From sensing to intervention for mental and behavioral health

Mishra, Varun

doi:10.1145/3341162.3349304

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…There are a variety of smart wearables and smart devices to monitor physical and mental health [58,61,102]. Human-Computer Interaction (HCI) researchers are actively working to improve wellness care for the general public and vulnerable population groups [56,68,88]. Furthermore, with the growth of the Internet of Things (IoT) devices, the smart speaker market volume reached 200 million and there are more than 6.84 billion smartphones worldwide in 2023 [35,85].…”

Section: Introductionmentioning

confidence: 99%

AI Therapist for Daily Functioning Assessment and Intervention Using Smart Home Devices

Nie

Zhao

Xia

et al. 2022

Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

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Fig. 1. CaiTI is a conversational "AI therapist" that screens and assesses the day-to-day functioning of the occupant across 37 dimensions and provides appropriate psychotherapeutic interventions and empathic validations depending on the physical and mental status of the occupant. Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a Conversational AI Therapist with psychotherapeutic Interventions (CaiTI), a platform that leverages large language models (LLM)s and smart devices to enable better mental health self-care. CaiTI can screen the day-to-day functioning using natural and psychotherapeutic conversations. CaiTI leverages reinforcement learning to provide personalized conversation flow. CaiTI can accurately understand and interpret user responses. When the user needs further attention during the conversation, CaiTI can provide conversational psychotherapeutic interventions, including cognitive behavioral therapy (CBT) and motivational interviewing (MI). Leveraging the datasets prepared by the licensed psychotherapists, we experiment and microbenchmark various LLMs' performance in tasks along CaiTI's conversation flow and discuss their strengths and weaknesses. With the psychotherapists, we implement CaiTI and conduct 14-day and 24-week studies. The study results, validated by therapists, demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions

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Section: Introductionmentioning

confidence: 99%

AI Therapist for Daily Functioning Assessment and Intervention Using Smart Home Devices

Nie

Zhao

Xia

et al. 2022

Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

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“…Pervasive solutions for emotion recognition with wearable devices have the advantage of being unobtrusive and more reliable than external observations, thanks to objective measurements with sensors that are not invasive. Still, to be reliable emotion recognition faces several practical challenges that cut across sensing and interventions [18]. Additionally, no universal model that is suitable for diverse users' profiles exist [5].…”

mentioning

confidence: 99%

Towards a Design Space for Emotion Recognition

Motti

2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Recognizing emotions through pervasive technologies has been the objective of several research studies. Still, the inherent complexities of emotion recognition result in applications that support the process rather than fully address it. Existing methods for data collection combine multiple sensors and devices. The analysis and classification of users' and environmental data face challenges to reach high accuracy, responsiveness, and reliability. Because emotion recognition is user-dependent, it still remains as an open challenge to be resolved. This paper provides a unified view about emotion recognition, presenting a multidimensional design space that includes multiple approaches. We discuss the practicalities involved with the implementation of each approach and the deployment of emotion recognition systems. We conclude with a discussion about their advantages, limitations, and open questions. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile devices.

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