Sawinder Kaur,Avery Gump,Y I Xiao et al.
Sawinder Kaur et al.
The advancement in deep learning and internet-of-things have led to diverse human sensing applications. However, distinct patterns in human sensing, influenced by various factors or contexts, challenge the generic neural network model's per...
Engagements with Generative AI and Personal Health Informatics: Opportunities for Planning, Tracking, Reflecting, and Acting around Personal Health Data [0.03%]
生成式AI和个性化健康管理中的参与：围绕个人健康数据进行规划、跟踪、反思和行动的机会
Shaan Chopra,Katherine Juarez,James Fogarty et al.
Shaan Chopra et al.
Personal informatics processes require navigating distinct challenges across stages of tracking, but the range of data, goals, expertise, and context that individuals bring to self-tracking often presents barriers that undermine those proce...
SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks [0.03%]
利用动态社交网络的注意力增强型鲁棒睡眠预测模型SleepNet
Maryam Khalid,Elizabeth B Klerman,Andrew W McHill et al.
Maryam Khalid et al.
Sleep behavior significantly impacts health and acts as an indicator of physical and mental well-being. Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related h...
Enabling Older Adults to Provide High-quality Activity Labels: Unpacking Accuracy, Precision, and Granularity in Activity Labeling [0.03%]
促进老年人提供高质量的活动标签：拆解活动标签中的准确性、精确性和粒度问题
Yiwen Wang,Hossein Khayami,Bongshin Lee et al.
Yiwen Wang et al.
High-quality labels of activity data with broad representations and real-world variability are key to developing activity recognition models tailored to the needs and characteristics of older adults. However, labeling real-world data presen...
The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data [0.03%]
基于可穿戴传感器的体力活动评估(PAAWS)数据集:实验室和日常生活环境中的加速度计数据
Veronika Potter,Hoan Tran,Daniel Mobley et al.
Veronika Potter et al.
Poor sleep and sedentary behavior patterns increase the risk of chronic diseases and negatively impact an individual's health and quality of life. Large-scale surveillance studies can unobtrusively measure free-living physical activities, s...
A Context-Assisted, Semi-Automated Activity Recall Interface Allowing Uncertainty [0.03%]
一种上下文支持的、半自动化的活动回忆界面，允许不确定性的存在
H A LE,Veronika Potter,Akshat Choube et al.
H A LE et al.
Measuring activities and postures is an important area of research in ubiquitous computing, human-computer interaction, and personal health informatics. One approach that researchers use to collect large amounts of labeled data to develop m...
Multimodal Sensing and Modeling of Endocrine Therapy Adherence in Breast Cancer Survivors [0.03%]
乳腺癌幸存者的内分泌治疗依从性的多模态感知与建模
Fangxu Yuan,Navreet Kaur,Zhiyuan Wang et al.
Fangxu Yuan et al.
Many breast cancer survivors are prescribed daily oral medications called endocrine therapy that prevent cancer recurrence. Despite its clinical importance, maintaining consistent daily adherence remains challenging due to the dynamic and i...
NavGraph: Enhancing Blind Travelers' Navigation Experience and Safety [0.03%]
NavGraph：增强盲人旅行者的导航体验和安全感
Sergio Mascetti,Dragan Ahmetovic,Gabriele Galimberti et al.
Sergio Mascetti et al.
Independent navigation remains a significant challenge for blind and low vision individuals, especially in unfamiliar environments. In this paper, we introduce the Parsimonious Instructions design principle, which aims to enhance navigation...
Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment [0.03%]
基于音频的生态瞬时评估在日常生活中收集自报的体力活动和姿势数据中的应用
H A LE,Rithika Lakshminarayanan,Jixin Li et al.
H A LE et al.
μEMA is a data collection method that prompts research participants with quick, answer-at-a-glance, single-multiple-choice self-report behavioral questions, thus enabling high-temporal-density self-report of up to four times per hour when ...
Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA) [0.03%]
基于微互动生态瞬时评估(μEMA)的纵向用户参与度研究
Aditya Ponnada,Shirlene D Wang,Jixin Li et al.
Aditya Ponnada et al.
Microinteraction ecological momentary assessment (μEMA) is a type of EMA that uses single-question prompts on a smartwatch to collect real-world self-reports. Smaller-scale studies show that μEMA yields higher response rates than EMA for ...