Comparison of In-home Robotic Companion Pet Use in South Korea and the United States: A Case Study

Bennett, Casey C.; Stanojević, Čedomir; Kim, Seong-Cheol; Lee, Jin-Jae; Yu, Janghoon; Oh, Jiyeong; Šabanović, Selma; Piatt, Jennifer

doi:10.1109/biorob52689.2022.9925468

Cited by 11 publications

(2 citation statements)

References 31 publications

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“…As alluded to in the Background section above, our broader aim here is to understand how different approaches to the in-the-wild real-time assessment of mental health disorders can be best realized via different data sources and multiple technologies. Understanding the subtle differences in different types of smartphone data for that purpose is a necessary first step towards integrating smartphones with similar real-time monitoring data of patient daily activities from technologies such as smart home IOT devices and in-home robotics [29][30][31] .…”

Section: Introductionmentioning

confidence: 99%

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory

et al. 2022

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Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same “information” about bipolar symptomology using different data sources, in a variety of settings.

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Section: Introductionmentioning

confidence: 99%

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory

et al. 2022

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“…This joint IoT data collection approach can also help to better understand culture-specific behaviors performed by patients and identify interactions that patients have with SARs that produce positive health outcomes. Perceptions and use patterns of SARs, additionally, are known to vary significantly across cultures (25)(26)(27) and, as a result, lead to differences in how people utilize SARs to help them address health-related issues. Exploring culturally variable modes of use that bring out the beneficial effects of SARs in naturalistic home environments, in the long run, can help develop more culturally-sensitive designs and implementation of SARs as a form of ecological momentary intervention (EMI).…”

Section: Socially Assistive Robots As Active Ingredients In Dtxmentioning

confidence: 99%

Conceptualizing socially-assistive robots as a digital therapeutic tool in healthcare

Stanojević

Bennett

Šabanović

et al. 2023

Front. Digit. Health

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Artificial Intelligence (AI)-driven Digital Health (DH) systems are poised to play a critical role in the future of healthcare. In 2021, $57.2 billion was invested in DH systems around the world, recognizing the promise this concept holds for aiding in delivery and care management. DH systems traditionally include a blend of various technologies, AI, and physiological biomarkers and have shown a potential to provide support for individuals with various health conditions. Digital therapeutics (DTx) is a more specific set of technology-enabled interventions within the broader DH sphere intended to produce a measurable therapeutic effect. DTx tools can empower both patients and healthcare providers, informing the course of treatment through data-driven interventions while collecting data in real-time and potentially reducing the number of patient office visits needed. In particular, socially assistive robots (SARs), as a DTx tool, can be a beneficial asset to DH systems since data gathered from sensors onboard the robot can help identify in-home behaviors, activity patterns, and health status of patients remotely. Furthermore, linking the robotic sensor data to other DH system components, and enabling SAR to function as part of an Internet of Things (IoT) ecosystem, can create a broader picture of patient health outcomes. The main challenge with DTx, and DH systems in general, is that the sheer volume and limited oversight of different DH systems and DTxs is hindering validation efforts (from technical, clinical, system, and privacy standpoints) and consequently slowing widespread adoption of these treatment tools.

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Purposeful Failures as a Form of Culturally-Appropriate Intelligent Disobedience During Human-Robot Social Interaction

Bennett

Weiß

2022

Lecture Notes in Computer Science

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Comparison of In-home Robotic Companion Pet Use in South Korea and the United States: A Case Study

Cited by 11 publications

References 31 publications

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory

Conceptualizing socially-assistive robots as a digital therapeutic tool in healthcare

Purposeful Failures as a Form of Culturally-Appropriate Intelligent Disobedience During Human-Robot Social Interaction

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