mORAL: An m Health Model for Inferring Oral Hygiene Behaviors in-the-wild Using Wrist-worn Inertial Sensors [0.03%]
mORAL:一个使用腕戴式惯性传感器推断真实世界中口腔卫生行为的移动医疗模型
Sayma Akther,Nazir Saleheen,Shahin Alan Samiei et al.
Sayma Akther et al.
We address the open problem of reliably detecting oral health behaviors passively from wrist-worn inertial sensors. We present our model named mORAL (pronounced em oral) for detecting brushing and flossing behaviors, without the use of inst...
Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection [0.03%]
用于床占用检测的自监督表示学习和时空特征融合
Yingjian Song,Zaid Farooq Pitafi,Fei Dou et al.
Yingjian Song et al.
In automated sleep monitoring systems, bed occupancy detection is the foundation or the first step before other downstream tasks, such as inferring sleep activities and vital signs. The existing methods do not generalize well to real-world ...
HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications [0.03%]
HabitSense：一种面向隐私、AI增强的多模态可穿戴平台用于移动医疗应用程序
Glenn J Fernandes,Jiayi Zheng,Mahdi Pedram et al.
Glenn J Fernandes et al.
Wearable cameras provide an objective method to visually confirm and automate the detection of health-risk behaviors such as smoking and overeating, which is critical for developing and testing adaptive treatment interventions. Despite the ...
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data [0.03%]
基于在线文本数据利用大型语言模型进行心理健康预测的精神健康LLM
Xuhai Xu,Bingsheng Yao,Yuanzhe Dong et al.
Xuhai Xu et al.
Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In...
Contextual Biases in Microinteraction Ecological Momentary Assessment (μEMA) Non-response [0.03%]
微交互生态瞬时评估（μEMA）中非回应的上下文偏差
Aditya Ponnada,Jixin Li,Shirlene D Wang et al.
Aditya Ponnada et al.
Ecological momentary assessment (EMA) is used to gather in-situ self-report on behaviors using mobile devices. Microinteraction EMA (μEMA), is a type of EMA where each survey is only one single question that can be answered with a glanceab...
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences [0.03%]
MindScape研究：整合大语言模型和行为感知以实现个性化AI驱动的日记体验
Subigya Nepal,Arvind Pillai,William Campbell et al.
Subigya Nepal et al.
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating ...
Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain [0.03%]
问少学多：通过建模问答信息增益调整生态瞬时评估调查长度
Jixin Li,Aditya Ponnada,Wei-Lin Wang et al.
Jixin Li et al.
Ecological momentary assessment (EMA) is an approach to collect self-reported data repeatedly on mobile devices in natural settings. EMAs allow for temporally dense, ecologically valid data collection, but frequent interruptions with length...
Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare [0.03%]
超越检测：临床心理健康护理中可操作感知研究的方向
Daniel A Adler,Yuewen Yang,Thalia Viranda et al.
Daniel A Adler et al.
Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work sugges...
Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos [0.03%]
基于步态任务视频的自闭症共患病小脑共济失调风险计算机视觉自动评估系统
Wasifur Rahman,Masum Hasan,Md Saiful Islam et al.
Wasifur Rahman et al.
Many patients with neurological disorders, such as Ataxia, do not have easy access to neurologists, -especially those living in remote localities and developing/underdeveloped countries. Ataxia is a degenerative disease of the nervous syste...
Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing [0.03%]
基于手机和可穿戴设备感应的大学生抑郁动态追踪研究
Rui Wang,Weichen Wang,Alex Dasilva et al.
Rui Wang et al.
There are rising rates of depression on college campuses. Mental health services on our campuses are working at full stretch. In response researchers have proposed using mobile sensing for continuous mental health assessment. Existing work ...