Hand pose estimation and movement analysis for occupational therapy

• Ano de defesa: 2021
• Autor(a) principal: Cejnog, Luciano Walenty Xavier
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Computer vision; Estimativa de pose de mão; Hand pose estimation; Occupational therapy; Terapia ocupacional; Visão computacional
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-16022022-161906/

Hand pose estimation is a challenging problem in computer vision with a wide range of applications, especially in human-computer interface. With the development of inexpensive consumer-level depth cameras and the evolution on deep learning techniques, the current state-of-art in the problem is continuously developing and several new methods have been proposed in recent years. Those methods are mostly data-driven and reach good results in standard datasets such as NYU, ICVL and HANDS17. An application that would benefit from the use of computer vision techniques is hand occupational therapy. In chronic diseases like rheumatoid arthritis (RA), the evaluation of the hand functional state is fundamental for the treatment and prevention of finger deformities. One of the procedures for deformity diagnosis is the measurement of movement angles i.e. flexion/extension and abduction/addution, made using goniometers in a process that can be time-consuming and invasive for the patient. The main proposal of this PhD is to fill a gap in the literature by proposing and evaluating the viability of using a framework composed of a 3D low-cost sensor and a 3D hand pose estimation state-of-art method for automatic assessment of rheumatoid arthritis patients. Given depth maps as input, our framework estimates 3D hand joint positions using a deep convolutional neural network. The proposed pose estimation algorithm can be executed in real-time, allowing users to visualise 3D skeleton tracking results at the same time as the depth images are acquired. Once 3D joint poses are obtained, our framework estimates flexion/extension and abduction/adduction angles by applying computational geometry oper- ations. The absence of public datasets with RA patients in the literature makes the estimation of hand poses of patients a challenge for computer vision data-driven methods. We therefore proposed a protocol to acquire new data from groups of patients and control. We performed experiments of identification of RA patients and control sets and also performed comparison with goniometer data. Results show that a method based on Fourier descriptors is able to perform automatic discrimination of hands with Rheumatoid Arthritis (RA) and healthy pa- tients. The angle between joints can be used as an indicative of current movement capabilities and function. The acquisition is much easier, non-invasive and patient-friendly, significantly reducing the evaluation time and offering real-time data for the dynamic movement.