NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform
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 s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/3281 |
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| author | Konstantinos Mitsopoulos Vasiliki Fiska Konstantinos Tagaras Athanasios Papias Panagiotis Antoniou Konstantinos Nizamis Konstantinos Kasimis Paschalina-Danai Sarra Diamanto Mylopoulou Theodore Savvidis Apostolos Praftsiotis Athanasios Arvanitidis George Lyssas Konstantinos Chasapis Alexandros Moraitopoulos Alexander Astaras Panagiotis D. Bamidis Alkinoos Athanasiou |
| author_facet | Konstantinos Mitsopoulos Vasiliki Fiska Konstantinos Tagaras Athanasios Papias Panagiotis Antoniou Konstantinos Nizamis Konstantinos Kasimis Paschalina-Danai Sarra Diamanto Mylopoulou Theodore Savvidis Apostolos Praftsiotis Athanasios Arvanitidis George Lyssas Konstantinos Chasapis Alexandros Moraitopoulos Alexander Astaras Panagiotis D. Bamidis Alkinoos Athanasiou |
| author_sort | Konstantinos Mitsopoulos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:55:31Z |
| format | Article |
| id | doaj.art-9dada49f2432435c86fc739b4b833a7d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:55:31Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9dada49f2432435c86fc739b4b833a7d2023-11-17T13:48:30ZengMDPI AGSensors1424-82202023-03-01236328110.3390/s23063281NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game PlatformKonstantinos Mitsopoulos0Vasiliki Fiska1Konstantinos Tagaras2Athanasios Papias3Panagiotis Antoniou4Konstantinos Nizamis5Konstantinos Kasimis6Paschalina-Danai Sarra7Diamanto Mylopoulou8Theodore Savvidis9Apostolos Praftsiotis10Athanasios Arvanitidis11George Lyssas12Konstantinos Chasapis13Alexandros Moraitopoulos14Alexander Astaras15Panagiotis D. Bamidis16Alkinoos Athanasiou17Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Design, Production and Management, University of Twente, 7522 NB Enschede, The NetherlandsDepartment of Physiotherapy, International Hellenic University, 57400 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Computer Science, American College of Thessaloniki, 55535 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceMedical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceBackground: 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.https://www.mdpi.com/1424-8220/23/6/3281body–machine interfacehuman–robot interactionneural rehabilitationrobotic gloverobotic jacketserious game |
| spellingShingle | Konstantinos Mitsopoulos Vasiliki Fiska Konstantinos Tagaras Athanasios Papias Panagiotis Antoniou Konstantinos Nizamis Konstantinos Kasimis Paschalina-Danai Sarra Diamanto Mylopoulou Theodore Savvidis Apostolos Praftsiotis Athanasios Arvanitidis George Lyssas Konstantinos Chasapis Alexandros Moraitopoulos Alexander Astaras Panagiotis D. Bamidis Alkinoos Athanasiou NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform Sensors body–machine interface human–robot interaction neural rehabilitation robotic glove robotic jacket serious game |
| title | NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform |
| title_full | NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform |
| title_fullStr | NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform |
| title_full_unstemmed | NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform |
| title_short | NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform |
| title_sort | neurosuitup system architecture and validation of a motor rehabilitation wearable robotics and serious game platform |
| topic | body–machine interface human–robot interaction neural rehabilitation robotic glove robotic jacket serious game |
| url | https://www.mdpi.com/1424-8220/23/6/3281 |
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