PeerJ (Mar 2023)

A 3D-printed passive exoskeleton for upper limb assistance in children with motor disorders: proof of concept through an electromyography-based assessment

  • Cristina Sanchez,
  • Laura Blanco,
  • Carmina del Río,
  • Eloy Urendes,
  • Vanina Costa,
  • Rafael Raya

DOI
https://doi.org/10.7717/peerj.15095
Journal volume & issue
Vol. 11
p. e15095

Abstract

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The rehabilitation of children with motor disorders is mainly focused on physical interventions. Numerous studies have demonstrated the benefits of upper function using robotic exoskeletons. However, there is still a gap between research and clinical practice, owing to the cost and complexity of these devices. This study presents a proof of concept of a 3D-printed exoskeleton for the upper limb, following a design that replicates the main characteristics of other effective exoskeletons described in the literature. 3D printing enables rapid prototyping, low cost, and easy adjustment to the patient anthropometry. The 3D-printed exoskeleton, called POWERUP, assists the user’s movement by reducing the effect of gravity, thereby allowing them to perform upper limb exercises. To validate the design, this study performed an electromyography-based assessment of the assistive performance of POWERUP, focusing on the muscular response of both the biceps and triceps during elbow flexion–extension movements in 11 healthy children. The Muscle Activity Distribution (MAD) is the proposed metric for the assessment. The results show that (1) the exoskeleton correctly assists elbow flexion, and (2) the proposed metric easily identifies the exoskeleton configuration: statistically significant differences (p-value = 2.26 ⋅ 10−7 0.8) in the mean MAD value were identified for both the biceps and triceps when comparing the transparent mode (no assistance provided) with the assistive mode (anti-gravity effect). Therefore, this metric was proposed as a method for assessing the assistive performance of exoskeletons. Further research is required to determine its usefulness for both the evaluation of selective motor control (SMC) and the impact of robot-assisted therapies.

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