GreenLife: A Persuasive Social Robot to Enhance the Sustainable Behavior in shared Living Spaces

Beheshtian, Nasim; Moradi, Sina; Ahtinen, Aino; Väänänen-Vainio-Mattila, Kaisa; Kähkönen, Kalle; Laine, Markus

doi:10.1145/3419249.3420143

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Still, in this field, Alan et al [32] presented an interactive IoT system designed to help manage energy costs, offering flexible autonomy and detailed information about its operation. Finally, Bang et al [33] proposed a gamified activity in the context of domestic sustainability, while Beheshtian et al [34] developed a social robot for sustainable living in a block of flats. These examples from Sustainable HCI can inform the design of new TAP solutions to help people have more sustainable home environments (e.g., designing applets to reduce energy consumption).…”

Section: Home Automation Environmentsmentioning

confidence: 99%

Designing Home Automation Routines Using an LLM-Based Chatbot

Giudici,

Padalino,

Paolino

et al. 2024

Designs

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Without any more delay, individuals are urged to adopt more sustainable behaviors to fight climate change. New digital systems mixed with engaging and gamification mechanisms could play an important role in achieving such an objective. In particular, Conversational Agents, like Smart Home Assistants, are a promising tool that encourage sustainable behaviors within household settings. In recent years, large language models (LLMs) have shown great potential in enhancing the capabilities of such assistants, making them more effective in interacting with users. We present the design and implementation of GreenIFTTT, an application empowered by GPT4 to create and control home automation routines. The agent helps users understand which energy consumption optimization routines could be created and applied to make their home appliances more environmentally sustainable. We performed an exploratory study (Italy, December 2023) with N = 13 participants to test our application’s usability and UX. The results suggest that GreenIFTTT is a usable, engaging, easy, and supportive tool, providing insight into new perspectives and usage of LLMs to create more environmentally sustainable home automation.

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Section: Home Automation Environmentsmentioning

confidence: 99%

Designing Home Automation Routines Using an LLM-Based Chatbot

Giudici,

Padalino,

Paolino

et al. 2024

Designs

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“…Sustainable living is an all-encompassing term that describes an ecologically, economically, and socially sound way of life. Behesthian et al [48] aimed to promote environmental and social sustainability in a housing community. To do so, they conducted a three-phase study to evaluate the potential for social robots and other persuasive technologies to incentivize sustainable behavior in shared living spaces.…”

Section: Sustainable Living Spacesmentioning

confidence: 99%

“…By comparing one floor's progress to another, the robot is creating a sense of competition between floors, which can be a motivating factor for pro-environmental behavior change [23,61]. Other HRI work has found that robots can be effective social agents for creating competition [48], and when competition with a robot has been invoked, people work harder [62]. Moreover, the robot could use descriptive norms to notify employees of what other companies are achieving in the sustainability realm, encouraging the employees to meet or surpass the goals of other corporations.…”

Section: Better Sensing No Manipulationmentioning

confidence: 99%

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EnviRobots: How Human–Robot Interaction Can Facilitate Sustainable Behavior

2021

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Environmental psychology aims to study human behavior with regard to the environment and how psychological techniques can be used to motivate behavior change. We argue that these concepts can be applied to interactive robots designed for other tasks, which then enables them to encourage sustainability behaviors in humans. We first present a literature review on the current state of social robots that are used to encourage sustainable behaviors. We next present eight hypothetical scenarios which are informed by the progress that has already been made in social robots in sustainability, as well as notable gaps where further environmental psychological concepts could be utilized. These scenarios encompass possible robots that range from limited sensing and no manipulation capabilities, to more sophisticated sensing and no manipulation, to sophisticated sensing and manipulation capabilities. We present these scenarios in which human–robot interaction could potentially result in pro-environmental behavioral changes in humans as recommendations for robot designers interested in helping design social robots for sustainability.

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“…As a result, persuasive technologies prioritize usercentered design, and they can assist users in leading a healthy lifestyle. Considering this, research has demonstrated the valuable roles these technologies play in preventing and aiding the management of illnesses (Schnall et al, 2015;Karppinen et al, 2016;Sonntag, 2016;Bartlett et al, 2017;Faddoul and Chatterjee, 2019;Fukuoka et al, 2019;Kim M. T. et al, 2019;Oyibo and Morita, 2021), promoting fitness and exercise (Bartlett et al, 2017;Schooley et al, 2021), and other significant ones (Jafarinaimi et al, 2005;Anagnostopoulou et al, 2019;Beheshtian et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Ataguba,

Orji

2024

Front. Big Data

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Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

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GreenLife: A Persuasive Social Robot to Enhance the Sustainable Behavior in shared Living Spaces

Cited by 8 publications

References 23 publications

Designing Home Automation Routines Using an LLM-Based Chatbot

Designing Home Automation Routines Using an LLM-Based Chatbot

EnviRobots: How Human–Robot Interaction Can Facilitate Sustainable Behavior

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

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