COVID-19 Prediction Infrastructure Using Deep Learning

Coronavirus can lead to respiratory illnesses ranging from mild to severe, and even death, which makes early detection critical. However, current COVID-19 (Coronavirus Disease 2019) detection methods are not only expensive but also time-consuming. This poses a challenge, especially with an increasing number of patients and demand for testing kits. Waiting for test results for a few days is not ideal, as the outbreak can spread quickly in the meantime. To address this issue, we propose a COVID-19 prediction infrastructure using deep learning. This innovative android-based application uses a Convolutional Neural Network model, trained on a custom dataset with an accuracy of 97 percent, to predict whether COVID-19 is present or not. With this fast and low-cost approach, users can quickly detect COVID-19 and take appropriate actions to reduce the risk of transmission

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

Analyse, modélisation et implémentation de stratégies d’assistance : déploiement d’orthèses cognitives pour les activités instrumentales de la vie quotidienne des traumatisés crâniens

De nos jours, les traumatismes cranio-cérébraux (TCC) sévères sont considérés comme un problème de santé publique au plan mondial. En effet, un TCC sévère engendre des répercussions importantes dans la vie des personnes l’ayant subi. Ces répercussions sont liées aux dysfonctionnements cognitifs, émotionnels et comportementaux. Ces troubles occasionnent une baisse, souvent très importante, de leur indépendance dans la réalisation des Activités Instrumentales de la Vie Quotidienne (AIVQ), telles que préparer un repas, gérer ses finances, utiliser son téléphone, conduire une automobile, faire des achats, etc. Très souvent, les personnes ayant subi un TCC sévère doivent retourner vivre au sein de leur domicile malgré les grandes difficultés liées à leur état. Les TCC sévères ressentiront très souvent le besoin d’assistance pour la réalisation des AIVQ. Cette thèse s’inscrit dans le cadre d’un grand projet de recherche financé par les Instituts de recherche en santé du Canada et le Conseil de Recherches en Sciences Naturelles et en Génie du Canada (CRSNG). En particulier, le Projet de Recherche Concertée sur la Santé (PRCS). L’objectif de cette thèse, au sein de ce projet, consiste à concevoir, représenter, formaliser et implémenter une structure d’assistance cognitive contextuelle et adaptative selon le profil des personnes atteintes de TCC sévère pour la réalisation des AIVQ. Cette assistance favorisera leur indépendance dans la réalisation des AIVQ au sein de leur domicile. La conception de cette assistance cognitive numérique implique un travail interdisciplinaire entre l’ergothérapie et l’informatique, afin de passer de la pratique d’assistance fournie par des cliniciens à la formulation formelle et à l’implémentation. Cette conception s’appuie sur une démarche de conception participative qui sollicite principalement les résidents d’un milieu d’hébergement alternatif domotisé.Abstract: Severe Traumatic Brain Injury (TBI) is considered a public health problem. Indeed, severe TBI causes significant cognitive, emotional and behavioral repercussions that impact the lives of these individuals, particularly their ndependence in Instrumental Activities of Daily Living (IADLs). Individuals who have experienced severe TBI frequently return to live in their homes despite the severe difficulties associated with their condition, though the need for assistance to perform IADLs frequently persists. The objective of this thesis is to design, represent, formalize and implement a context-aware and adaptive structure of cognitive assistance. This assistance is created according to the general needs of individuals with severe TBI for IADL performance. The proposed assistance will promote their independence to perform IADL in a home environment. The design of this cognitive assistance technology involves an interdisciplinary collaboration between occupational therapy and computer science, to evolve from the assistance provided by the clinicians to a formal computer science formulation and implementation. This design is based on a participative design approach that mainly involves TBI residents of a smart alternative housing unit. A prototype of a cognitive orthotic for meal preparation (COOK) was created and deployed within an alternative housing unit. Implementation of this cognitive orthotic lifted the prohibition on use of a stove for meal preparation that had previously been placed on their residents. By allowing these residents to cook independently, COOK has contributed to helping them become more independent in cooking and more confident in their ability to do so

Development and Evaluation of an Assistive Prompting System for People with Traumatic Brain Injury

Cognitive deficits in executive functioning are among the most frequent sequelae after traumatic brain injury (TBI) at all levels of severity. Due to these functional deficits in cognition, individuals with TBI often experience difficulties in performing instrumental activities of daily living (IADL), especially those IADLs that involve a sequence of goal-directed actions. We obtained updated information on the use of assistive technology for cognition (ATC) through a survey study among twenty-nine participants with TBI. Results highlighted the needs to support the development and evaluation of ATC in assisting multi-step tasks. Cooking tasks were selected as a representative for they are cognitively demanding and have been identified essential for living independently. With the recent advance in sensing and smart home technologies, it’s possible to provide context-aware prompts with minimal user inputs. However, limited information is known regarding what types of context-aware prompts are really needed by people with TBI in completing cooking tasks. We compared the effectiveness and usability of current available prompting methods (e.g. paper-based prompting method and user-controlled method) among ten individuals with TBI in their home kitchens. We categorized the nature of problems faced by end-users with both prompting methods in cooking tasks and proposed relevant context-aware solutions. A test-bed Cueing Kitchen with sensing and prompting elements was developed to address these identified needs and to evaluate the feasibility of context-aware ATC interventions in assisting people with TBI with kitchen activities. Sixteen individuals with TBI participated in the study. Results showed that comparing to the conventional user-controlled method, the automatic method decreased the amount of external assistance required by participants, received higher ratings in perceived ease-of-use, and was helpful for decreasing user stress levels. However, the user-controlled method showed strengths in offering participants more flexibility and control on the timing of prompts. The contributions from this dissertation not only developed a context-aware prompting testbed and evaluated the feasibility of an automatic system, but also advanced the guidelines and potential solutions for future development of assistive prompting technology for people with cognitive impairments in sequential tasks