Serious Games in Autism Spectrum Disorder - An Example of Personalised Design

Over the last decade, several studies evaluated the use of Serious Games as tools to encourage the development of communication, the process of learning, and social behaviour in people with Autism Spectrum Disorder (ASD) alongside traditional therapeutic approaches. The proposed study intends to employ and rate the use of Serious Games to create personalised interactive environments aimed to improve the learning of educational content in children with ASD. In particular, a multidisciplinary team supported the design and development of the serious game to allow a personalised approach

Strumenti di immersive simulation rivolti a persone con Disturbi dello Spettro Autistico

I Disturbi dello Spettro Autistico (Autism Spectrum Disorders – ASD) comprendono un insieme di quadri psicopatologici caratterizzati da diverse difficoltà nelle relazioni intersoggettive, nella comunicazione e nell’inclusione sociale. Diversi studi hanno valutato l’utilizzo di serious games come strumento, da affiancare ai percorsi terapeutici tradizionali, per favorire il potenziamento della comunicazione, del processo di apprendimento, del comportamento sociale e delle abilità psicomotorie in persone con ASD. Lo studio proposto si pone l’obiettivo di adoperare e valutare l’utilizzo di strumenti di immersive simulation per rafforzare e incrementare l’apprendimento di nozioni logico-matematiche elementari in soggetti con ASD. La progettazione e lo sviluppo del serious game sono stati supportati da un team multidisciplinare che ha individuato un gruppo tester e ha determinato gli aspetti essenziali per lo sviluppo dell’applicazione tramite la stesura di un adeguato documento di progettazione. La fase di sviluppo del prototipo è stata supportata dall’utilizzo di Unity come game engine e di 3D Studio Max come software di modellazione 3D. Il prototipo di gioco, sviluppato su più livelli, è stato realizzato per PC ed è attualmente in fase di testing con gli utenti. Dai risultati della prima fase di testing si potrà valutare se siano presenti fattori o elementi da riprogettare per completare lo sviluppo del serious game e avere una prima stima degli effetti in termini di apprendimento e sviluppo di competenze

Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation

Personalized training via serious game to improve daily living skills in pediatric patients with autism spectrum disorder

The majority of people with Autism Spectrum Disorder (ASD) exhibit difficulties in social communication and behavior, which hinder their learning capability, amid others. Among technological solutions for people with ASD, serious games are frequently used to enhance learning of specific skills and instructional contents. However, because of heterogeneity in applications and game design, few studies have investigated their use in training daily activities. This paper presents a 3D personalized serious game we developed and validated to help ASD patients practice with shopping activities. Personalized training is paramount in people with ASD, thus several elements of this game were personalized to improve engagement and therefore the effectiveness of the virtual training. In order to assess the validity of the game, ten subjects (age 11.9 2.7, 20% female) with ASD played ten sessions of the serious game, once per week. The participants underwent a real-life experience pre- and post-training in a real-life supermarket. Changes in daily living skills among participants were evaluated through specific tools: a form based on the International Classification of Functioning, Disability and Health for Children and Youth; and the Vineland Adaptive Behavior Scale II. Significant improvements (p<0.05) were detected in the main skills trained with the serious game, especially in learning the shopping procedure, directing attention, and problem-solving skills. These findings suggest that personalized serious games can represent a prominent tool to enhance daily living skills, but future work should clinically validate their efficacy

Multimodal Feedback in Assisting a Wearable Brain-Computer Interface Based on Motor Imagery

A multimodal sensory feedback was exploited in the present study to improve the detection of neurological phenomena associated with motor imagery. At this aim, visual and haptic feedback were simultaneously delivered to the user of a brain-computer interface. The motor imagery-based brain-computer interface was built by using a wearable and portable electroencephalograph with only eight dry electrodes, a haptic suit, and a purposely implemented virtual reality application. Preliminary experiments were carried out with six subjects participating in five sessions on different days. The subjects were randomly divided into “control group” and “neurofeedback group”. The former performed pure motor imagery without receiving any feedback, while the latter received multimodal feedback as a response to their imaginative act. Results of a cross validation showed that at most 61% of classification accuracy was achieved in performing the pure motor imagination. On the contrary, subjects of the “neurofeedback group” achieved up to 82% mean accuracy, with a peak of 91% in one of the sessions. However, no improvement in pure motor imagery was observed, either when practicing with pure motor imagery or with feedback

A personalised serious game to improve daily living skills in people with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) includes a set of neurodevelopmental chronic disorders characterised by two main categories of symptoms: deficits in social communication and social interaction; restricted patterns of behaviours. The degree of impairment in these two domains can change radically among individuals and ASD is often associated with intellectual disability, psychiatric impairments, and other kinds of comorbidities, hence the high heterogeneity in the clinical presentation of ASD. ASD symptoms appear in early childhood but generally persist throughout life, so ASD is considered a lifelong disorder. As shown in several studies, technology-based interventions such as serious games (SGs) represent an innovative tool to support children and adults with ASD. Serious games motivate the player and facilitate the learning of skills and the training of actions and behaviours that can then be transferred to real life. However, SGs aimed at people with ASD show several limits; in particular, SGs often fail to consider the heterogeneity of the disorder and the different functioning profiles of individuals with ASD, they present a restricted range of topics and genres, and above all, their assessment proves little evidence of skill generalisation from the virtual world to real-life contexts. These limitations do not aid the spread of serious games in rehabilitation contexts and increase the gap between research and practice. The aim of the present thesis is to investigate new methodologies and techniques to improve autonomy in people with ASD through a personalised serious game. More specifically, a rehabilitation SG-based intervention for people with ASD was carried out for enhancing skills related to a specific daily living activity: shopping in a supermarket. The underlying hypothesis of this study is that training with an individualised serious game can improve the learning and generalisation of trained skills in a real-life environment. In order to prove this hypothesis, a sample of ten subjects with ASD, aged between 8 and 16 years, played with a personalised serious game, ShopAut, for ten sessions, one per week, for no more than 30 minutes. Before the training with ShopAut, a real-life experience was observed to evaluate the participants’ performance in a real supermarket. After the training with ShopAut, a second real-life experience was carried out to assess the improvements achieved by the participants. This study involved a multidisciplinary team, namely a biomedical engineer, a neuropsychiatrist, a psychologist, neuro and psychomotor therapists, speech therapists, and occupational therapists. The team planned and coordinated the rehabilitation intervention, designed the SG analyzing the personalized elements that had to be implemented in the game, and assessed and validated the effectiveness of the intervention. The serious game ShopAut was developed integrating an individualised design that provides both the personalisation of the game’s scenario, contents, difficulty, and user interface; and the customisation of game modes, player perspectives, and input devices. It is a three-dimensional game conceptually based on classic 3D life simulation games and provides a realistic shopping experience where the player can practice and engage with, above all, shopping activities, experiment their problem-solving skills, and take on unexpected events. For the assessment of pre- and post-training, an ad hoc form based on the International Classification of Functioning, Disability and Health: children and youth version (ICF-CY) was used to evaluate the participants’ functioning in the supermarket, and a clinical standardised scale, Vineland Adaptive Behavior Scale II (VABS-II), was adopted to measure the ability of the participants to perform daily activities. Moreover, the game performances of the participants were analysed over the game sessions. The personalised design allowed us to individualise the game experience, improving gameplay, playability, and usability, and consequently the learning outcome. Participants found our SG enjoyable and engaging. The results from the real-life experiences show a significant improvement in the real-life performance of the participants, especially in the main skills trained with ShopAut. More specifically, the training helped the participants to maintain their attention on specific actions, to improve problem-solving and orientation skills, and above all to enhance their ability in the shopping activity. Improvements were also proved by positive scores on the VABS-II scales, especially in daily living skills. Overall, the current study provides good evidence for the use of personalised SGs in interventions for children and teens with ASD. In particular, the results suggest that personalised SGs can effectively support rehabilitation interventions for people with ASD to improve generalisation process and autonomy

Simulation model for analysis and management of the now-show in outpatient clinic

n outpatient management, the lead-time is a critical issue due to its important negative effect on healthcare quality perception. In particular, it generates the phenomenon of "no-show": when patients do not attend their scheduled appointments. In this study, we analyze the process of outpatient booking and its critical issues; in particular, we propose a simulation model to evaluate some different approaches. From our results, the lists cleaning can be considered a good tool to manage and reduce the no-sho

A Multidisciplinary Telerehabilitation Approach for Supporting Social Interaction in Autism Spectrum Disorder Families: An Italian Digital Platform in Response to COVID-19

Due to its complexity and high variability in symptomology, autism spectrum disorder (ASD) requires a coordinated and multidisciplinary intervention to better support the different programs over time and to promote social interactions in all contexts of life. Telemedicine can offer a valuable contribution in this regard, providing low-cost and portable applications. In this paper, we presented an Italian project, SUPER, which aimed to foster collaboration and information sharing between ASD families, health services, and schools. SUPER provided a digital platform with several tools that were useful both to enhance general and specific ASD knowledge and to promote personalized programs for children with ASD. We conducted a preliminary user test for the platform with 30 participants (18 therapists and 12 parents of children with ASD) using the system usability scale (SUS). The total mean SUS score (89.2) showed that SUPER is an excellent, usable system. Moreover, we extracted the usability and learnability mean components from the SUS scores, which were 96.1 and 61.7, respectively. Our preliminary results indicate that SUPER is a very user-friendly application and its innovative telemedicine approach could be ahelpful communication and collaboration tool among the different contexts of care for children with ASD

Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning

Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide. A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR), through the dosing of the insulin bolus to inject before meals. The Artificial Pancreas (AP), combining autonomous insulin delivery and blood glucose monitoring, is a promising solution. However, state-of-the-art APs require several information for bolus delivery, such as the estimated carbohydrate intake over the meals. This is mainly related to the limited knowledge of the determinants of PGR. Although meal carbohydrates are mostly considered as the major factor into, uencing PGR, other food components play a relevant role in PGRs, and thus, should be taken into account. Based on these considerations, a study to determine the effect of nutritional factors (i.e., carbohydrates, proteins, lipids, fibers, and energy intake) in the short and middle term on Blood Glucose Levels (BGLs) prediction was conducted by Machine Learning (ML) methods. A ML model able to predict the BGLs after 15, 30, 45, and 60 minutes from the meal leveraging on insulin doses, blood glucose, and nutritional factors in T1D patients on AP systems was implemented. More specifically, to investigate the impact of the nutritional factors on the model predictions, a Feed-Forward Neural Network, was fed with several dispositions of BGLs, insulin, and nutritional factors. Both public and self-produced data were used to validate the proposal. The results suggest that patient-specific information about nutritional factors can be significant for middle term postprandial BGLs predictions

Reproducible Assessment of Valence and Arousal Based on an EEG Wearable Device

An electroencephalography-based detection system of emotional states exploiting few dry channels is proposed. The circumplex model of affect was the reference theory adopted and the standardized dataset International Affective Picture System IAPS was exploited for emotion elicitation. A subset of stimuli polarized on both the valence and the arousal dimension was employed to maximize the effectiveness of the emotion induction. A Self-Assessment Manikin (SAM) was submitted to the subjects after each image to assess the valence and arousal scores of the target emotion. The agreement between the two measures, namely the IAPS scores and the SAM scores was verified through a Bland Altman analysis and a Spearman correlation analysis. An initial screening of the sample allowed to manage the bias caused by depressive and anxiety disorders. The proposed system was experimentally validated. 9 healthy subjects participated in the experimental activity and their EEG signals were acquired through an 8-channel headset. As a result, the best accuracy in the within-subject case of 62.5 ± 4.89 % for the valence dimension and of 66.67 ± 11.88 % for the arousal dimension, was obtained. The poor correlation emerged between IAPS scores and SAM scores negatively impacts on the accuracy and highlights the issue of IAPS update