Preston Rhian C, Dinsdale Kenna, Shippy Madison R, Fitter Naomi T
CoRIS Institute, Oregon State University, P.O. Box 1212, Corvallis 97331, OR, USA.
Int J Soc Robot. 2024 May;16(5):899-918. doi: 10.1007/s12369-023-01086-x. Epub 2023 Dec 26.
Prolonged sedentary behavior in the vast population of office and remote workers leads to increased cardiovascular and musculoskeletal health challenges, and existing solutions for encouraging breaks are either costly health coaches or notification systems that are easily ignored. A socially assistive robot (SAR) for promoting healthy workplace practices could provide the physical presence of a health coach along with the scalability of a notification system. To investigate the impact of such a system, we implemented a SAR as an alternative break-taking support solution and examined its impact on individual users' break-taking habits over relatively long-term deployments. We conducted an initial two-month-long study ( = 7) to begin to understand the robot's influence beyond the point of novelty, and we followed up with a week-long data collection ( = 14) to augment the dataset size. The resulting data was used to inform a robot behavior model and formulate possible methods of personalizing robot behaviors. We found that uninterrupted sitting time tended to decrease with our SAR intervention. During model formulation, we found participant responsiveness to the break-taking prompts could be classified into three archetypes and that archetype-specific adjustments to the general model led to improved system success. These results indicate that break-taking prompts are not a one-size-fits-all problem, and that even a small dataset can support model personalization for improving the success of assistive robotic systems.
大量办公室职员和远程工作者长时间久坐的行为导致心血管和肌肉骨骼健康面临更多挑战,而现有的鼓励休息的解决方案要么是成本高昂的健康教练,要么是容易被忽视的通知系统。用于促进健康工作场所行为的社交辅助机器人(SAR)可以兼具健康教练的实际在场和通知系统的可扩展性。为了研究这样一个系统的影响,我们将一个社交辅助机器人作为一种替代性的休息支持解决方案进行了实施,并在相对长期的部署过程中考察了其对个体用户休息习惯的影响。我们开展了一项为期两个月的初步研究(n = 7),以开始了解机器人在新鲜感过后的影响,随后又进行了为期一周的数据收集(n = 14),以扩大数据集规模。所得数据被用于为机器人行为模型提供信息,并制定个性化机器人行为的可能方法。我们发现,在我们的社交辅助机器人干预下,连续久坐时间往往会减少。在模型制定过程中,我们发现参与者对休息提示的反应可以分为三种原型,并且对通用模型进行特定于原型的调整会提高系统的成功率。这些结果表明,休息提示并非一个一刀切的问题,而且即使是一个小数据集也可以支持模型个性化,以提高辅助机器人系统的成功率。