Browsing by Author "Klein, Luan C."
Now showing 1 - 7 of 7
Results Per Page
Sort Options
- Angle assessment for upper limb rehabilitation: a novel light detection and ranging (LiDAR)-based approachPublication . Klein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius F.S.B.; Braun, João; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, JoséThe accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.
- Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow RehabilitationPublication . Klein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius F.S.B.; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, JoséAngle assessment is crucial in rehabilitation and significantly influences physiotherapists’ decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5◦ and 17◦, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easyto- implement solution.
- Deep learning-based localization approach for autonomous robots in the robotAtFactory 4.0 competitionPublication . Klein, Luan C.; Mendes, João; Braun, João; Martins, Felipe N.; Oliveira, Andre Schneider; Costa, Paulo Gomes da; Wörtche, Heinrich; Lima, JoséAccurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.
- Design and Development of an Omnidirectional Mecanum Platform for the RobotAtFactory 4.0 CompetitionPublication . Braun, João; Baidi, Kaïs; Bonzatto, Luciano; Berger, Guido; Pinto, Milena F.; Kalbermatter, Rebeca B.; Klein, Luan C.; Grilo, Vinicius F.S.B.; Pereira, Ana I.; Costa, Paulo Gomes da; Lima, JoséRobotics competitions are highly strategic tools to engage and motivate students, cultivating their curiosity and enthusiasm for technology and robotics. These competitions encompass various disciplines, such as programming, electronics, control systems, and prototyping, often beginning with developing a mobile platform. This paper focuses on designing and implementing an omnidirectional mecanum platform, encompassing aspects of mechatronics, mechanics, electronics, kinematics models, and control. Additionally, a simulation model is introduced and compared with the physical robot, providing a means to validate the proposed platform.
- Intelligent sensorization system using ML applied to roboticsPublication . Klein, Luan C.; Lima, José; Martins, Felipe Nascimento; Oliveira, André SchneiderA capacidade de se localizar com precisão é fundamental para robôs autônomos. Vários métodos voltados para a solução desse problema foram desenvolvidos ao longo do tempo, incluindo métodos clássicos, marcadores fiduciais e, mais recentemente técnicas de aprendizado de máquina (do inglês, machine learning, ML). Esse trabalho propõe diferentes técnicas de ML para abordar o problema da localização de robôs na competição RobotAtFactory 4.0. Esse estudo abrange desde testes das abordagens em sistemas embebidos, com foco na viabilidade da aplicação desses métodos, na exploração de diversos modelos e abordagens usando ML, até a aplicação de modelos treinados em simulação em ambientes reais. Os resultados experimentais mostraram que os modelos podem ser executados em sistemas embebidos, e diversas técnicas obtiveram resultados com precisão milimétrica. Além disso, a aplicação direta de modelos treinados em simulação para ambiente real se apresentou promisora. Uma das principais vantagens dos modelos de ML é o desvinculo com a dependência do conhecimento prévio da posição exata dos marcadores fiducias.
- A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competitionPublication . Klein, Luan C.; Braun, João; Mendes, João; Pinto, Vítor H.; Martins, Felipe N.; Oliveira, Andre Schneider; Oliveira, Andre Schneider; Wörtche, Heinrich; Costa, Paulo Gomes da; Lima, JoséLocalization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.
- Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case studyPublication . Klein, Luan C.; Braun, João; Martins, Felipe N.; Wörtche, Heinrich; Oliveira, Andre Schneider; Mendes, João; Pinto, Vítor H.; Costa, Paulo Gomes daThe use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.