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Rehabilitation plays a crucial role in cancer care, as the functioning of cancer survivors is frequently compromised by impairments that can result from the disease itself but also from the long-term sequelae of the treatment. Nevertheless, the current literature shows that only a minority of patients receive physical and/or cognitive rehabilitation. This lack of rehabilitative care is a consequence of many factors, one of which includes the transportation issues linked to disability that limit the patient’s access to rehabilitation facilities. The recent COVID-19 pandemic has further shown the benefits of improving telemedicine and home-based rehabilitative interventions to facilitate the delivery of rehabilitation programs when attendance at healthcare facilities is an obstacle. In recent years, researchers have been investigating the benefits of the application of virtual reality to rehabilitation. Virtual reality is shown to improve adherence and training intensity through gamification, allow the replication of real-life scenarios, and stimulate patients in a multimodal manner. In our present work, we offer an overview of the present literature on virtual reality-implemented cancer rehabilitation. The existence of wide margins for technological development allows us to expect further improvements, but more randomized controlled trials are needed to confirm the hypothesis that VRR may improve adherence rates and facilitate telerehabilitation.

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Multi-agent path planning on grid maps is a challenging problem and has numerous real-life applications ranging from robotics to real-time strategy games and non-player characters in video games. A* is a cost-optimal forward search algorithm for path planning which scales up poorly in practice since both the search space and the branching factor grow exponentially in the number of agents. In this work, we propose an A* implementation for the Graphics Processor Units (GPUs) which uses as search space a grid map. The approach uses a search space decomposition to break down the forward search A* algorithm into parallel independently forward sub-searches. The solution offer no guarantees with respect to completeness and solution quality but exploits the computational capability of GPUs to accelerate path planning for many thousands of agents. The paper describes this implementation using the Compute Unified Device Architecture (CUDA) programming environment, and demonstrates its advantages in GPU performance compared to GPU implementation of Real-Time Adaptive A*

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