Emotion-enabled haptic-based serious game for post stroke rehabilitation

Hou, Xiyuan; Sourina, Olga

doi:10.1145/2503713.2503738

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…Numerous on and off-body sensors often become part of serious games. Hou and his colleagues claimed that an EEG-enabled haptic-based serious game might aid in the rehabilitation of stroke patients with motor deficiencies [65]. The findings of a study [66] that combined EMG and depth data revealed that the virtual reality (VR) environment presented here can improve muscle activation in elbow flexion-extension motions.…”

Section: Serious Games Targeting Stroke Rehabilitationmentioning

confidence: 82%

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

et al. 2022

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This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers’ knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with stroke survivors. The methodologies utilized professional medical experiences and real-life evidence data gathered during the duration of the iTrain project to create the scenarios for the game’s deep reinforcement caregiver behavior improvement model, as well as the design of game mechanics, game images and game characters, and gameplay implementation. Furthermore, the results of the game’s direct impact on caregivers (n = 25) and stroke survivors (n = 21) in Lithuania using the Geriatric Depression Scale (GDS) and user experience questionnaire (UEQ) are presented. Both surveys had favorable outcomes, showing the effectiveness of the approach. The GDS scale (score 10) revealed a low number of 28% of individuals depressed, and the UEQ received a very favorable grade of +0.8.

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Section: Serious Games Targeting Stroke Rehabilitationmentioning

confidence: 82%

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

et al. 2022

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“…In this case study, we explore the application of EEG recognition for detecting happiness in the context of virtual reality (VR) gaming [8]. The goal is to develop a system that adapts gameplay dynamically based on the player's emotional state, enhancing the gaming experience and promoting positive affect, Figure 3.…”

Section: Case Study: Eeg Recognition Of Happiness In Virtual Reality ...mentioning

confidence: 99%

EEG-based recognition of happiness

Leong,

Leong

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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The ability to recognize emotional states, such as happiness, from electroencephalography (EEG) signals has gained significant attention in recent years due to its potential applications in affective computing, mental health monitoring, and human-computer interaction. This paper presents a comprehensive overview of EEG-based recognition of happiness, including signal acquisition, preprocessing techniques, feature extraction methods, classification algorithms, challenges, and real-world applications. Additionally, it discusses the future directions and potential impact of EEG-based happiness recognition.

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“…These devices, specifically EEG-based devices, have also proved to be useful for making adaptive games [21], which is particularly our field of study. EEG biosensors, are suitable to be used for cognitive games [22]; in multiplayer cooperative games [23], for evaluating how the cognitive activity changes while playing with teammates; also in serious games, in order to promote rehabilitation with patients that suffer from motor deficits due to a stroke, these researchers developed a new game that aims to help them with the process of rehabilitation [24].…”

Section: Related Workmentioning

confidence: 99%

Difficulty Adjustment Using Player's Performance and Electroencephalographic Data

Fernandez

Mikami

Kondo

2019

The Journal of the Society for Art and Science

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For high skilled players, an easy game might become boring and for low skilled players, a difficult game might become frustrating. The purpose of this research was to create new and better ways to offer players with different skills, an appropriate experience. We focused on adapting the difficulty levels of a simple 2D platform game, designing and building levels automatically. The proposed method consists of Dynamic Difficulty Adjustment (DDA) and Rhythm-Group Theory (a procedural content generation method), combined with levels of attention obtained from Electroencephalographic (EEG) data. Experiments were designed in the way that players had to clear five different levels that were created automatically using the player's performance and EEG data obtained from a biosensor while playing. Results showed that the method successfully adapts the level difficulty according to the player's status. In addition, the designed method calculates difficulty using values computed in real time to decide the shape and structure of the levels. The method designed in this research can be implemented not only in platformers but also in other genres that involve elements of rhythm, additionally, it could be used by game developers as a tool for playtesting in order to improve the game design, receive quantitative and numerical feedback from players and create an overall better experience for their players.

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Emotion-enabled haptic-based serious game for post stroke rehabilitation

Cited by 18 publications

References 11 publications

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

EEG-based recognition of happiness

Difficulty Adjustment Using Player's Performance and Electroencephalographic Data

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