Halim Tannous

La rééducation fonctionnelle à domicile: La rééducation fonctionnelle classique comprend des séances thérapeutiques, des exercices à domicile et des mouvements avec ou sans l'aide de thérapeutes. Cette approche classique souffre de nombreuses limitations, en raison de l’incapacité de l’expert à suivre les séances à domicile du patient et du manque de motivation du patient pour répéter les exercices sans retour. Les jeux sérieux ont été présentés comme une solution à ces problèmes. Premièrement, des exergames ont été développés à l’aide d’une méthodologie de codesign, où patients, experts et développeurs ont pris part aux procédures de conception. Le capteur Kinect a été utilisé pour capturer la cinématique en temps réel au cours de l’exercice. Ensuite, une fusion de données a été étudier entre le capteur Kinect et les unités de mesure inertielles, afin d’augmenter la précision de l’estimation de l’angle des articulations, à l’aide d’une approche système de systèmes. Le système a été évalué par des patients avec différentes pathologies au cours de multiples campagnes d'évaluation. Les résultats obtenus montrent que les jeux sérieux peuvent être une solution pour des pathologies spécifiques. En outre, les experts ont été convaincus de la pertinence clinique de ce dispositif et ont estimé que les données proposées étaient suffisantes pour évaluer la situation du patient. La mise en place de tels systèmes maximiserait l’efficacité du programme de réadaptation. D'autre part, ce système permettrait également de réduire les limitations actuellement présentes dans les programmes de rééducation classiques, permettant aux patients de visualiser leurs mouvements et aux experts de suivre l'exécution de l'exercice à domicile.

The world has been dealing with the COVID-19 pandemic since December 2019 and a lot of effort has focused on tracking the spread of the virus by gathering information regarding testing statistics and generating viral genomic sequences. Unfortunately, there is neither a single comprehensive resource with global historical testing data nor a centralized database with summary statistics of the identified genomic variants.We merged different pre-aggregated historical testing data and complemented them with our manually extracted ones, which consist of 6852 historical test statistics from 76 countries/states unreported in any other dataset, at the date of submission, making our dataset the most comprehensive to date. We also analyzed all publicly deposited SARS-CoV-2 genomic sequences in GISAID and annotated their variants. Both datasets can be accessed through our interactive dashboard which also provides important insights on different outbreak trends across countries and states.The dashboard is available at https://bioinfo.lau.edu.lb/gkhazen/covid19. A daily updated version of the datasets can be downloaded from github.com/KhazenLab/covid19-data.

Biomedical connected objects like kinematic sensors have been commonly used for patient monitoring in many clinical applications. Moreover, serious games have become widely used to improve patients‘ motivation during functional rehabilitation. In this work, we developed and evaluated a new engineering system as a solution for functional rehabilitation at home. A multi-sensor fusion between Kinect camera and inertial sensors was developed to animate a 3D avatar during rehabilitation and to estimate kinematic data of different joints for clinical monitoring. Two serious game scenarios were designed for upper and lower limb rehabilitation. The developed system was evaluated through patient kinematic data and a questionnaire-based approach with a panel of eight post-stroke patients and four clinical experts. The evaluation of the system showed that multi-sensor fusion provides useful data for clinical follow-up. The virtual game scenarios lead to a high level of immersion for patients. Feedbacks from clinical experts concerning the system's GUIs and the clinical relevance of the acquired data for each rehabilitation session are positive. The developed system paves the way to deploy recent technologies, such as multi-sensor fusion and serious games, as a solution for home-based rehabilitation, which can optimize the benefit of the involved patients and medical experts.

Ambient sound monitoring is a widely used strategy to follow older adults, which could help them achieve healthy ageing with comfort and security. In a previous work, we have already developed a smart audio sensor able to recognize everyday life sounds in order to detect activities of daily living (ADL) and distress situations. In this paper, we propose to add a new functionality by analyzing the speech flow to detect the number of people in a room. The proposed algorithms are based on speaker diarization methods. This information can be used to better detect activities of daily life but also to know when the person is home alone. This functionality can also offer more comfort through light, heating and air conditioning adaptation to the number of people in an environment.

Home-based rehabilitation is an essential step to maximize the recovery of patients during their rehabilitation programs. Experts, between scheduled sessions, recommend home-based rehabilitation exercises. However, there is a lack of motivation for patients to perform these repetitive tasks. Serious games have been used recently as a solution for this problem. These games also help improve the patient motivation problem, but most of them do not include cognitive aspects. In this paper, we propose a serious gaming system, for cognitive and functional rehabilitation, implemented using a system of systems approach. The overall system integrates inertial sensors and Kinect cameras to estimate the kinematic data of the patient, and a dance pad to interact with the game.

A multidisciplinary journal on gaming and gamification for health education/promotion, teaching and social change.

Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

Serious games have been established as a complementary tool for conventional rehabilitation. This trend proved to be beneficial for both experts and patients since the first are able to assign and monitor virtual reality games for their patients to be executed at home, and the latter are greatly motivated by the challenges of these games rather than the repeatable and boring rehabilitation movements. In a previous work, we proposed a serious game for lower limb rehabilitation of the musculoskeletal system using the Microsoft Kinect^TM camera and a virtual environment. However, this system suffers from inaccurate kinematic measurement due to various external sources. Therefore, this paper explores various kinematic measurement technics to improve the accuracy of our system. Six real time orientation measurement algorithms were assessed and compared, using multiple types of sensors. The results obtained showed a better performance for our system. In fact, this study reinforced a clearer view on a low cost, real time portable system to be implemented at home.