SPIDERS<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e682" altimg="si7.svg"><mml:mo>+</mml:mo></mml:math>: A light-weight, wireless, and low-cost glasses-based wearable platform for emotion sensing and bio-signal acquisition

Nie, Jingping; Liu, Yanchen; Hu, Y. Charlie; Wang, Yuanyuting; Xia, Stephen; Preindl, Matthias; Jiang, Xiaofan

doi:10.1016/j.pmcj.2021.101424

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…The primary forms of wearing these devices include spectacle frames, contact lenses, patches, helmets, and headphones. These devices are specifically engineered to offer a range of functionalities and comforts, encompassing vision correction, hearing enhancement, protection, and even entertainment. − Eye-wearable devices primarily exist in two forms: eyeglass frames and contact lenses. The frame-type device effectively enables noninvasive and convenient monitoring of blink frequency through sensors, thereby enabling timely detection of certain diseases …”

Section: Primary Formsmentioning

confidence: 99%

Toward the Next Generation Human–Machine Interaction: Headworn Wearable Devices

Gao,

Jiang,

Zhou

et al. 2024

Anal. Chem.

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Section: Primary Formsmentioning

confidence: 99%

Toward the Next Generation Human–Machine Interaction: Headworn Wearable Devices

Gao,

Jiang,

Zhou

et al. 2024

Anal. Chem.

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“…Many of the works discussed leverage a single platform (i.e., either a smartphone or ASIC), but there are still many opportunities for improving the practical use of HAR by exploring intelligent ways to partition computation across the cloud, mobile platforms, and other edge devices. DNN-based HAR systems can largely benefit by incorporating methodologies proposed by works such as [325][326][327][328][329][330][331][332], that carefully partition computation and data across multiple devices and the cloud.…”

Section: Challenges In Model Deploymentmentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

Self Cite

216

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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“…COVID-19 significantly impacted global mental health with high barriers to accessing mental health screenings and treatments, including home care [42,86]. 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].…”

Section: Introductionmentioning

confidence: 99%

“…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]. Recent advances in artificial intelligence (AI) and large language models (LLMs) further expanded the possibilities for intelligent health-oriented applications [29,61,63,92,100].…”

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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SPIDERS+: A light-weight, wireless, and low-cost glasses-based wearable platform for emotion sensing and bio-signal acquisition

Cited by 16 publications

References 23 publications

Toward the Next Generation Human–Machine Interaction: Headworn Wearable Devices

Toward the Next Generation Human–Machine Interaction: Headworn Wearable Devices

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

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

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