Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Sadeghi Esfahlani, Shabnam and Shirvani, Hassan and Butt, Javaid (2019) Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games. IEEE Access, 7. p. 102617. ISSN 2169-3536

[img]
Preview
Text
Published Version
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (11MB) | Preview
[img] Text
Accepted Version
Restricted to Repository staff only
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (7MB) | Request a copy
Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2926118

Abstract

In this paper, an intuitive neuro-rehabilitation video game has been developed employing the fusion of artificial neural networks (ANNs), inverse kinematics (IK), and fuzzy logic (FL) algorithms. The embedded algorithms automatically adjust the game difficulty level based on the player’s interaction with the game. Moreover, it is manifested as an alternative approach for possible movements to improve incorrect positioning through real-time visual feedback on the screen; 52 participants volunteered to engage in the program. Motor assessment scale (MAS) was determined to assess the participants’ functional ability pre- and post-treatments. The system input is received via the Microsoft Kinect, a foot Pedal (Saitek), and the Thalmic Myo armband. The ANN classifier integrates the limb joints orientation, angular velocity, lower arms’ muscle activity, hand gestures, feet sole (plantar) pressure parameters, and the MAS scores to learn from data and predict the improvement following the intervention. The fuzzy input generates a crisp output and provides a personalized rehabilitation program with the potential to be integrated into clinical protocols. Experiments to obtain the input signals and desired outputs were conducted for the learning and validation of the network. The networks pattern recognition, self-organizing map, and non-linear auto-regression analysis performed using feed-forward and Levenberg–Marquardt backpropagation (LMBP) procedure. The results showed the effectiveness of the non-linear auto-regression using the optimized LMBP algorithm to classify and visualize the target categories. Furthermore, the state of the network demonstrates the prediction accuracy exceeding 94%. Clustering algorithm grouped the data based on the similarity. Self-organizing map trained the network to learn the topology of samples with high correlation, presented outputs with high achievement.

Item Type: Journal Article
Keywords: Fuzzy Logic, Neuro-rehabilitation, Pattern Recognition, Inverse Kinematics, Artificial Neural Network, Classification
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 01 Jul 2019 09:47
Last Modified: 14 Nov 2019 16:07
URI: http://arro.anglia.ac.uk/id/eprint/704474

Actions (login required)

Edit Item Edit Item