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. 2020 Feb 6;20(3):858.
doi: 10.3390/s20030858.

Connected Elbow Exoskeleton System for Rehabilitation Training Based on Virtual Reality and Context-Aware

Daniel H de la Iglesia  1   2 , André Sales Mendes  1 , Gabriel Villarrubia González  1 , Diego M Jiménez-Bravo  1 , Juan F de Paz Santana  1
Affiliations

Connected Elbow Exoskeleton System for Rehabilitation Training Based on Virtual Reality and Context-Aware

Daniel H de la Iglesia et al. Sensors (Basel). .

Abstract

Traditional physiotherapy rehabilitation systems are evolving into more advanced systems based on exoskeleton systems and Virtual Reality (VR) environments that enhance and improve rehabilitation techniques and physical exercise. In addition, due to current connected systems and paradigms such as the Internet of Things (IoT) or Ambient Intelligent (AmI) systems, it is possible to design and develop advanced, effective, and low-cost medical tools that patients may have in their homes. This article presents a low-cost exoskeleton for the elbow that is connected to a Context-Aware architecture and thanks to a VR system the patient can perform rehabilitation exercises in an interactive way. The integration of virtual reality technology in rehabilitation exercises provides an intensive, repetitive and task-oriented capacity to improve patient motivation and reduce work on medical professionals. One of the system highlights is the intelligent ability to generate new exercises, monitor the exercises performed by users in search of progress or possible problems and the dynamic modification of the exercises characteristics. The platform also allows the incorporation of commercial medical sensors capable of collecting valuable information for greater accuracy in the diagnosis and evolution of patients. A case study with real patients with promising results has been carried out.

Keywords: edge computing; elbow rehabilitation; exoskeleton; virtual reality.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Designed device deployed on a user’s arm. The device is fixed through the three velcro straps.
Figure 2
Figure 2
Measurement of the force (F) exerted by the user through the exoskeleton and measured through a Gauge type load cell that is amplified through a Wheatstone bridge.
Figure 3
Figure 3
Components of the exoskeleton of a one degree of freedom (DOF) EXOMedical: (a) ESP8266 Wireless microcontroller; (b) 15 kg servo motor; (c) Load cell up to 25 kg; (d) 3D printed parts in PLA; (e) Two 18,650 lithium batteries of 3.7v 3400 mah each.
Figure 4
Figure 4
General diagram of the Context-Aware architecture for the EXOMedical system.
Figure 5
Figure 5
Main components of the virtual reality environment based on the Unity 3D engine and the Oculus system.
Figure 6
Figure 6
The capture of one of the exercises performed through the Unity 3D environment to be reproduced in the Virtual Reality environment. The user must move the disk and deposit it on the table as part of an initiation exercise in the system.
Figure 7
Figure 7
Virtual Reality version of the Buzz Wire Game performed in the case of study. In the center, it is possible to observe the ring (in orange) that the user must transport from the starting point (in red) to the endpoint (in green).
Figure 8
Figure 8
Patient performing a rehabilitation exercise through the VR environment and the exoskeleton for the elbow: (a) The user performing exercises with simulated weight through the control of the VR device; (b) The user performing exercises with real weight and exoskeleton assistance.
Figure 8
Figure 8
Patient performing a rehabilitation exercise through the VR environment and the exoskeleton for the elbow: (a) The user performing exercises with simulated weight through the control of the VR device; (b) The user performing exercises with real weight and exoskeleton assistance.
Figure 9
Figure 9
Performance and difficulty experienced by patient 3 during the 4 sessions with simulated weight: (a) Session 1; (b) Session 2; (c) Session 3; (d) Session 4.
Figure 10
Figure 10
Performance and difficulty experienced by patient 3 during the 4 sessions with real weight: (a) Session 1; (b) Session 2; (c) Session 3; (d) Session 4.
Figure 11
Figure 11
The average difficulty of each patient in the four sessions performed; (a) With simulated weight; (b) With real weight.
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