Using Augmented Reality for real-time feedback to enhance the execution of the squat.

The importance of exercise and strength training has been emphasised, yet it is shown that the number of people who do not reach the average recommended hours of exercise has increased (WHO, 2020). Currently, a range of physical fitness products employs the use of technology. These products focus on providing engaging experiences but do not provide personalised real-time feedback to improve the execution of the exercise and reduce the risk of injuries. Hence, this research aims to explore the effectiveness of AR technology in providing real-time visual feedback for squat motion. Furthermore, which type of visual feedback is most effective for reducing errors in squat performance is also explored. This prototype includes a large screen that shows a mirror image of the participant as they perform squats with four different types of real-time visual feedback implemented. The motion of the participants was captured using the Kinect v2 system. This prototype focuses on giving feedback about the knee valgus error, which commonly occurs during the squat motion. The four visual feedback types implemented are Traffic, Arrow, Avatar, and All-in-One. A user study with twenty participants was conducted to evaluate the feedback methods. The participants performed ten squats for each type of visual feedback, and their performance was measured with the frequency of the good, moderate, and poor squats they performed. A User Experience Questionnaire (UEQ) and a post-experiment interview were also conducted to measure their preferences and opinions regarding visual feedback. The results showed that Arrow outperformed the other conditions in terms of performance, followed by All-in-One, Traffic and Avatar. However, the majority of participants preferred Traffic, Arrow, All-in-One and Avatar in the descending order of preferences. The participants could further be categorised into two groups, a beginner and an advanced group. It was found that the beginner group preferred All-in-One, Arrow, Traffic and Avatar, in descending order. For the advanced group, in descending order, their performance ranked with Arrow to be best and followed by Traffic, All-in-One and Avatar. However, the majority preferred Traffic, followed by Arrow, Avatar and All-in-One. The difference in performance results between the two groups can be attributed to the beginner group participants needing more information to improve their performance. In contrast, the advanced group benefits from a more straightforward and more intuitive visual feedback type since they already have sufficient knowledge. Future work could include a lateral view of the squat motion which would deliver more information to the user. Lastly, this prototype design can be extended to detect other types of errors users often perform during the squat motion or other strength training exercises or sports

Serious game augmented reality 3D for physical rehabilitation

This research consists in the development of a PhysioAR framework (Augmented Reality Physiotherapy) that consider a set of two wearable sensors (Left Controller and Right Controller and Meta/Oculus Quest headset controller for use in natural interactions with a set of AR therapeutic serious games developed on the Unity 3D. The system allows to perform training sessions for hands and fingers, knees and legs motor rehabilitation bearing in mind that the games are for people who have suffered from stroke. The training is part of special care that must be taken for this through the serious games that are properly adapted to be a source of motivation and easy to be played. This FisioAR project includes, two different apps designed, one for calendar and for physiotherapists has a background data with all information needed to do and other to make login in main app and have the possibility to interact with our three types of games specifically designed, developed and implemented for Oculus Quest. Two different mobile apps were constructed on Outsystems platform, where one is destinated to physiotherapists and other is destinated to AVC patient’s. Three Different types of serious games were developed on Unity Platform Engine and all the games have specific contents to be played according with motor and cognitive rehabilitation objectives. The first game called Boxes Game, has six cubes displayed with different colors and six spheres also with six different colors. The main goal of this game is to put the maximum number of spheres in a box with the same color. This game will involve the use of legs, knees and arms and can be easily adapted to each patients’ conditions, making it more or less demanding. The Second Game is called Garden Care Game. Its scenario was made with prefabs (assets) and materials from Unity asset store to simulate a realistic garden, with a watering can, fences and a set of flowers. The main goal of this game is to care the flowers with water. This simple goal is related with the measurement of the wrist rotation made by the patient through wearable sensors while watering each flower. This game as a score for each flower watered. In the Third Game called Puzzle Game, there’s a white screen with the same number of divisions as the existing image blocks in project.Esta pesquisa consiste no desenvolvimento de uma solução do projeto FisioAR baseada em dispositivos vestíveis, combinando um conjunto de sensores vestíveis e controlador de headset para uso em interações naturais com um conjunto de serious games terapêuticos VR desenvolvidos na plataforma de games 3D Unity. O sistema permite realizar treinos de reabilitação motora de mãos e dedos, joelhos e pernas tendo em vista que os jogos são para pessoas que sofreram AVC e devem ser tomados cuidados especiais com isso e que os jogos estão devidamente adaptados para serem mais simples. ser jogado. Este projeto FisioAR tem em todas as implementações, dois aplicativos diferentes projetados, três tipos diferentes de jogos projetados no Oculus Quest. Dois aplicativos diferentes foram construídos na plataforma Outsystems sendo um destinado a fisioterapeutas e outro a pacientes AVC. Três tipos diferentes de jogos foram especialmente projetados no Unity Platform Engine e todos os jogos possuem conteúdos específicos para serem jogados. O primeiro jogo denominado Boxes Game, tem seis cubos apresentados com cores diferentes e seis esferas também com seis cores diferentes. O principal objetivo deste jogo é colocar o número máximo de esferas em uma caixa com a mesma cor e com distância mínima percorrida. Este jogo envolverá o uso de pernas, joelhos e braços e pode ser facilmente adaptado às condições de cada paciente, tornando-o mais ou menos exigente. O segundo jogo é chamado de jogo de cuidado de jardim. Seu cenário foi feito com pré-fabricados e materiais da loja de ativos da unidade para simular um jardim realista, com regador, cercas e um conjunto de flores. O objetivo principal deste jogo é regar as flores. Esse objetivo simples está relacionado à medição da rotação do punho feita pelo paciente por meio de sensores vestíveis ao regar cada flor. Este jogo é uma pontuação para cada flor regada. No terceiro jogo, chamado Puzzle Game, há uma tela branca com o mesmo número de divisões que os blocos de imagem existentes no projeto

Mobile depth sensing technology and algorithms with application to occupational therapy healthcare

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe UK government is striving to shift its current healthcare delivery model from clini-cian–oriented services, to that of patient and self–care–oriented intervention strategies. It seeks to do so through Information Communication (ICT) and Computer Mediated Re-ality Technologies (CMRT) as a key strategy to overcome the ever–increasing scarcity of healthcare resources and costs. To this end, in the UK the use of paper–based information systems have exhibited their limitations in providing apposite care. At the national level, The Royal College of Occupational Therapists (RCOT) identify home visits and modifica-tions as key levers in a multifactorial health programme to evaluate interventions for older people with a history of falling or are identified as being prone to falling. Prescribing Assistive Equipment (AE) is one such mechanism that seeks to reduce the risk of falling whilst promoting the continued independence of physical dexterity and mobility in older adults at home. In the UK, the yearly cost of falls is estimated at £2.3 billion. Further evidence places a 30% to 60% abandonment rate on prescribed AE by and large due to a ‘poor fit’ and measurement inaccuracies. To remain aligned with the national strategy, and assist in the eradication of measurement inaccuracies, this thesis employs Mobile Depth Sensing and Motion Track-ing Devices (MDSMTDs) to assist OTs in in the process of digitally measuring the extrin-sic fall–risk factors for the provision of AE. The quintessential component in this assess-ment lies in the measurement of fittings and furniture items in the home. To digitise and aid in this process, the artefact presented in this thesis employs stereo computer–vision and camera calibration algorithms to extract edges in 3D space. It modifies the Sobel–Feldman convolution filter by reducing the magnitude response and employs the camera intrinsic parameters as a mechanism to calculate the distortion matrix for interpolation between the edges and the 3D point cloud. Further Augmented Reality User Experience (AR-UX) facets are provided to digitise current state of the art clinical guidance and over-lay its instructions onto the real world (i.e., 3D space). Empirical mixed methods assessment revealed that in terms of accuracy, the arte-fact exhibited enhanced performance gains over current paper–based guidance. In terms of accuracy consistency, the artefact can rectify measurement consistency inaccuracies, but there are still a wide range of factors that can influence the integrity of the point-cloud in respect of the device’s point-of-view, holding positions and measurement speed. To this end, OTs usability, and adoption preferences materialise in favour of the artefact. In conclusion, this thesis demonstrates that MDSMTDs are a promising alterna-tive to existing paper–based measurement practices as OTs appear to prefer the digital–based system and that they can take measurements more efficiently and accurately