An interactive and low-cost full body rehabilitation framework based on 3D immersive serious games

Development of a virtual reality system usability questionnaire (VRSUQ)

Virtual reality (VR) has gained significant attention as a technology that provides immersive experiences similar to the real world. In order for a VR system to be accepted, usability needs to be guaranteed. Accordingly, VR-related researchers are continuing their efforts to improve VR systems by conducting usability evaluations. However, existing studies have limitations in that they cannot comprehensively evaluate the detailed properties of VR systems by using questionnaires developed for general product usability evaluation or focusing only on some usability aspects of VR systems. This suggests it may be difficult to fully capture usability issues in a VR system, and that it is necessary to develop a usability evaluation tool that reflects the specific characteristics of the VR system. Therefore, this study develops and proposes the Virtual Reality System Usability Questionnaire (VRSUQ). In the development of the questionnaire, items were structured based on a literature review and discussions with experts. To account for the diverse characteristics of VR systems, the validity of the questionnaire was verified through both exploratory and confirmatory factor analysis, utilizing data obtained from three distinct experimental studies that employed different VR systems. In addition, by comparing the results of VRSUQ with the results from the System Usability Scale, which is widely used for perceived usability evaluation, alternative possibilities for using VRSUQ are presented. Further testing on various VR platforms is needed to ensure the reliability and validity of VRSUQ, and as results from using VRSUQ are accumulated, it is expected to be widely used as a more powerful and robust VR-specific perceived usability evaluation tool.

The Technology to Enhance Patient Motivation in Virtual Reality Rehabilitation: A Review

Virtual reality (VR) technology has experienced a steady rise and has been widely applied in the field of rehabilitation. The integration of VR technology in rehabilitation has shown promising results in enhancing their motivation for treatment, thereby enabling patients to actively engage in rehab training. Despite the advancement, there is a dearth of comprehensive summary and analysis on the use of VR technology to enhance patient motivation in rehabilitation. Thus, this narrative review aims to evaluate the potential of VR technology in enhancing patient motivation during motor rehabilitation training. This review commences with an explanation of how enhancing motivation through the VR rehabilitation system could improve the efficiency and effectiveness of rehabilitation training. Then, the technology was analyzed to improve patient motivation in the present VR rehabilitation system in detail. Furthermore, these technologies are classified and summarized to provide a comprehensive overview of the state-of-the-art approaches for enhancing patient motivation in VR rehabilitation. Findings showed VR rehabilitation training utilizes game-like exercises to enhance the engagement and enjoyment of rehabilitation training. By immersing patients in a simulated environment with multisensory feedback, VR systems offer a unique approach to rehabilitation that can lead to improved patient motivation. Both ultimately lead to improved patient outcomes, which is not typically achievable with traditional rehabilitation methods. The review concludes that VR rehabilitation presents an opportunity to improve patient motivation and adherence to long-term rehabilitation training. However, to further enhance patient self-efficacy, VR rehabilitation should integrate psychology and incorporate methods. Moreover, it is necessary to build a game design theory for rehabilitation games, and the latest VR feedback technology should also be introduced.

Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection

Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study introduces a new Deep Neural Network for identifying ASD in children through gait analysis, using features extracted from frames composing video recordings of their walking patterns. The innovative method presented herein is based on imagery and combines gait analysis and deep learning, offering a noninvasive and objective assessment of neurodevelopmental disorders while delivering high accuracy in ASD detection. Our model proposes a bimodal approach based on the concatenation of two distinct Convolutional Neural Networks processing two feature sets extracted from the same videos. The features obtained from the convolutions of both networks are subsequently flattened and merged into a single vector, serving as input for the fully connected layers in the binary classification process. This approach demonstrates the potential for effective ASD detection in children through the combination of gait analysis and deep learning techniques.

Artificial intelligence in physical rehabilitation: A systematic review

BACKGROUND

Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

METHODS

We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

RESULTS

We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

CONCLUSION

Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Fast Three-Dimensional Posture Reconstruction of Motorcyclists Using OpenPose and a Custom MATLAB Script

Ergonomics focuses on the analysis of the interaction between human beings and their working environment. During the riding of a motorbike, ergonomics studies the rider's posture on the motorbike. An incorrect posture can lead to physical and psychological discomfort, and can affect the perception of risk and the handling of the motorcycle. It is important for motorcyclists to adopt a good riding posture, for their health and road safety. The aim of this work is to propose a fast, cheap, and sufficiently robust method for the 3D reconstruction of the posture assumed by a motorcyclist. The stereo vision and the application of OpenPose made it possible to obtain a 3D reconstruction of the key points, and their evolution over time. The evaluation of the distances between the 3D key points, which represent the length of the various parts of the body, appears to remain sufficiently stable over time, and faithful to the real distances, as taken on the motorcyclist themself. The 3D reconstruction obtained can be applied in different fields: ergonomics, motorsport training, dynamics, and fluid dynamics analysis.

A Review of Hand Function Rehabilitation Systems Based on Hand Motion Recognition Devices and Artificial Intelligence

The incidence of stroke and the burden on health care and society are expected to increase significantly in the coming years, due to the increasing aging of the population. Various sensory, motor, cognitive and psychological disorders may remain in the patient after survival from a stroke. In hemiplegic patients with movement disorders, the impairment of upper limb function, especially hand function, dramatically limits the ability of patients to perform activities of daily living (ADL). Therefore, one of the essential goals of post-stroke rehabilitation is to restore hand function. The recovery of motor function is achieved chiefly through compensatory strategies, such as hand rehabilitation robots, which have been available since the end of the last century. This paper reviews the current research status of hand function rehabilitation devices based on various types of hand motion recognition technologies and analyzes their advantages and disadvantages, reviews the application of artificial intelligence in hand rehabilitation robots, and summarizes the current research limitations and discusses future research directions.

Artificial Intelligence-Driven Serious Games in Healthcare: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.39840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Artificial Intelligence (AI)-driven serious games have been used in healthcare to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state in order to plan on how to leverage them in the current and future healthcare needs.

OBJECTIVE

The current study aimed to explore the features of AI-driven serious games in healthcare as reported by previous research.

METHODS

We carried out a scoping review to achieve the above-mentioned objective. The most popular databases in information technology and health fields (e.g., MEDLINE and IEEE Xplore) were searched using keywords related to serious games and AI. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently performed the study selection process. Three reviewers independently used Microsoft Excel to extract data from the included studies. A narrative approach was used for data synthesis.

RESULTS

The search process returned 1470 records. Of these records, 46 met all eligibility criteria. 60 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games in most of the studies were used for rehabilitation. The serious games in the majority of the included studies can be played by only single player. Most serious games were played on standalone devices (offline games). The most common genres of serious games were role-playing games, puzzle games, and platformer games. Unity was the most prominent game engine used to develop serious games. Personal computers (PCs) were the most common platforms used to play serious games. The most common algorithms used in the included studies were Support Vector Machine (SVM), Convolutional Neural Network (CNN), Artificial Neural Networks (ANN), and Random Forest (RF). The most common purposes of AI were the detection of disease and the evaluation of user's performance. The dataset size ranged from 36 to 795,600, with an average of about 52,124. The most common validation techniques used in the included studies were K-fold cross-validation and training test split validation. Accuracy was the most commonly used metric to evaluate the performance of AI models.

CONCLUSIONS

The last decade witnessed an increase in the development of AI-driven serious games for healthcare purposes and targeting various health conditions and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. While the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.

Bioinspired Stretchable Fiber-Based Sensor toward Intelligent Human-Machine Interactions

Wearable integrated sensing devices with flexible electronic elements exhibit enormous potential in human-machine interfaces (HMI), but they have limitations such as complex structures, poor waterproofness, and electromagnetic interference. Herein, inspired by the profile of Lindernia nummularifolia (LN), a bionic stretchable optical strain (BSOS) sensor composed of an LN-shaped optical fiber incorporated with a stretchable substrate is developed for intelligent HMI. Such a sensor enables large strain and bending angle measurements with temperature self-compensation by the intensity difference of two fiber Bragg gratings' (FBGs') center wavelength. Such configurations enable an excellent tensile strain range of up to 80%, moreover, leading to ultrasensitivity, durability (≥20,000 cycles), and waterproofness. The sensor is also capable of measuring different human activities and achieving HMI control, including immersive virtual reality, robot remote interactive control, and personal hands-free communication. Combined with the machine learning technique, gesture classification can be achieved using muscle activity signals captured from the BSOS sensor, which can be employed to obtain the motion intention of the prosthetic. These merits effectively indicate its potential as a solution for medical care HMI and show promise in smart medical and rehabilitation medicine.

A systematic review on the usability of robotic and virtual reality devices in neuromotor rehabilitation: patients' and healthcare professionals' perspective

BACKGROUND

The application of virtual reality (VR) and robotic devices in neuromotor rehabilitation has provided promising evidence in terms of efficacy, so far. Usability evaluations of these technologies have been conducted extensively, but no overviews on this topic have been reported yet.

METHODS

A systematic review of the studies on patients' and healthcare professionals' perspective through searching of PubMed, Medline, Scopus, Web of Science, CINAHL, and PsychINFO (2000 to 2021) was conducted. Descriptive data regarding the study design, participants, technological devices, interventions, and quantitative and qualitative usability evaluations were extracted and meta-synthetized.

RESULTS

Sixty-eight studies were included. VR devices were perceived as having good usability and as a tool promoting patients' engagement and motivation during the treatment, as well as providing strong potential for customized rehabilitation sessions. By contrast, they suffered from the effect of learnability and were judged as potentially requiring more mental effort. Robotics implementation received positive feedback along with high satisfaction and perceived safety throughout the treatment. Robot-assisted rehabilitation was considered useful as it supported increased treatment intensity and contributed to improved patients' physical independence and psychosocial well-being. Technical and design-related issues may limit the applicability making the treatment difficult and physically straining. Moreover, cognitive and communication deficits were remarked as potential barriers.

CONCLUSIONS

Overall, VR and robotic devices have been perceived usable so far, reflecting good acceptance in neuromotor rehabilitation programs. The limitations raised by the participants should be considered to further improve devices applicability and maximise technological rehabilitation effectiveness.

TRIAL REGISTRATION

PROSPERO registration ref. CRD42021224141 .

Kinect-Based Rehabilitation Systems for Stroke Patients: A Scoping Review

Method

This study was conducted according to Arksey and O'Malley's framework. To investigate the evidence on the effects of Kinect-based rehabilitation, a search was executed in five databases (Web of Science, PubMed, Cochrane Library, Scopus, and IEEE) from 2010 to 2020.

Results

Thirty-three articles were finally selected by the inclusion criteria. Most of the studies had been conducted in the US (22%). In terms of the application of Kinect-based rehabilitation for stroke patients, most studies had focused on the rehabilitation of upper extremities (55%), followed by balance (27%). The majority of the studies had developed customized rehabilitation programs (36%) for the rehabilitation of stroke patients. Most of these studies had noted that the simultaneous use of Kinect-based rehabilitation and other physiotherapy methods has a more noticeable effect on performance improvement in patients.

Conclusion

The simultaneous application of Kinect-based rehabilitation and other physiotherapy methods has a stronger effect on the performance improvement of stroke patients. Better effects can be achieved by designing Kinect-based rehabilitation programs tailored to the characteristics and abilities of stroke patients.

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

Biofeedback Applied to Interactive Serious Games to Monitor Frailty in an Elderly Population

This article proposes an example of a multiplatform interactive serious game, which is an additional tool and assistant used in the rehabilitation of patients with musculoskeletal system problems. In medicine, any actions and procedures aimed at helping the rehabilitation of patients should entail the most comfortable, but at the same time, effective approach. Regardless of how these actions are orientated, whether for rehabilitation following surgery, fractures, any problems with the musculoskeletal system, or just support for the elderly, rehabilitation methods undoubtedly have good goals, although often the process itself can cause all kinds of discomfort and aversion among patients. This paper presents an interactive platform which enables a slightly different approach to be applied in terms of routine rehabilitation activities and this will help make the process more exciting. The main feature of the system is that it works in several ways: for normal everyday use at home, or for more in-depth observation of various biological parameters, such as heart rate, temperature, and so on. The basic component of the system is the real-time tracking system of the body position, which constitutes both a way to control the game (controller) and a means to analyze the player’s activity. As for the closer control of rehabilitation, the platform also provides the opportunity for medical personnel to monitor the player in real time, with all the data obtained from the game being used for subsequent analysis and comparison. Following several laboratory tests and feedback analysis, the progress indicators are quite encouraging in terms of greater patient interest in this kind of interaction, and effectiveness of the developed platform is also on average about 30–50% compared to conventional exercises, which makes it more attractive in terms of patient support.

The Influence of Virtual Reality Head-Mounted Displays on Balance Outcomes and Training Paradigms: A Systematic Review

Background: Falls are the leading causes of (non)fatal injuries in older adults. Recent research has developed interventions that aim to improve balance in older adults using virtual reality (VR).

Purpose: We aimed to investigate the validity, reliability, safety, feasibility, and efficacy of head mounted display (HMD) systems for assessing and training balance in older adults.

Methods: We searched EBSCOhost, Scopus, Web of Science, and PubMed databases until 1 September 2020 to find studies that used HMD systems for assessing or training balance. The methodological quality was assessed using a modified version of Downs and Black. We also appraised the risk of bias using Risk of Bias Assessment tool for Non-randomized Studies (RoBANS).

Results: A total of 19 articles (637 participants) were included for review. Despite heterogenous age ranges and clinical conditions across studies, VR HMD systems were valid to assess balance and could be useful for fall prevention and for improving postural control and gait patterns. These systems also have the capacity to differentiate healthy and balance-impaired individuals. During VR versions of traditional balance tests, older adults generally acquire a cautious behavior and take more time to complete the tasks.

Conclusion: VR HMD systems can offer ecologically valid scenarios to assess and train functional balance and can be used alone or in addition to other interventions. New norms and protocols should be defined according to participants' age, health status, and severity of their illness when using VR HMD systems for balance assessment and training. For safe and feasible training, attention must be given to display type, VR elements and scenarios, duration of exposure, and system usability. Due to high risk of bias and overall poor quality of the studies, further research is needed on the effectiveness of HMD VR training in older adults.

LieToMe: An Ensemble Approach for Deception Detection from Facial Cues

Deception detection is a relevant ability in high stakes situations such as police interrogatories or court trials, where the outcome is highly influenced by the interviewed person behavior. With the use of specific devices, e.g. polygraph or magnetic resonance, the subject is aware of being monitored and can change his behavior, thus compromising the interrogation result. For this reason, video analysis-based methods for automatic deception detection are receiving ever increasing interest. In this paper, a deception detection approach based on RGB videos, leveraging both facial features and stacked generalization ensemble, is proposed. First, a face, which is well-known to present several meaningful cues for deception detection, is identified, aligned, and masked to build video signatures. These signatures are constructed starting from five different descriptors, which allow the system to capture both static and dynamic facial characteristics. Then, video signatures are given as input to four base-level algorithms, which are subsequently fused applying the stacked generalization technique, resulting in a more robust meta-level classifier used to predict deception. By exploiting relevant cues via specific features, the proposed system achieves improved performances on a public dataset of famous court trials, with respect to other state-of-the-art methods based on facial features, highlighting the effectiveness of the proposed method.

Bodyprint-A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

Design of a 3D Platform for Immersive Neurocognitive Rehabilitation

In recent years, advancements in human–computer interaction (HCI) have enabled the development of versatile immersive devices, including Head-Mounted Displays (HMDs). These devices are usually used for entertainment activities as video-gaming or augmented/virtual reality applications for tourist or learning purposes. Actually, HMDs, together with the design of ad-hoc exercises, can also be used to support rehabilitation tasks, including neurocognitive rehabilitation due to strokes, traumatic brain injuries, or brain surgeries. In this paper, a tool for immersive neurocognitive rehabilitation is presented. The tool allows therapists to create and set 3D rooms to simulate home environments in which patients can perform tasks of their everyday life (e.g., find a key, set a table, do numerical exercises). The tool allows therapists to implement the different exercises on the basis of a random mechanism by which different parameters (e.g., objects position, task complexity) can change over time, thus stimulating the problem-solving skills of patients. The latter aspect plays a key role in neurocognitive rehabilitation. Experiments obtained on 35 real patients and comparative evaluations, conducted by five therapists, of the proposed tool with respect to the traditional neurocognitive rehabilitation methods highlight remarkable results in terms of motivation, acceptance, and usability as well as recovery of lost skills.

An open source graphical user interface for wireless communication and operation of wearable robotic technology

INTRODUCTION

Wearable robotic exoskeletons offer the potential to move gait training from the clinic to the community thereby providing greater therapy dosage in more naturalistic settings. To capitalize on this potential, intuitive and robust interfaces are necessary between robotic devices and end users. Such interfaces hold great promise for research if they are also designed to record data from the robot during its use.

METHODS

We present the design and validation of an open source graphical user interface (GUI) for wireless operation of and real-time data logging from a pediatric robotic exoskeleton. The GUI was designed for trained users such as an engineer or clinician. A simplified mobile application is also provided to enable exoskeleton operation by an end-user or their caretaker. GUI function was validated during simulated walking with the exoskeleton using a motion capture system.

RESULTS

Our results demonstrate the ability of the GUI to wirelessly operate and save data from exoskeleton sensors with high fidelity comparable to motion capture.

CONCLUSION

The GUI code, available in a public repository with a detailed description and step-by-step tutorial, is configurable to interact with any robotic device operated by a microcontroller and therefore represents a potentially powerful tool for deployment and evaluation of community based robotics.

A Novel Robot-Aided Upper Limb Rehabilitation Training System Based on Multimodal Feedback

During robot-aided rehabilitation exercises, monotonous, and repetitive actions can, to the subject, feel tedious and tiring, so improving the subject's motivation and active participation in the training is very important. A novel robot-aided upper limb rehabilitation training system, based on multimodal feedback, is proposed in this investigation. To increase the subject's interest and participation, a friendly graphical user interface and diversiform game-based rehabilitation training tasks incorporating multimodal feedback are designed, to provide the subject with colorful and engaging motor training. During this training, appropriate visual, auditory, and tactile feedback is employed to improve the subject's motivation via multi-sensory incentives relevant to the training performance. This approach is similar to methods applied by physiotherapists to keep the subject focused on motor training tasks. The experimental results verify the effectiveness of the designed multimodal feedback strategy in promoting the subject's participation and motivation.

Motor learning and transfer between real and virtual environments in young people with autism spectrum disorder: A prospective randomized cross over controlled trial

Autism spectrum disorder (ASD) is associated with persistent deficits in social communication and social interaction, including impaired multisensory integration, which might negatively impact cognitive and motor skill performance, and hence negatively affect learning of tasks. Considering that tasks in virtual environment may provide an engaging tool as adjuncts to conventional therapies, we set out to compare motor performance between young people with ASD and a typically developing (TD) control group that underwent coincident timing tasks based on Kinect (no physical contact) and on Keyboard (with physical contact) environments. Using a randomized repeated cross-over controlled trial design, 50 young people with ASD and 50 with TD, matched by age and sex were divided into subgroups of 25 people that performed the two first phases of the study (acquisition and retention) on the same device-real or virtual-and then switched to the other device to repeat acquisition and retention phases and finally switched on to a touch screen (transfer phase). Results showed that practice in the virtual task was more difficult (producing more errors), but led to a better performance in the subsequent practice in the real task, with more pronounced improvement in the ASD as compared to the TD group. It can be concluded that the ASD group managed to transfer the practice from a virtual to a real environment, indicating that virtual methods may enhance learning of motor and cognitive skills. A need for further exploration of its effect across a number of tasks and activities is warranted. Autism Res 2020, 13: 307-319. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Individuals with autism spectrum disorder are known to have difficulties with learning motor tasks. Considering that performing motor tasks in virtual environment may be an engaging tool as adjuncts to conventional therapies, we aimed to estimate performance in tasks regardless of physical touch. Results showed that participants had more difficulty using the non-touch task; however, virtual training improved performance on the physical (real) task. This result indicates that virtual methods could be a promising therapeutic approach for the ASD population.