Deep Reinforcement Learning in Immersive Virtual Reality Exergame for Agent Movement Guidance

Elor, Aviv; Kurniawan, Sri

doi:10.1109/segah49190.2020.9201901

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…Additional users must be included to assess the efficacy of the "PPO Only" and "PPO + GAIL" models and investigate unlearned exercises. [5]…”

Section: Deep Reinforcement Learning In Immersive Virtual Reality Exe...mentioning

confidence: 99%

VR Computer Role-Playing Game Using Reinforcement Learning

hassan,

Ghareeb,

Doqmak

et al. 2023

Journal of Computing and Communication

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This paper shall discuss the process of making one of the first Computer Role-Playing Virtual Reality Games that take advantage of the underutilized hand-tracking technology provided by the Oculus Quest 2 headset. The game takes place in two separate fictional dystopian worlds. The player will have to navigate and explore to discover its secrets while trying to survive against enemies that employ a reinforcement learning algorithm to stop the player. Through extensive experimentation and fine-tuning of various implementation aspects, including positive and negative rewards, hyper parameter values related to Proximal Policy Optimization (PPO), and the number of environment observations, our agent achieved highly favorable outcomes in terms of general machine learning model efficiency indicators. As explained in the methodology section, the model's behavior aligns with the desired behavior, achieved by implementing three rays at different angles to determine some of the rewards. These results validate the effectiveness and success of our approach in training the agent to exhibit desired behaviors and achieve desirable outcomes.

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“…Additional users must be included to assess the efficacy of the "PPO Only" and "PPO + GAIL" models and investigate unlearned exercises. [5]…”

Section: Deep Reinforcement Learning In Immersive Virtual Reality Exe...mentioning

confidence: 99%

VR Computer Role-Playing Game Using Reinforcement Learning

hassan,

Ghareeb,

Doqmak

et al. 2023

Journal of Computing and Communication

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“…II. SYSTEM DESIGN Our physical therapy system is based on a game called "Project Butterfly" (PBF) that was proposed by Elor et al [49], [50]. Previously, PBF explored the feasibility of a virtual reality enhanced exo-skeleton for post-stroke and elderly assistance through two exercises, but was not designed or tested for upper extremity physical therapy over a extended period of time with varying custom exercise movements as reported in this study.…”

Section: Introductionmentioning

confidence: 99%

Gaming Beyond the Novelty Effect of Immersive Virtual Reality for Physical Rehabilitation

et al. 2022

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Immersive Virtual Reality (iVR) Head-Mounted Display (HMD) systems paired with serious exercise games can positively augment physical rehabilitation process from both engagement and analytics perspectives. This paper presents a serious game for iVR HMD based long term upper-extremity exercise. We demonstrate the capabilities of our game through a case study with five users recovering from upper-extremity injuries. We examine how our program maintains engagement and motivation over eight weeks, where users completed bi-weekly prescribed movements framed as protecting a virtual butterfly. We assess user experiences through a mixture of biomarkers from brainwave, heart rate, and galvanic skin response recorded at runtime as well as motion capture and behavioral game data. Our results suggest that the iVR game was an effective medium in inducing high compliance, physical performance, and biometric changes even with increasing difficulty beyond the novelty effect period. We conclude with considerations of future work for iVR physical therapy games that adapt to biometric response.

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“…These mechanisms can alleviate the difficulty of a task by providing interactions in line with the user's preferences (e.g., exploring, competing, etc.) [17] and have been used to design more engaging interactive learning interactions [14].…”

Section: Introductionmentioning

confidence: 99%