robotics Article Feasibility and Performance Validation of a Leap Motion Controller for Upper Limb Rehabilitation Marcus R. S. B. de Souza 1 , Rogério S. Gonçalves 2, * and Giuseppe Carbone 3   Citation: de Souza, M.R.S.B.; Gonçalves, R.S.; Carbone, G. Feasibility and Performance Validation of a Leap Motion Controller for Upper Limb Rehabilitation. Robotics 2021, 10, 130. https://doi.org/10.3390/ robotics10040130 Academic Editor: Abhilash Pandya Received: 3 November 2021 Accepted: 28 November 2021 Published: 4 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Federal Institute of Espírito Santo (IFES), Vitória 29173-087, Brazil; mrsbs.mecatronica@gmail.com 2 School of Mechanical Engineering, Federal University of Uberlândia, Uberlândia 38408-016, Brazil 3 Department of Mechanical, Energy, and Management Engineering, University of Calabria, 87036 Cosenza, Italy; giuseppe.carbone@unical.it * Correspondence: rsgoncalves@ufu.br Abstract: The leap motion controller is a commercial low-cost marker-less optical sensor that can track the motion of a human hand by recording various parameters. Upper limb rehabilitation therapy is the treatment of people having upper limb impairments, whose recovery is achieved through continuous motion exercises. However, the repetitive nature of these exercises can be interpreted as boring or discouraging while patient motivation plays a key role in their recovery. Thus, serious games have been widely used in therapies for motivating patients and making the therapeutic process more enjoyable. This paper explores the feasibility, accuracy, and repeatability of a leap motion controller (LMC) to be applied in combination with a serious game for upper limb rehabilitation. Experimental feasibility tests are carried out by using an industrial robot that replicates the upper limb motions and is tracked by using an LMC. The results suggest a satisfactory performance in terms of tracking accuracy although some limitations are identified and discussed in terms of measurable workspace. Keywords: upper limb rehabilitation; serious games; leap motion controller; performance validation 1. Introduction Stroke is a leading cause of long-term disability and leaves a significant number of individuals with cognitive and motor deficits. The most frequent consequence of stroke is the paralysis of the upper limb, as mentioned, for example, in [1–3]. Rehabilitation training is the most effective way to reduce motor impairments in stroke patients and robots can be suited for this purpose, since they can train patients for long durations with high accuracy [4–6]. Effective stroke rehabilitation should be early, intense, and repetitive, which can lead to problems with motivation and patient involvement, since conventional rehabilitation therapy is often monotonous and repetitive [7]. A new paradigm is emerging in the rehabilitation field characterized by the systematic use of serious games [8]. Serious games aim to provide a truly interactive rehabilitation experience and generate a high level of motivation in patients. The rehabilitation using serious games are more attractive softening the notion of effort during the exercises [9]. A leap motion controller has been preliminarily proposed in combination with se- rious games for upper limb rehabilitation, such as outlined in [10–14]. However, the proposed applications lack a reliable information on the expected performance of leap motion controllers when applied to specific rehabilitation tasks within serious games interactive environments. The paper is structured as follows: Section 2 provides a critical review on the specifica- tions for a LMC followed by a brief review on the kinesiology and rehabilitation of upper limb. The methodology of experiments is presented in Section 4. The detailed results are analyzed and discussed in Section 5. Finally, key conclusions and recommendations are drawn in Section 6. Robotics 2021, 10, 130. https://doi.org/10.3390/robotics10040130 https://www.mdpi.com/journal/robotics