International Journal of Telemedicine and Clinical Practices
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International Journal of Telemedicine and Clinical Practices (6 papers in press)
Systematic review of indoor fall detection systems for the elderly using Kinect by Amina Ben Haj Khaled, Ali Khalfallah, Med 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 camera-based 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 healthcare; fall detection; Kinect V1; Kinect V2; PRISMA; machine learning. DOI: 10.1504/IJTMCP.2021.10038534
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.
Voice analysis rehabilitation platform based on LSTM algorithm 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
Effect of WhatsApp-based reminders on adherence to home exercise program by Chidozie Emmanuel Mbada, Mustapha Alabi Lateef, Adekola Babatunde Ademoyegun, Adewale Isaiah Oyewole, Laminde Maikudi, Clara Fatoye, Francis Fatoye Abstract: This study investigated the effectiveness of WhatsApp-based reminders on home exercise program (HEP) adherence. Forty consenting patients [experimental group (EG) (n = 20); control group (CG) (n = 20)] participated in this study. EG received WhatsApp-based reminder messages on HEP thrice weekly for six weeks while CG received no reminders other than therapists' instructions to carry out HEP. Participants recorded their HEP activities on a provided diary, which was monitored weekly. Number of days of adherence was assessed at weeks 3 and 6. There was significant difference in adherence to HEP in EG (6.85 +- 0.37 vs. 19.95 +- 0.89 vs. 20.43 +- 0.69; p = 0.001) and CG (4.50 +- 0.61 vs. 13.50 +- 1.24 vs. 13.95 +- 1.61; p = 0.001) at baseline, 3rd and 6th week, and between EG and CG at weeks 3 and 6 (t = 12.669; p = 0.001). WhatsApp-based reminders significantly improved adherence to HEP among patients receiving physiotherapy. Keywords: adherence; home exercise program; HEP; mobile applications; physiotherapy; tele-rehabilitation; WhatsApp. DOI: 10.1504/IJTMCP.2022.10044235
Does telephone counselling improve clinic adherence? Findings from a randomised controlled trial in a tertiary centre in Nigeria by Paul Ojieiriaikhi Erohubie, Bawo Onesirosan James, Joyce Ohiole Omoaregba, Sunday Onyemaechi Oriji, Chijioke Chimbo, Omigie Ann Erohubie Abstract: Defaulting from scheduled outpatient clinic visit is common in all medical specialties and is particularly high in mental health clinics. Studies comparing phone call interventions and text-messaging regarding clinic attendance in clinical settings have reported conflicting results. This study assessed the effect of phone call-based adherence counselling on clinic attendance among outpatients with schizophrenia. A double-blind randomised controlled trial was conducted among 86 adult outpatients. Participants in the control group received a short message service (SMS) from the researchers reminding them of their clinic appointment while participants in the intervention group received phone call-based adherence counselling in addition to SMS reminders. Remarkably higher number of participants in the intervention group attended the first clinic appointment compared with the control group. However, the difference was not statistically significant. There was no superiority of a combination of phone call-based brief adherence counselling and SMS over SMS alone in improving clinic attendance. Keywords: telephone counselling; clinic attendance; schizophrenia; randomised controlled trial; short message service; SMS. DOI: 10.1504/IJTMCP.2022.10046366
Comprehending the roles of perceived usefulness and satisfaction in smoking cessation online health communities: a social capital perspective by Chenglong Li Abstract: This research seeks to unravel the roles of perceived usefulness (PU) and satisfaction in smoking cessation online health communities (OHCs). In the research model, user satisfaction and PU are proposed to motivate users knowledge-sharing and recommendation behaviours. Social ties, shared language, shared vision, reciprocity, and commitment are antecedents to PU from a social capital perspective, which leads to user satisfaction with smoking cessation OHCs in turn. The research model is empirically validated with survey data collected from the users of two smoking cessation OHCs. The research results show that both PU and satisfaction affect users knowledge-sharing and recommendation behaviours positively. PU has a significant impact on satisfaction, and PU of smoking cessation OHCs is affected by shared language, shared vision, and commitment. The findings extend the understanding of PU and satisfaction in the context of smoking cessation OHCs and offer practical implications for smoking cessation OHC service providers. Keywords: social capital; perceived usefulness; smoking cessation; online health community; recommendation behaviour; knowledge-sharing; user satisfaction. DOI: 10.1504/IJTMCP.2022.10047612