Delong Du, M.Sc.

2024

• D. Du, A. Vavouris, O. Veisi, L. Jin, G. Stevens, L. Stankovic, V. Stankovic, and A. Boden, „Time and Money Matters for Sustainability: Insights on User Preferences on Renewable Energy for Electric Vehicle Charging Stations,“ in Proceedings of Mensch und Computer 2024, New York, NY, USA, 2024, p. 269–278. doi:10.1145/3670653.3670677
  [BibTeX] [Abstract] [Download PDF]
  

  Charging electric vehicles (EVs) with renewable energy can lessen their environmental impact. However, the fluctuating availability of renewable energy affects the sustainability of public EV charging stations. Nearby public charging stations may utilize differing energy sources due to their microgrid connections – ranging from exclusively renewable to non-renewable or a combination of both – highlighting the substantial variability in energy supply types within short distances. This study investigates the near-future scenario of integrating dynamic renewable energy availability in charging station navigation to impact the choices of EV users towards renewable sources. We conducted a within-subjects design survey with 50 car users and semi-structured interviews with 10 EV users from rural, suburban, and urban areas. The results show that when choosing EV charging stations, drivers often prioritize either time savings or money savings based on the driving scenarios that influence drivers’ consumer value. Notably, EV users tend to select renewable-powered stations when they align with their main priority, be it saving money or time. This study offers end-user insights into the front-end graphic user interface and the development of the back-end ranking algorithm for navigation recommender systems that integrate dynamic renewable energy availability for the sustainable use of electric vehicles.

  @inproceedings{du_time_2024,
  address = {New York, NY, USA},
  series = {{MuC} '24},
  title = {Time and {Money} {Matters} for {Sustainability}: {Insights} on {User} {Preferences} on {Renewable} {Energy} for {Electric} {Vehicle} {Charging} {Stations}},
  isbn = {9798400709982},
  shorttitle = {Time and {Money} {Matters} for {Sustainability}},
  url = {https://doi.org/10.1145/3670653.3670677},
  doi = {10.1145/3670653.3670677},
  abstract = {Charging electric vehicles (EVs) with renewable energy can lessen their environmental impact. However, the fluctuating availability of renewable energy affects the sustainability of public EV charging stations. Nearby public charging stations may utilize differing energy sources due to their microgrid connections - ranging from exclusively renewable to non-renewable or a combination of both - highlighting the substantial variability in energy supply types within short distances. This study investigates the near-future scenario of integrating dynamic renewable energy availability in charging station navigation to impact the choices of EV users towards renewable sources. We conducted a within-subjects design survey with 50 car users and semi-structured interviews with 10 EV users from rural, suburban, and urban areas. The results show that when choosing EV charging stations, drivers often prioritize either time savings or money savings based on the driving scenarios that influence drivers’ consumer value. Notably, EV users tend to select renewable-powered stations when they align with their main priority, be it saving money or time. This study offers end-user insights into the front-end graphic user interface and the development of the back-end ranking algorithm for navigation recommender systems that integrate dynamic renewable energy availability for the sustainable use of electric vehicles.},
  urldate = {2024-08-31},
  booktitle = {Proceedings of {Mensch} und {Computer} 2024},
  publisher = {Association for Computing Machinery},
  author = {Du, Delong and Vavouris, Apostolos and Veisi, Omid and Jin, Lu and Stevens, Gunnar and Stankovic, Lina and Stankovic, Vladimir and Boden, Alexander},
  month = sep,
  year = {2024},
  pages = {269--278},
  }

• M. Shajalal, M. Atabuzzaman, A. Boden, G. Stevens, and D. Du, What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on TransformersArxiv, 2024.
  [BibTeX] [Abstract] [Download PDF]
  

  Customers’ reviews and feedback play crucial role on electronic commerce (E-commerce) platforms like Amazon, Zalando, and eBay in influencing other customers’ purchasing decisions. However, there is a prevailing concern that sellers often post fake or spam reviews to deceive potential customers and manipulate their opinions about a product. Over the past decade, there has been considerable interest in using machine learning (ML) and deep learning (DL) models to identify such fraudulent reviews. Unfortunately, the decisions made by complex ML and DL models – which often function as black-boxes – can be surprising and difficult for general users to comprehend. In this paper, we propose an explainable framework for detecting fake reviews with high precision in identifying fraudulent content with explanations and investigate what information matters most for explaining particular decisions by conducting empirical user evaluation. Initially, we develop fake review detection models using DL and transformer models including XLNet and DistilBERT. We then introduce layer-wise relevance propagation (LRP) technique for generating explanations that can map the contributions of words toward the predicted class. The experimental results on two benchmark fake review detection datasets demonstrate that our predictive models achieve state-ofthe-art performance and outperform several existing methods. Furthermore, the empirical user evaluation of the generated explanations concludes which important information needs to be considered in generating explanations in the context of fake review identification.

  @misc{shajalal_what_2024,
  title = {What {Matters} in {Explanations}: {Towards} {Explainable} {Fake} {Review} {Detection} {Focusing} on {Transformers}},
  shorttitle = {What {Matters} in {Explanations}},
  url = {http://arxiv.org/abs/2407.21056},
  abstract = {Customers’ reviews and feedback play crucial role on electronic commerce (E-commerce) platforms like Amazon, Zalando, and eBay in influencing other customers’ purchasing decisions. However, there is a prevailing concern that sellers often post fake or spam reviews to deceive potential customers and manipulate their opinions about a product. Over the past decade, there has been considerable interest in using machine learning (ML) and deep learning (DL) models to identify such fraudulent reviews. Unfortunately, the decisions made by complex ML and DL models - which often function as black-boxes - can be surprising and difficult for general users to comprehend. In this paper, we propose an explainable framework for detecting fake reviews with high precision in identifying fraudulent content with explanations and investigate what information matters most for explaining particular decisions by conducting empirical user evaluation. Initially, we develop fake review detection models using DL and transformer models including XLNet and DistilBERT. We then introduce layer-wise relevance propagation (LRP) technique for generating explanations that can map the contributions of words toward the predicted class. The experimental results on two benchmark fake review detection datasets demonstrate that our predictive models achieve state-ofthe-art performance and outperform several existing methods. Furthermore, the empirical user evaluation of the generated explanations concludes which important information needs to be considered in generating explanations in the context of fake review identification.},
  language = {en},
  urldate = {2024-08-05},
  publisher = {arXiv},
  author = {Shajalal, Md and Atabuzzaman, Md and Boden, Alexander and Stevens, Gunnar and Du, Delong},
  month = jul,
  year = {2024},
  note = {arXiv:2407.21056 [cs]},
  keywords = {Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Social and Information Networks},
  }

• G. Trovato, Y. Du, S. Mitchell, F. P. Trevejo, R. L. Condori, M. Katagiri, R. Obe, M. Gawande, S. Cosentino, M. Manavi, F. Carros, and R. Wieching, „CelesTE, theomorphic device for cognitive support of older adults,“ in 2024 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), 2024, p. 200–205. doi:10.1109/ARSO60199.2024.10557814
  [BibTeX] [Abstract] [Download PDF]
  

  Healthy ageing is a challenge in societies that can be coped with the help of socially assistive robots. This study introduces CelesTE, a theomorphic device designed to support the well-being of older adults. Building upon the foundations laid by SanTO, the Catholic robot, CelesTE takes the form of an angel in prayer and aims to engage users, particularly those of the Christian Catholic faith. The paper delves into CelesTE’s conceptual evolution, addressing challenges related to religious perceptions, fallibility, and user interaction. Quantitative and qualitative feedback was collected from 14 participants across three European countries. Results indicate generally positive acceptance, although limitation were found. Negative responses are considered particularly valuable for CelesTE’s future development.

  @inproceedings{trovato_celeste_2024,
  title = {{CelesTE}, theomorphic device for cognitive support of older adults},
  url = {https://ieeexplore.ieee.org/abstract/document/10557814},
  doi = {10.1109/ARSO60199.2024.10557814},
  abstract = {Healthy ageing is a challenge in societies that can be coped with the help of socially assistive robots. This study introduces CelesTE, a theomorphic device designed to support the well-being of older adults. Building upon the foundations laid by SanTO, the Catholic robot, CelesTE takes the form of an angel in prayer and aims to engage users, particularly those of the Christian Catholic faith. The paper delves into CelesTE’s conceptual evolution, addressing challenges related to religious perceptions, fallibility, and user interaction. Quantitative and qualitative feedback was collected from 14 participants across three European countries. Results indicate generally positive acceptance, although limitation were found. Negative responses are considered particularly valuable for CelesTE’s future development.},
  urldate = {2024-12-11},
  booktitle = {2024 {IEEE} {International} {Conference} on {Advanced} {Robotics} and {Its} {Social} {Impacts} ({ARSO})},
  author = {Trovato, Gabriele and Du, Yegang and Mitchell, Scean and Trevejo, Franco Pariasca and Condori, Rodrigo Lopez and Katagiri, Masao and Obe, Rio and Gawande, Manishk and Cosentino, Sarah and Manavi, Mehrbod and Carros, Felix and Wieching, Rainer},
  month = may,
  year = {2024},
  note = {ISSN: 2162-7576},
  keywords = {Europe, Older adults, Robots, Aging, Buildings, Assistive robots},
  pages = {200--205},
  }

• M. Shajalal, A. Boden, G. Stevens, D. Du, and D. Kern, Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home EnvironmentsArxiv, 2024. doi:10.48550/arXiv.2404.16074
  [BibTeX] [Abstract] [Download PDF]
  

  Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models‘ decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.

  @misc{shajalal_explaining_2024,
  title = {Explaining {AI} {Decisions}: {Towards} {Achieving} {Human}-{Centered} {Explainability} in {Smart} {Home} {Environments}},
  shorttitle = {Explaining {AI} {Decisions}},
  url = {http://arxiv.org/abs/2404.16074},
  doi = {10.48550/arXiv.2404.16074},
  abstract = {Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models' decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.},
  urldate = {2024-05-14},
  publisher = {arXiv},
  author = {Shajalal, Md and Boden, Alexander and Stevens, Gunnar and Du, Delong and Kern, Dean-Robin},
  month = apr,
  year = {2024},
  note = {arXiv:2404.16074 [cs]},
  keywords = {Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence},
  }

• A. A. Tehrani, O. Veisi, B. V. Fakhr, and D. Du, „Predicting solar radiation in the urban area: A data-driven analysis for sustainable city planning using artificial neural networking,“ Sustainable cities and society, vol. 100, p. 105042, 2024. doi:10.1016/j.scs.2023.105042
  [BibTeX] [Abstract] [Download PDF]
  

  Predicting solar radiation in cities using the Artificial Neural Network model (ANN) is a pioneering step in transforming future-oriented city planning using solar energy. This research harnesses vast datasets to forecast the average annual solar radiation, considering minimal urban information across various urban attributes, including coordinates (X, Y, Z), average height, inhabited and non-occupied areas, and the Azimuth angle. Our method employed parametric design and remote sensing to generate this dataset and then used the ANN model to make predictions and simulations. Urban attributes of 20 cities were examined, including Casablanca, Abu Dhabi, Cape Town, Dublin, Havana, Melbourne, Rome, Singapore, Nairobi, Mumbai, New York, Nagoya, Sao Paulo, Tehran, Madrid, Toronto, Antananarivo, Beijing, Lisbon, and Paris. This data-driven approach trains our ANN model to discern complex and nonlinear relationships between independent and dependent variables and thus enables our model to predict solar radiation in urban cities. Our data training results indicate that the output (the minimum solar radiation each year of the cities) can be predicted using the study input variables with a loss of 0.01, a mean squared error of 0.01, and an R2-squared value of 85\%. Such predictions can refine urban designs of buildings, public spaces, and various urban infrastructures to optimize solar energy use, reducing environmental impacts and fossil fuel reliance, thus aiding climate change mitigation and sustainability. Our findings underscore the integral association between solar radiation and sustainable urban evolution, giving urban planners and researchers sustainable strategies for advancing energy efficiency and ecological equilibrium.

  @article{tehrani_predicting_2024,
  title = {Predicting solar radiation in the urban area: {A} data-driven analysis for sustainable city planning using artificial neural networking},
  volume = {100},
  issn = {2210-6707},
  shorttitle = {Predicting solar radiation in the urban area},
  url = {https://www.sciencedirect.com/science/article/pii/S2210670723006534},
  doi = {10.1016/j.scs.2023.105042},
  abstract = {Predicting solar radiation in cities using the Artificial Neural Network model (ANN) is a pioneering step in transforming future-oriented city planning using solar energy. This research harnesses vast datasets to forecast the average annual solar radiation, considering minimal urban information across various urban attributes, including coordinates (X, Y, Z), average height, inhabited and non-occupied areas, and the Azimuth angle. Our method employed parametric design and remote sensing to generate this dataset and then used the ANN model to make predictions and simulations. Urban attributes of 20 cities were examined, including Casablanca, Abu Dhabi, Cape Town, Dublin, Havana, Melbourne, Rome, Singapore, Nairobi, Mumbai, New York, Nagoya, Sao Paulo, Tehran, Madrid, Toronto, Antananarivo, Beijing, Lisbon, and Paris. This data-driven approach trains our ANN model to discern complex and nonlinear relationships between independent and dependent variables and thus enables our model to predict solar radiation in urban cities. Our data training results indicate that the output (the minimum solar radiation each year of the cities) can be predicted using the study input variables with a loss of 0.01, a mean squared error of 0.01, and an R2-squared value of 85\%. Such predictions can refine urban designs of buildings, public spaces, and various urban infrastructures to optimize solar energy use, reducing environmental impacts and fossil fuel reliance, thus aiding climate change mitigation and sustainability. Our findings underscore the integral association between solar radiation and sustainable urban evolution, giving urban planners and researchers sustainable strategies for advancing energy efficiency and ecological equilibrium.},
  urldate = {2024-02-06},
  journal = {Sustainable Cities and Society},
  author = {Tehrani, Alireza Attarhay and Veisi, Omid and Fakhr, Bahereh Vojdani and Du, Delong},
  month = jan,
  year = {2024},
  keywords = {Artificial neural networking, Energy simulations, Remote sensing, Solar radiation, Sustainable cities, Urban texture},
  pages = {105042},
  }

2023

• O. Veisi, D. Du, M. A. Moradi, F. C. Guasselli, S. Athanasoulias, H. A. Syed, C. Müller, and G. Stevens, „Designing SafeMap Based on City Infrastructure and Empirical Approach: Modified A-Star Algorithm for Earthquake Navigation Application,“ in Proceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI, New York, NY, USA, 2023, p. 61–70. doi:10.1145/3615900.3628788
  [BibTeX] [Abstract] [Download PDF]
  

  Designing routing systems for earthquakes requires frontend usability studies and backend algorithm modifications. Evaluations from subject-matter experts can enhance the design of both the front-end interface and the back-end algorithm of urban artificial intelligence (AI). Urban AI applications need to be trustworthy, responsible, and reliable against earthquakes, by assisting civilians to identify safe and fast routes to safe areas or health support stations. However, routes may become dangerous or obstructed as regular routing applications may fail to adapt responsively to city destruction caused by earthquakes. In this study, we modified the A-star algorithm and designed an interactive mobile app with the evaluation and insights of subject-matter experts including 15 UX designers, 7 urbanists, 8 quake survivors, and 4 first responders. Our findings reveal reducing application features and quickening application use time is necessary for stressful earthquake situations, as emerging features such as augmented reality and voice assistant may negatively backlash user experience in earthquake scenarios due to over-immersion, distracting users from real world condition. Additionally, we utilized expert insights to modify the A-star algorithm for earthquake scenarios using the following steps: 1) create a dataset based on the roads; 2) establish an empty dataset for weight; 3) enable the updating of weight based on infrastructure; and 4) allow the alteration of weight based on safety, related to human behavior. Our study provides empirical evidence on why urban AI applications for earthquakes need to adapt to the rapid speed to use and elucidate how and why the A-star algorithm is optimized for earthquake scenarios.

  @inproceedings{veisi_designing_2023,
  address = {New York, NY, USA},
  series = {{UrbanAI} '23},
  title = {Designing {SafeMap} {Based} on {City} {Infrastructure} and {Empirical} {Approach}: {Modified} {A}-{Star} {Algorithm} for {Earthquake} {Navigation} {Application}},
  isbn = {9798400703621},
  shorttitle = {Designing {SafeMap} {Based} on {City} {Infrastructure} and {Empirical} {Approach}},
  url = {https://dl.acm.org/doi/10.1145/3615900.3628788},
  doi = {10.1145/3615900.3628788},
  abstract = {Designing routing systems for earthquakes requires frontend usability studies and backend algorithm modifications. Evaluations from subject-matter experts can enhance the design of both the front-end interface and the back-end algorithm of urban artificial intelligence (AI). Urban AI applications need to be trustworthy, responsible, and reliable against earthquakes, by assisting civilians to identify safe and fast routes to safe areas or health support stations. However, routes may become dangerous or obstructed as regular routing applications may fail to adapt responsively to city destruction caused by earthquakes. In this study, we modified the A-star algorithm and designed an interactive mobile app with the evaluation and insights of subject-matter experts including 15 UX designers, 7 urbanists, 8 quake survivors, and 4 first responders. Our findings reveal reducing application features and quickening application use time is necessary for stressful earthquake situations, as emerging features such as augmented reality and voice assistant may negatively backlash user experience in earthquake scenarios due to over-immersion, distracting users from real world condition. Additionally, we utilized expert insights to modify the A-star algorithm for earthquake scenarios using the following steps: 1) create a dataset based on the roads; 2) establish an empty dataset for weight; 3) enable the updating of weight based on infrastructure; and 4) allow the alteration of weight based on safety, related to human behavior. Our study provides empirical evidence on why urban AI applications for earthquakes need to adapt to the rapid speed to use and elucidate how and why the A-star algorithm is optimized for earthquake scenarios.},
  urldate = {2024-02-05},
  booktitle = {Proceedings of the 1st {ACM} {SIGSPATIAL} {International} {Workshop} on {Advances} in {Urban}-{AI}},
  publisher = {Association for Computing Machinery},
  author = {Veisi, Omid and Du, Delong and Moradi, Mohammad Amin and Guasselli, Fernanda Caroline and Athanasoulias, Sotiris and Syed, Hussain Abid and Müller, Claudia and Stevens, Gunnar},
  month = nov,
  year = {2023},
  keywords = {A-star algorithm, city infrastructure, earthquake, navigation, routing, user experience},
  pages = {61--70},
  }

• D. Du, S. G. Amirhajlou, A. Gyabaah, R. Paluch, and C. Müller, „Mediating Personal Relationships with Robotic Pets for Fostering Human-Human Interaction of Older Adults,“ , 2023. doi:10.48340/IHC2023_P003
  [BibTeX] [Abstract] [Download PDF]
  

  Good human relationships are important for us to have a happy life and maintain our well-being. Otherwise, we will be at risk of experiencing loneliness or depression. In human-computer interaction (HCI) and computer-supported cooperative work (CSCW), robotic systems offer nuanced approaches to foster human connection, providing interaction beyond the traditional mediums that smartphones and computers offer. However, many existing studies primarily focus on the human-robot relationships that older adults form directly with robotic pets rather than exploring how these robotic pets can enhance …

  @article{du_mediating_2023,
  title = {Mediating {Personal} {Relationships} with {Robotic} {Pets} for {Fostering} {Human}-{Human} {Interaction} of {Older} {Adults}},
  issn = {2510-2591},
  url = {https://dl.eusset.eu/handle/20.500.12015/5016},
  doi = {10.48340/IHC2023_P003},
  abstract = {Good human relationships are important for us to have a happy life and maintain our well-being. Otherwise, we will be at risk of experiencing loneliness or depression. In human-computer interaction (HCI) and computer-supported cooperative work (CSCW), robotic systems offer nuanced approaches to foster human connection, providing interaction beyond the traditional mediums that smartphones and computers offer. However, many existing studies primarily focus on the human-robot relationships that older adults form directly with robotic pets rather than exploring how these robotic pets can enhance ...},
  language = {en},
  urldate = {2023-10-03},
  author = {Du, Delong and Amirhajlou, Sara Gilda and Gyabaah, Akwasi and Paluch, Richard and Müller, Claudia},
  year = {2023},
  }

2022

• J. Duval, R. Thakkar, D. Du, K. Chin, S. Luo, A. Elor, M. S. El-Nasr, and M. John, „Designing Spellcasters from Clinician Perspectives: A Customizable Gesture-Based Immersive Virtual Reality Game for Stroke Rehabilitation,“ Acm transactions on accessible computing, vol. 15, iss. 3, p. 26:1–26:25, 2022. doi:10.1145/3530820
  [BibTeX] [Abstract] [Download PDF]
  

  Developing games is time-consuming and costly. Overly clinical therapy games run the risk of being boring, which defeats the purpose of using games to motivate healing in the first place [10, 23]. In this work, we adapt and repurpose an existing immersive virtual reality (iVR) game, Spellcasters, originally designed purely for entertainment for use as a stroke rehabilitation game—which is particularly relevant in the wake of COVID-19, where telehealth solutions are increasingly needed [4]. In preparation for participatory design sessions with stroke survivors, we collaborate with 14 medical professionals to ensure Spellcasters is safe and therapeutically valid for clinical adoption. We present our novel VR sandbox implementation that allows medical professionals to customize appropriate gestures and interactions for each patient’s unique needs. Additionally, we share a co-designed companion app prototype based on clinicians’ preferred data reporting mechanisms for telehealth. We discuss insights about adapting and repurposing entertainment games as serious games for health, features that clinicians value, and the potential broader impacts of applications like Spellcasters for stroke management.

  @article{duval_designing_2022,
  title = {Designing {Spellcasters} from {Clinician} {Perspectives}: {A} {Customizable} {Gesture}-{Based} {Immersive} {Virtual} {Reality} {Game} for {Stroke} {Rehabilitation}},
  volume = {15},
  issn = {1936-7228},
  shorttitle = {Designing {Spellcasters} from {Clinician} {Perspectives}},
  url = {https://dl.acm.org/doi/10.1145/3530820},
  doi = {10.1145/3530820},
  abstract = {Developing games is time-consuming and costly. Overly clinical therapy games run the risk of being boring, which defeats the purpose of using games to motivate healing in the first place [10, 23]. In this work, we adapt and repurpose an existing immersive virtual reality (iVR) game, Spellcasters, originally designed purely for entertainment for use as a stroke rehabilitation game—which is particularly relevant in the wake of COVID-19, where telehealth solutions are increasingly needed [4]. In preparation for participatory design sessions with stroke survivors, we collaborate with 14 medical professionals to ensure Spellcasters is safe and therapeutically valid for clinical adoption. We present our novel VR sandbox implementation that allows medical professionals to customize appropriate gestures and interactions for each patient’s unique needs. Additionally, we share a co-designed companion app prototype based on clinicians’ preferred data reporting mechanisms for telehealth. We discuss insights about adapting and repurposing entertainment games as serious games for health, features that clinicians value, and the potential broader impacts of applications like Spellcasters for stroke management.},
  number = {3},
  urldate = {2023-08-25},
  journal = {ACM Transactions on Accessible Computing},
  author = {Duval, Jared and Thakkar, Rutul and Du, Delong and Chin, Kassandra and Luo, Sherry and Elor, Aviv and El-Nasr, Magy Seif and John, Michael},
  month = aug,
  year = {2022},
  keywords = {digital therapeutics, game design, games for health, immersive virtual reality, serious games, Stroke rehabilitation, therapy},
  pages = {26:1--26:25},
  }