Using Facially Expressive Robots to Calibrate Clinical Pain Perception

Moosaei, Maryam; Das, Sumit Kumar; Popa, Dan O.; Riek, Laurel D.

doi:10.1145/2909824.3020216

Cited by 24 publications

(21 citation statements)

References 63 publications

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“…Given the centrality of cognition to independence and quality of life in aging populations in particular, we aim to synthesize the available research pertaining to cognitive robotic interventions in older adults. Additionally, we emphasize robots specifically tailored to promote autonomy in the home, as this is a key objective of older adults themselves, 14,15 as well as secondary and tertiary stakeholders such as caregivers, physicians, and policymakers. 3,16 Consequently, we focus our conceptual review on older adults without dementia (ie, those who are cognitively healthy and/or those who have mild cognitive impairment [MCI]).…”

Section: Introductionmentioning

confidence: 99%

<p>Home-Based Cognitively Assistive Robots: Maximizing Cognitive Functioning and Maintaining Independence in Older Adults Without Dementia</p>

Patten¹,

Keller²,

Maye³

et al. 2020

CIA

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Promoting health and prolonging independence in the home is a priority for older adults, caregivers, clinicians, and society at large. Rapidly developing robotics technology provides a platform for interventions, with the fields of physically and socially assistive robots expanding in recent years. However, less attention has been paid to using robots to enhance the cognitive health of older adults. The goal of this review is to synthesize the current literature on home-based cognitively assistive robots (CAR) in older adults without dementia and to provide suggestions to improve the quality of the scientific evidence in this subfield. First, we set the stage for CAR by: a) introducing the field of robotics to improve health, b) summarizing evidence emphasizing the importance of home-based interventions for older adults, c) reviewing literature on robot acceptability in older adults, d) highlighting important ethical issues in healthcare robotics, and e) reviewing current findings on socially assistive robots, with a focus on translating findings to the CAR context. With this foundation in place, we then review the literature on CAR, identifying gaps and limitations of current evidence, and proposing future directions for research. We conclude that CAR is promising and feasible and that there is a need for more methodologically rigorous evaluations of CAR to promote prolonged home-based independence in older adults.

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Section: Introductionmentioning

confidence: 99%

<p>Home-Based Cognitively Assistive Robots: Maximizing Cognitive Functioning and Maintaining Independence in Older Adults Without Dementia</p>

Patten¹,

Keller²,

Maye³

et al. 2020

CIA

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“…Communicative motion will be explicitly incorporated into the design in the future. In addition, it will also be beneficial to explore the effect of facial expression [101] and spoken language [31] during interactions. On the other hand, with the development of dedicated short range communication (DSRC) technology [60], vehicles are able to perform vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication.…”

Section: • To Account For Diverse Modes Of Interactionsmentioning

confidence: 99%

Designing Robot Behavior in Human-Robot Interactions

Liu¹,

Tian²,

Lin³

et al. 2019

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Designing Robot Behavior in Human-Robot InteractionsHuman-robot interactions (HRI) have been recognized to be a key element of future robots in many application domains such as manufacturing, transportation, service and entertainment. These applications entail huge social and economical impacts. Future robots are envisioned to function as human's counterparts, which are independent entities that make decisions for themselves; intelligent actuators that interact with the physical world; and involved observers that have rich senses and critical judgements. Most importantly, they are entitled social attributions to build relationships with humans. We call these robots corobots.Technically, it is challenging to design the behavior of co-robots. Unlike traditional robots that work in structured and deterministic environments, co-robots need to operate in highly unstructured and stochastic environments. The fundamental research question to address in this dissertation is how to ensure that co-robots operate efficiently and safely in dynamic uncertain environments.The focus of this dissertation is 1) to set up a unified analytical framework for various human-robot systems; 2) to establish a methodology to design the robot behavior to address the fundamental problem.A multi-agent framework to model human-robot systems is introduced in Chapter 2. In order to address the uncertainties during human-robot interactions, a unique parallel planning and control architecture is introduced in Chapter 2, which has a cognition module for human behavior estimation and human motion prediction, a long term global planner to ensure efficiency of robot behavior, and a short term local planner to ensure real time safety under uncertainties. The functionalities of these components are discussed in Chapter 3 to Chapter 5. Chapter 3 discusses the cognition module, which includes offline classification and online adaptation of various human behaviors. Chapter 4 and Chapter 5 discuss the optimal control or optimization problems for short term and long term robot motion planning. In a cluttered environment, the optimization problems are highly nonlinear and non-convex, hence hard to solve in real time, which may delay the robot's response in emergency situations. Fast online algorithms are developed to handle the issue: the convex feasible setThe past five years has been an incredible journey in my life. I would not have come this far without the tremendous help from many people.My deep and sincere gratitude goes to my Ph.D. advisor Professor Masayoshi Tomizuka, who has been a great mentor to me during the past five years for his profound knowledge, insightful visions, enthusiasm on work, and humor in life. Moreover, Professor Tomizuka respects all my research ideas, inspires me to explore the unknowns, and offers the strongest support in my career development. I sincerely wish I could be a great person, an extraordinary mentor as he is in the future.

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“…PMEs are the third leading cause of death in the United States, 70% of which occur as a result of team failures [11,17,33]. These errors are a direct result of poor communication, poor collaboration, and mistakes made during patient care, which causes patient harm, and in some cases, patient death [33,57]. Such errors are exacerbated by the hierarchical nature of clinical teams.…”

Section: How Clinical Teams Impact Patient Safetymentioning

confidence: 99%

Coordinating Clinical Teams

Taylor

Lee

Kubota

et al. 2019

Proc. ACM Hum.-Comput. Interact.

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Patient safety errors account for over 400,000 preventable deaths annually in US in hospitals alone, 70% of which are caused by team communication breakdowns, stemming from hierarchical structures and asymmetrical power dynamics between physicians, nurses, patients, and others. Nurses are uniquely positioned to identify and prevent these errors, but they are often penalized for speaking up, particularly when physicians are responsible. Nevertheless, empowering nurses and building strong interdisciplinary teams can lead to improved patient safety and outcomes. Thus, our group has been developing a series of intelligent systems that support teaming in safety critical settings, Robot-Centric Team Support System (RoboTSS), and recently developed a group detection and tracking system for collaborative robots. In this paper, we explore how RoboTSS can be used to empower nurses in interprofessional team settings, through a three month long, collaborative design process with nurses across five US-based hospitals. The main findings and contributions of this paper are as follows. First, we found that participants envisioned using a robotic crash cart to guide resuscitation procedures to improve efficiency and reduce errors. Second, nurses discussed how RoboTSS can generate choreography for efficient spatial reconfigurations in co-located clinical teams, which is particularly important in time-sensitive situations such as resuscitation. Third, we found that nurses want to use RoboTSS to "stop the line", and disrupt power dynamics by policing unsafe physician behavior, such as avoiding safety protocols using a robotic crash cart. Fourth, nurses envisioned using our system to support real-time error identification, such as breaking the sterile field, and then communicating those errors to physicians, to relieve them of responsibility. Finally, based on our findings, we propose robot design implications that capture how nurses envision utilizing RoboTSS. We hope this work promotes further exploration in how to design technology to challenge authority in asymmetrical power relationships, particularly in healthcare, as strong teams save lives.

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Using Facially Expressive Robots to Calibrate Clinical Pain Perception

Cited by 24 publications

References 63 publications

<p>Home-Based Cognitively Assistive Robots: Maximizing Cognitive Functioning and Maintaining Independence in Older Adults Without Dementia</p>

<p>Home-Based Cognitively Assistive Robots: Maximizing Cognitive Functioning and Maintaining Independence in Older Adults Without Dementia</p>

Designing Robot Behavior in Human-Robot Interactions

Coordinating Clinical Teams

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