An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

Esfahlani, Shabnam Sadeghi; Cirstea, Silvia; Sanaei, Alireza; Wilson, George

doi:10.1109/isie.2017.8001435

Cited by 23 publications

(29 citation statements)

References 29 publications

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“…An expanding number of analysts focus on portraying the estimation of rehabilitation intercessions that can be utilized all through the disease, from the underlying side effects to the propelled stages [4]. Our previous research [5], [6] have also shown that integrating off-the-shelf devices with video game rehabilitation program could facilitate a platform to enable…”

Section: Introductionmentioning

confidence: 99%

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

2019

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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 preand 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.

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Section: Introductionmentioning

confidence: 99%

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

2019

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“…Kinect seems to be the most preferred sensor for capturing body parts and following their movement in space, which was used by 15.4% (26/169) of the studies included in the review. Some of the studies used only the Kinect sensor for their systems [26,[62][63][64][65][66][67][68][69][70][71][72][73], whereas others combined it with biosignal capturing devices such as electromyogram (EMG) [24,41,74,75] or a sensing jacket [52] to gain better control of the user's movement for the final goal (ie, rehabilitation). In addition, some studies have used Kinect combined with gaming devices such as VR headsets [76] and a Wii balance board [77] or other devices such as goniometers [78][79][80], Tyromotion Timo plate [77], Xsen 3D sensor [81], body markers [82], and a customized haptic glove [83].…”

Section: Commercial Sensorsmentioning

confidence: 99%

Serious Gaming Technology in Upper Extremity Rehabilitation: Scoping Review

Koutsiana¹,

Ladakis²,

Fotopoulos³

et al. 2020

JMIR Serious Games

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Background: Serious gaming has increasingly gained attention as a potential new component in clinical practice. Specifically, its use in the rehabilitation of motor dysfunctions has been intensively researched during the past three decades. Objective: The aim of this scoping review was to evaluate the current role of serious games in upper extremity rehabilitation, and to identify common methods and practice as well as technology patterns. This objective was approached via the exploration of published research efforts over time. Methods: The literature search, using the PubMed and Scopus databases, included articles published from 1999 to 2019. The eligibility criteria were (i) any form of game-based arm rehabilitation; (ii) published in a peer-reviewed journal or conference; (iii) introduce a game in an electronic format; (iv) published in English; and (v) not a review, meta-analysis, or conference abstract. The search strategy identified 169 relevant articles. Results: The results indicated an increasing research trend in the domain of serious gaming deployment in upper extremity rehabilitation. Furthermore, differences regarding the number of publications and the game approach were noted between studies that used commercial devices in their rehabilitation systems and those that proposed a custom-made robotic arm, glove, or other devices for the connection and interaction with the game platform. A particularly relevant observation concerns the evaluation of the introduced systems. Although one-third of the studies evaluated their implementations with patients, in most cases, there is the need for a larger number of participants and better testing of the rehabilitation scheme efficiency over time. Most of the studies that included some form of assessment for the introduced rehabilitation game mentioned user experience as one of the factors considered for evaluation of the system. Besides user experience assessment, the most common evaluation method involving patients was the use of standard medical tests. Finally, a few studies attempted to extract game features to introduce quantitative measurements for the evaluation of patient improvement. Conclusions: This paper presents an overview of a significant research topic and highlights the current state of the field. Despite extensive attempts for the development of gamified rehabilitation systems, there is no definite answer as to whether a serious game is a favorable means for upper extremity functionality improvement; however, this certainly constitutes a supplementary means for motivation. The development of a unified performance quantification framework and more extensive experiments could generate richer evidence and contribute toward this direction.

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“…A membership function µ A (x) associated with A is a function that maps X into [0, 1] and gives the grade of membership of X as A. The MF in the Vision Lab fuzzy logic system was triangular in the range of [0, 1] [19]. Eqn defines the triangular membership function.…”

Section: B Fuzzy Logic (Fl)mentioning

confidence: 99%

Video Game and Fuzzy Logic to Improve Amblyopia and Convergence Insufficiency

Esfahlani

Shirvani

Esfahlani

2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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Intuitive learning of visual tasks appears to be an assuring exercise for Amblyopia and Convergence Insufficiency (CI) improvement. Amblyopia is a developmental dysfunction of vision identified by poor monocular visual acuity whereas CI is a common binocular/two-eyed vision disorder in which the eyes do not operate near efficiently. This study proposed the development of virtual reality (VR) video game platform, guided through Fuzzy Logic, targeting casualties with amblyopia and CI condition. The game enables precise control over stimulus parameters, and trains contrast sensitivity with the benefits of motivation and reward that maintain practice over long periods. The dichoptic visual training facilitated through the VR headset and game engine with eye tracking and stimulus data collection capability. This non-invasive eye-training exercise program aims to train the patients to overcome the common conditions such as lazy eye and exophoria at near. Our vision lab made of 2D and 3D game environments facilitated exercises, which are recommended by opticians 1 , through three stimulating video game scenarios. The preliminary results of this study have shown that this program has the potential to be adopted for vision therapy. As such in the future study, a randomized clinical trial with participants 9 to 18 years of age will randomly be assigned to receive 12 weeks of vision lab versus home-based pencil push-ups and vision patching exercise for statistical analysis and validation.

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An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

Cited by 23 publications

References 29 publications

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Serious Gaming Technology in Upper Extremity Rehabilitation: Scoping Review

Video Game and Fuzzy Logic to Improve Amblyopia and Convergence Insufficiency

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