictionary of correct phonemes to pronounce. The platform enables a real time automatic score of the performed exercises and the test planning. The LSTM training dataset can be enriched by adding new exercise to learn. The output graphical dashboards enforce clinical evaluations and reporting.
    Keywords: speech disorder recognition; long short-term memory; LSTM; telemedicine platform.
    DOI: 10.1504/IJTMCP.2020.10034206