Making Video Games More Inclusive for People Living With Motor Neuron Disease: Scoping Review

BACKGROUND

Evidence suggests that individuals with motor neuron disease (MND), a terminal illness, find enjoyment and social connection through video games. However, MND-related barriers can make gaming challenging, exacerbating feelings of boredom, stress, isolation, and loss of control over daily life.

OBJECTIVE

We scoped the evidence to describe relevant research and practice regarding what may help reduce difficulties for people with MND when playing video games.

METHODS

A scoping review was conducted using the Arksey and O'Malley framework, recent scoping review guidance, and engaging with people with lived experience of MND. Peer-reviewed studies were sourced from PubMed and the Swinburne University of Technology Library. Gray literature was identified from government, not-for-profit, commercial, and community websites. Data were extracted and summarized from the collected literature.

RESULTS

The evidence base, consisting of quantitative and qualitative research, lived experience stories, information resources, reviews, and guidelines, included 85 documents. Only 8 (9%) directly addressed video games and people with MND; however, these studies were limited in depth and quality. The primary technologies examined included customized information and communications technology for communication and control of computing systems (including desktop, laptop, smartphone, tablet, and console systems) and video game software and hardware (including hand controllers and accessibility features, such as difficulty level, speed, and remappable buttons and controls). Common factors in the evidence base highlight barriers and enablers to enjoying video games for people with MND. These include technological, physical, social, and economic challenges. Addressing these requires greater involvement of people with MND in game and technology research and development. Changes to information and communications technology, game software and hardware, policies, and guidelines are needed to better meet their needs.

CONCLUSIONS

There is a significant gap in understanding the lived experience of people with MND with video games and what makes playing them easier, including appropriate customization of technology and the social experience of games. This gap perpetuates exclusion from gaming communities and recreation, potentially worsening social isolation. Existing evidence suggesting viable options for future research and practice. Video game and information and communications technology research and development must prioritize qualitative and quantitative research with people with MND at an appropriate scale, with a focus on lived experience, use of improved participant engagement techniques, and user-focused design for more inclusive games. Practical work needs to increase awareness of what can help make games more inclusive, including incorporation of accessibility early in the game production process, early incorporation of accessibility in game production, and affordable options for customized interfaces and other devices to play games.

Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control

To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb's structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.

Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control

The touchless techniques in human-computer interaction (HCI) can effectively expand computer access capabilities for disabled people. This paper presents Touchless Head-Control (THC), an assistive system method for computer cursor control based on head pose captured with an RGB camera. Our work aimed to replace the standard cursor control using a device on the user's head. The convolutional neural networks with predicted fine-grained feature maps and binned classification were applied to estimate the head pose angles. The mouse pointer or cursor is moved to actual locations on the screen based on head movement (yaw and pitch) and the center position of the face. Head tilt to the right or left (roll) to control the mouse button. In addition, the proposed method can be used to simulate the movement of the robot or joystick using the head to control objects within three degrees of freedom (DOF). Various participants were involved in the interaction design evaluation, in which target selection accuracy, travel time, and path efficiency were measured. This technology allows people with limited motor skills to easily control a PC cursor and 3D object orientation without the use of additional equipment or sensors.

RoBERTaEns: Deep Bidirectional Encoder Ensemble Model for Fact Verification

The application of the bidirectional encoder model to detect fake news has been widely applied because of its ability to provide factual verification with good results. Good fact verification requires the most optimal model and has the best evaluation to make news readers trust the reliable and accurate verification results. In this study, we evaluated the application of a homogeneous ensemble (HE) on RoBERTa to improve the accuracy of a model. We improve the HE method using a bagging ensemble from three types of RoBERTa models. Then, each prediction is combined to build a new model called RoBERTaEns. The FEVER dataset is used to train and test our model. The experimental results showed that the proposed method, RoBERTaEns, obtained a higher accuracy value with an F1-Score of 84.2% compared to the other RoBERTa models. In addition, RoBERTaEns has a smaller margin of error compared to the other models. Thus, it proves that the application of the HE functions increases the accuracy of a model and produces better values in handling various types of fact input in each fold.