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Klein LC, Chellal AA, Grilo V, Braun J, Gonçalves J, Pacheco MF, Fernandes FP, Monteiro FC, Lima J. Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:530. [PMID: 38257623 PMCID: PMC10820647 DOI: 10.3390/s24020530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 12/29/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
The 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.
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Affiliation(s)
- Luan C. Klein
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Department of Electronics (DAELN), Universidade Tecnológica Federal do Paraná (UTFPR), Campus Curitiba, 80230-901 Curitiba, Brazil
| | - Arezki Abderrahim Chellal
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- School of Science and Technology, Universidade de Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal
| | - Vinicius Grilo
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
| | - João Braun
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal
| | - José Gonçalves
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Maria F. Pacheco
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Florbela P. Fernandes
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Fernando C. Monteiro
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - José Lima
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
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Mitsopoulos K, Fiska V, Tagaras K, Papias A, Antoniou P, Nizamis K, Kasimis K, Sarra PD, Mylopoulou D, Savvidis T, Praftsiotis A, Arvanitidis A, Lyssas G, Chasapis K, Moraitopoulos A, Astaras A, Bamidis PD, Athanasiou A. NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform. SENSORS (BASEL, SWITZERLAND) 2023; 23:3281. [PMID: 36991992 PMCID: PMC10053382 DOI: 10.3390/s23063281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND This article presents the system architecture and validation of the NeuroSuitUp body-machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. METHODS The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. RESULTS Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. CONCLUSIONS Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.
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Affiliation(s)
- Konstantinos Mitsopoulos
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Vasiliki Fiska
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Konstantinos Tagaras
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios Papias
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Panagiotis Antoniou
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Konstantinos Nizamis
- Department of Design, Production and Management, University of Twente, 7522 NB Enschede, The Netherlands
| | - Konstantinos Kasimis
- Department of Physiotherapy, International Hellenic University, 57400 Thessaloniki, Greece
| | - Paschalina-Danai Sarra
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Diamanto Mylopoulou
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Theodore Savvidis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Apostolos Praftsiotis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios Arvanitidis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - George Lyssas
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Konstantinos Chasapis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Alexandros Moraitopoulos
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Alexander Astaras
- Department of Computer Science, American College of Thessaloniki, 55535 Thessaloniki, Greece
| | - Panagiotis D. Bamidis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Alkinoos Athanasiou
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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Herron CW, Fuge ZJ, Kogelis M, Tremaroli NJ, Kalita B, Leonessa A. Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23021014. [PMID: 36679811 PMCID: PMC9866104 DOI: 10.3390/s23021014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/12/2023]
Abstract
In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.
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