6 research outputs found
Volet clinique de la conception et de lâĂ©valuation dâune technologie dâassistance Ă la prĂ©paration de repas conçue avec et pour des personnes ayant subi un Traumatisme CranioCĂ©rĂ©bral (TCC) grave
Introduction : Au Canada, 100 000 personnes sont victimes d'un traumatisme craniocĂ©rĂ©bral (TCC) annuellement. Les 16-24 ans prĂ©sentent lâun des taux dâincidence les plus Ă©levĂ©s, ce qui signifie quâune grande partie des victimes et ces personnes vivront en moyenne 50 ans avec les sĂ©quelles physiques et cognitives du TCC. De plus, 10% des personnes qui ont subi un traumatisme crĂąnien souffriront des sĂ©quelles dâun TCC grave leur causant dâimportantes difficultĂ©s de fonctionnement, particuliĂšrement au niveau des activitĂ©s plus complexes de la vie quotidienne, comme la prĂ©paration de repas. Les technologies dâassistance Ă la cognition (TAC) ont dĂ©montrĂ© leur pertinence pour faciliter le fonctionnement dans leurs habitudes de vie des personnes vivant avec les sĂ©quelles dâun TCC. Toutefois, aucune TAC spĂ©cifique Ă la prĂ©paration de repas et Ă cette clientĂšle nâest actuellement disponible. MĂ©thodologie : La prĂ©sente thĂšse prĂ©sente le volet clinique de la conception dâune technologie dâassistance Ă la prĂ©paration des repas, conception rĂ©alisĂ©e dans le cadre dâun projet interdisciplinaire joignant les sciences de la rĂ©adaptation et de lâinformatique. Cette technologie dâassistance nommĂ©e COOK (Cognitive Orthosis for coOKing) a Ă©tĂ© dĂ©veloppĂ©e avec et pour des personnes qui ont subi un TCC grave vivant en rĂ©sidence spĂ©cialisĂ©e. Une approche de conception centrĂ©e sur lâutilisateur a dâailleurs Ă©tĂ© retenue et organise le projet en trois grandes Ă©tapes : 1- lâanalyse des besoins 2- le design de la TAC et 3- lâĂ©valuation de ses effets. Une analyse des besoins a dâabord Ă©tĂ© rĂ©alisĂ©e auprĂšs des futurs utilisateurs ainsi quâauprĂšs des acteurs clĂ©s afin de dresser le profil des futurs utilisateurs, dâidentifier les interventions pertinentes pour optimiser leur fonctionnement et pour ensuite traduire ces interventions en exigences cliniques pour faciliter le design. La phase de design a permis de dĂ©velopper une technologie basĂ©e sur les donnĂ©es probantes en rĂ©adaptation cognitive et rĂ©pondant aux besoins spĂ©cifiques des futurs utilisateurs. Ces derniers ont dâailleurs collaborĂ© avec lâĂ©quipe tout au long de cette phase. COOK a ensuite Ă©tĂ© implantĂ© chez les trois participants afin dâĂ©valuer les effets de son utilisation Ă court et long terme (1, 3 et 6 mois post-implantation) et dâamĂ©liorer son utilisabilitĂ©. RĂ©sultats : GrĂące Ă lâanalyse des besoins, trois profils de futurs utilisateurs ont Ă©tĂ© dressĂ©s et les meilleures pratiques en rĂ©adaptation cognitive pour rĂ©pondre Ă leurs besoins spĂ©cifiques ont Ă©tĂ© identifiĂ©es, facilitant ainsi la conception interdisciplinaire de COOK. Avec lâaide de COOK et dâinterventions cliniques complĂ©mentaires, les trois participants ont tous repris la prĂ©paration des repas de façon sĂ©curitaire. De plus, COOK a dĂ©montrĂ© une efficacitĂ© intĂ©ressante et les participants Ă©taient satisfaits de la technologie, sauf auprĂšs dâun participant qui ne voyait pas lâutilitĂ© pour lui. Conclusion : Cette technologie semble donc prometteuse pour la rĂ©adaptation et le maintien Ă domicile de clientĂšles prĂ©sentant des incapacitĂ©s cognitives. Dâautres travaux seront nĂ©cessaires afin dâadapter cette technologie Ă dâautres clientĂšles et diffĂ©rents milieux de vie.Introduction: In Canada, 100,000 people suffer from traumatic brain injury (TBI) each year. The incidence is highest in individuals between 16-24 years of age, which means that people living with TBI will live an average of 50 years with the physical and cognitive sequelae. Of these individuals, 5% will have sustained a severe TBI, which will cause significant difficulties in their functioning, particularly in complex daily activities such as meal preparation. Assistive Technology for Cognition (ATC) has been shown to have high potential to facilitate the functioning of people with TBI. However, no ATC for meal preparation is currently available or suitable for this clientele. Methodology: As part of an interdisciplinary project, combining the disciplines of rehabilitation and computer sciences, COOK (Cognitive Orthosis for coOKing), an assistive technology to support meal preparation, was designed with and for people with severe TBI. This thesis presents the clinical aspect of the conception. For the design phase, a user-centered design methodology was chosen and organized into 3 main steps: 1- ATC needs analysis 2-design, and 3- evaluation of usability. As a result, a needs analysis was first conducted with future users and key stakeholders (3 future users, their relatives, the staff of the living environment and their healthcare professionals, and key external stakeholders). The design addressed the needs of residents with evidence-based practice guidelines in the cognitive rehabilitation field and these were translated into technological features. Future users were constantly consulted throughout the design process. Next, COOK was implemented with 3 future users in order to evaluate and improve its usability (1-, 3- and 6-months post-implementation). In addition, COOK demonstrated interesting efficiency and participants were satisfied with the technology, except for one participant who did not see how COOK could be of use for him. Results: The 3 participants with severe TBI have all resumed safe meal preparation with COOK. This technology has high potential for rehabilitation and home care for clients with cognitive impairments. Further work will be necessary to adapt this technology to other clienteles and different living environments
AN INVESTIGATION INTO CONTEXT-AWARE AUTOMATED SERVICE IN SMART HOME FACILITIES: SEARCH ENGINE AND MACHINE LEARNING WITH SMARTPHONE
Technological advances, in general, coupled with the widespread use of smartphones, create ever more opportunities for mobile applications. This thesis considers the use of such devices within embedded systems to provide automated services in smart home automation. The overall approach links together context-aware data from the physical environment, sensors and actuators for domestic appliances and statistics-based decision-making. A prototype system named âWireless Sensor/Actuator Mobile Computing in the Smart Homeâ (WiSAMCinSH) is developed, which in turns aims to provide services that can benefit clients who are currently dependent on others in their daily activities.
This research highlights and covers the following concepts. Firstly, it addresses the need to improve the prototypical decision-making model by enabling it to take into account context-aware information as conditions under which particular action decisions are appropriate. Secondly, an essential aspect of context-aware performance architecture is that its features must be of high accuracy, explicitly readable and fast. Thirdly, it is necessary to determine which probability-based rules are most effective in generating the dynamic environment to control the home facilities. Finally, it is important to analyse and classify in depth the accuracy of context acquisition and the corresponding context control using cross-validation methods.
A case study uses integrated mobile detection technology to improve the efficiency of mobile applications, taking into account the resource limitations forced on the use of mobile devices. It also utilises other embedded sensing technologies to predict expectations, thereby enabling automatic control of facilities in the home. The main approach is to combine search engines and machine learning to create a system architecture for a context-aware computing service. Among the major challenges are finding the best statistics-based rules for decision-making and overcoming the heterogeneous character of the many devices which are used together. The results achieved show very promising potential for the use of mobile applications within a context-aware computing service, albeit one which still presents problems to be resolved through future research