Evolutionary Algorithms for a Better Gaming Experience in Rehabilitation Robotics

Andrade, Kléber de Oliveira; Joaquim, Ricardo C.; Caurin, Glauco Augusto de Paula; Crocomo, Márcio Kassouf

doi:10.1145/3180657

Cited by 14 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system considered in this work is a one-DOF platform used for human-robot interaction [12], shown in Fig. 1 Fig.…”

Section: Impedance Optimization a Interaction Controllermentioning

confidence: 99%

See 1 more Smart Citation

A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction

Lahr¹,

Garcia²,

Ajoudani³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptability features. This paper proposes a modelbased evolutionary optimization algorithm to tune the apparent impedance of a wrist rehabilitation device. We used passivity to define boundaries for the possible controller outcomes, limiting the shared autonomy of the robot and ensuring the coupled system stability. The experiment consists of a hardware-in-theloop optimization and a one-degree-of-freedom robot used for wrist rehabilitation. Experimental results showed that the proposed technique could generate customized passive impedance controllers for three subjects. Furthermore, when compared with a constant impedance controller, the method suggested decreased in 20% the root mean square of interaction torques while maintaining stability during optimization.

show abstract

“…The system considered in this work is a one-DOF platform used for human-robot interaction [12], shown in Fig. 1 Fig.…”

Section: Impedance Optimization a Interaction Controllermentioning

confidence: 99%

“…1. Robot for wrist rehabilitation therapy with serious games [12]. Each patient has different muscle stiffness and pain sensitivity, so the robot must adapt for better treatment.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction

Lahr¹,

Garcia²,

Ajoudani³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overviews on the applicability of bio-inspired optimization to games have been published during the last decade [396,397], but new paradigms such as serious games have lately rekindled the interest in this topic and enlarged its application scope far beyond leisure (see e.g. [398] for rehabilitation robotics).…”

Section: Applications Of Bio-inspired Optimizationmentioning

confidence: 99%

Bio-inspired computation: Where we stand and what's next

Ser

Osaba

Molina

et al. 2019

Swarm and Evolutionary Computation

497

106

View full text Add to dashboard Cite

show abstract

“…Few DDA methods have examined real-time difficulty adjustment using sequential data in the context of VR rehabilitation [11,[20][21][22][23]. Our motivation is to propose probabilistic decision-making for adjusting the difficulty based on the patient's movement in order to dynamically modify the game requirement throughout the sessions.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement

Kamikokuryo

Haga

Venture

et al. 2022

Sensors

View full text Add to dashboard Cite

Motor rehabilitation is used to improve motor control skills to improve the patient’s quality of life. Regular adjustments based on the effect of therapy are necessary, but this can be time-consuming for the clinician. This study proposes to use an efficient tool for high-dimensional data by considering a deep learning approach for dimensionality reduction of hand movement recorded using a wireless remote control embedded with the Oculus Rift S. This latent space is created as a visualization tool also for use in a reinforcement learning (RL) algorithm employed to provide a decision-making framework. The data collected consists of motions drawn with wireless remote control in an immersive VR environment for six different motions called “Cube”, “Cylinder”, “Heart”, “Infinity”, “Sphere”, and “Triangle”. From these collected data, different artificial databases were created to simulate variations of the data. A latent space representation is created using an adversarial autoencoder (AAE), taking into account unsupervised (UAAE) and semi-supervised (SSAAE) training. Then, each test point is represented by a distance metric and used as a reward for two classes of Multi-Armed Bandit (MAB) algorithms, namely Boltzmann and Sibling Kalman filters. The results showed that AAE models can represent high-dimensional data in a two-dimensional latent space and that MAB agents can efficiently and quickly learn the distance evolution in the latent space. The results show that Sibling Kalman filter exploration outperforms Boltzmann exploration with an average cumulative weighted probability error of 7.9 versus 19.9 using the UAAE latent space representation and 8.0 versus 20.0 using SSAAE. In conclusion, this approach provides an effective approach to visualize and track current motor control capabilities regarding a target in order to reflect the patient’s abilities in VR games in the context of DDA.

show abstract

Evolutionary Algorithms for a Better Gaming Experience in Rehabilitation Robotics

Cited by 14 publications

References 19 publications

A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction

A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction

Bio-inspired computation: Where we stand and what's next

Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement

Contact Info

Product

Resources

About