Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies最新文献

{"title":"A Context-Assisted, Semi-Automated Activity Recall Interface Allowing Uncertainty.","authors":"H A LE, Veronika Potter, Akshat Choube, Rithika Lakshminarayanan, Varun Mishra, Stephen Intille","doi":"10.1145/3770710","DOIUrl":"10.1145/3770710","url":null,"abstract":"<p><p>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 models for activity recognition and measurement is asking participants to self-report their daily activities. Although participants can typically recall their sequence of daily activities, remembering the precise start and end times of each activity is significantly more challenging. ACAI is a novel, context-assisted <b>AC</b>tivity <b>A</b>nnotation <b>I</b>nterface that enables participants to efficiently label their activities by accepting or adjusting system-generated activity suggestions while explicitly expressing uncertainty about temporal boundaries. We evaluated ACAI using two complementary studies: a usability study with 11 participants and a two-week, free-living study with 14 participants. We compared our activity annotation system with the current gold-standard methods for activity recall in health sciences research: 24PAR and its computerized version, ACT24. Our system reduced annotation time and perceived effort while significantly improving data validity and fidelity compared to both standard human-supervised and unsupervised activity recall approaches. We discuss the limitations of our design and implications for developing adaptive, human-in-the-loop activity recognition systems used to collect self-report data on activity.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 4","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12758905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145900881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.","authors":"Veronika Potter, Hoan Tran, Daniel Mobley, Suzanne M Bertisch, Dinesh John, Stephen Intille","doi":"10.1145/3770639","DOIUrl":"10.1145/3770639","url":null,"abstract":"<p><p>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, sedentary behaviors, and sleep using wearable sensors; however, many human activity recognition algorithms cannot reliably detect activities in true free-living settings because they are trained on data collected in a controlled, lab setting. We describe the data collection protocol and present the first release of a multimodal, multi-sensor-site dataset (PAAWS R1). The PAAWS R1 release includes ~4 hours of semi-naturalistic activities from 252 individuals and ~7 days of 24-hour, free-living activities from 20 adults. We have annotated waking day activities using video to provide second-by-second, ground-truth labels capturing short, quickly changing bouts of activity with realistic activity transitions. Additionally, we have labeled up to two nights of sleep stages from PSG data collected during some nights of the free-living protocol. The PAAWS dataset enables researchers to directly compare activity recognition algorithms on the same participants' data across multiple collection protocols and days of free-living behaviors, encouraging convergence towards robust algorithms that could aid health research and drive novel mobile computing interventions and applications.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 4","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12893702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Sensing and Modeling of Endocrine Therapy Adherence in Breast Cancer Survivors.","authors":"Fangxu Yuan, Navreet Kaur, Zhiyuan Wang, Manuel Gonzales, Cristian Garcia Alcaraz, Gabriel Estrella, Kristen J Wells, Laura E Barnes","doi":"10.1145/3770864","DOIUrl":"10.1145/3770864","url":null,"abstract":"<p><p>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 interrelated influences of behavioral, physiological, and psychological factors. While prior studies have explored adherence prediction using mobile sensing, they often rely on single-modality data, limited temporal granularity, or aggregate-level modeling-limiting their ability to capture short and long-term behavioral variability and to facilitate deeper understanding of non-adherence and tailored interventions. To address these gaps, we propose a multimodal sensing framework that explicitly models daily adherence dynamics using temporally adaptive inputs. We recruited a sample of breast cancer survivors (<i>N</i> = 20) and collected longitudinal data streams including wearable-derived physiological features (Fitbit), medication event monitoring system (MEMS) data, and ecological momentary assessments (EMAs). Using multimodal data across varying time windows, we examined whether recent patterns in behavioral, physiological, psychological, and environmental factors improve the prediction of next-day endocrine therapy adherence. Our results demonstrate the feasibility of using multimodal sensing data to predict daily adherence with moderate accuracy. Moreover, models integrating multimodal data consistently outperformed those relying on a single modality. Importantly, we observed that the predictive value of each modality varied depending on the temporal proximity of the input signals, underscoring the importance of modeling immediate and longer-term behavioral patterns. The findings offer valuable insights for advancing adherence monitoring systems, suggesting that incorporating personalized and temporally adaptive data fusion strategies may significantly enhance the effectiveness of intervention design and delivery.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 4","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12711140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145782251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA).","authors":"Aditya Ponnada, Shirlene D Wang, Jixin Li, Wei-Lin Wang, Genevieve F Dunton, Donald Hedeker, Stephen S Intille","doi":"10.1145/3749541","DOIUrl":"10.1145/3749541","url":null,"abstract":"<p><p>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 up to 4 weeks. In this paper, we evaluated μEMA's longitudinal engagement in a 12-month study. Each participant completed EMA surveys (one smartphone prompt/hour for 96 days in 4-day bursts) and μEMA surveys (four smartwatch prompts/hour for the 270 days). Using data from 177 participants ( 1.37 million μEMA and 14.9K EMA surveys), we compared engagement across three groups: those who completed 12 months of EMA data collection(<i>Completed</i>), those who voluntarily withdrew after six months of EMA data collection (<i>Withdrew</i>), and those unenrolled by staff after six months of poor EMA response rates (<i>Unenrolled</i>). Compared to EMA, unenrolled participants were 2.25 times, those who withdrew were 1.65 times, and completed participants were 1.53 times more likely to answer μEMA prompts (<i>p</i> < 0.001). Regardless of response rates, μEMA was perceived as less burdensome than EMA (<i>p</i> < 0.001). These results suggest μEMA is a viable method for intensive longitudinal data collection, particularly for participants who find EMA unsustainable.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 3","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engagements with Generative AI and Personal Health Informatics: Opportunities for Planning, Tracking, Reflecting, and Acting around Personal Health Data.","authors":"Shaan Chopra, Katherine Juarez, James Fogarty, Sean A Munson","doi":"10.1145/3749503","DOIUrl":"10.1145/3749503","url":null,"abstract":"<p><p>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 processes. We investigate the potential of Generative AI (GAI) to support people across stages of pursuing self-tracking for health. We conducted interview and observation sessions with 19 participants from the United States who self-track for health, examining how they interact with GAI around their personal health data. Participants formulated and refined queries, reflected on recommendations, and abandoned queries that did not meet their needs and health goals. They further identified opportunities for GAI support across stages of self-tracking, including in deciding what data to track and how, in defining and modifying tracking plans, and in interpreting data-driven insights. We discuss GAI opportunities in accounting for a range of health goals, in providing support for self-tracking processes across planning, reflection, and action, and in consideration of limitations of embedding GAI in health self-tracking tools.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 3","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NavGraph: Enhancing Blind Travelers' Navigation Experience and Safety.","authors":"Sergio Mascetti, Dragan Ahmetovic, Gabriele Galimberti, James M Coughlan","doi":"10.1145/3749537","DOIUrl":"10.1145/3749537","url":null,"abstract":"<p><p>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 safety while minimizing the number of instructions delivered to the user. We demonstrate the application of this principle through NavGraph, a navigation application adopting a modular architecture comprising four components: localization, routing, guidance, and user interface. NavGraph is designed to provide effective, non-intrusive navigation assistance by optimizing route computation and instruction delivery. We evaluated NavGraph in a user study with 10 blind participants, comparing it to a baseline solution. Results show that NavGraph significantly reduces the number of instructions and improves clarity and safety, without compromising navigation time. These findings support the potential of the Parsimonious Instructions design principle in assistive navigation technologies.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 3","pages":"117"},"PeriodicalIF":4.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12682350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145709761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRoP: Context-wise Robust Static Human-Sensing Personalization.","authors":"Sawinder Kaur, Avery Gump, Y I Xiao, Jingyu Xin, Harshit Sharma, Nina R Benway, Jonathan L Preston, Asif Salekin","doi":"10.1145/3729483","DOIUrl":"https://doi.org/10.1145/3729483","url":null,"abstract":"<p><p>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 performance due to natural distribution shifts. To address this, personalization tailors models to individual users. Yet most personalization studies overlook intra-user heterogeneity across contexts in sensory data, limiting intra-user generalizability. This limitation is especially critical in clinical applications, where limited data availability hampers both generalizability and personalization. Notably, intra-user sensing attributes are expected to change due to external factors such as treatment progression, further complicating the challenges. To address the intra-user generalization challenge, this work introduces CRoP, a novel static personalization approach. CRoP leverages off-the-shelf pre-trained models as generic starting points and captures user-specific traits through adaptive pruning on a minimal sub-network while allowing generic knowledge to be incorporated in remaining parameters. CRoP demonstrates superior personalization effectiveness and intra-user robustness across four human-sensing datasets, including two from real-world health domains, underscoring its practical and social impact. Additionally, to support CRoP's generalization ability and design choices, we provide empirical justification through gradient inner product analysis, ablation studies, and comparisons against state-of-the-art baselines.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 2","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147819949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Older Adults to Provide High-quality Activity Labels: Unpacking Accuracy, Precision, and Granularity in Activity Labeling.","authors":"Yiwen Wang, Hossein Khayami, Bongshin Lee, Amanda Lazar, Hernisa Kacorri, Eun Kyoung Choe","doi":"10.1145/3770649","DOIUrl":"10.1145/3770649","url":null,"abstract":"<p><p>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 presents significant challenges, placing a heavy burden on users to provide high-quality labels while staying engaged in their activities. This paper investigates older adults' perceptions of providing high-quality labels in the context of training their personalized activity trackers. We conducted a co-design study with 12 older adults to envision the labeling process-describing activity names and time spans-using the teachable machines paradigm as a scaffold. We unpack the contextualized definitions of accuracy, precision, and granularity through a thematic analysis of older adults' perspectives on activity labeling. Our findings present participants' preferred strategies for obtaining high-quality activity labels with less burden and intrusiveness, including user-initiated labeling and machine-initiated prompting. We discuss design considerations for future data labeling tools that address discrepancies between user perceptions and technical standards in training personalized activity trackers.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"9 4","pages":"1-24"},"PeriodicalIF":4.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12955817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147356442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain.","authors":"Jixin Li, Aditya Ponnada, Wei-Lin Wang, Genevieve F Dunton, Stephen S Intille","doi":"10.1145/3699735","DOIUrl":"10.1145/3699735","url":null,"abstract":"<p><p>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 lengthy surveys on mobile devices can burden users, impacting compliance and data quality. We propose a method that reduces the length of each EMA question set measuring interrelated constructs, with only modest information loss. By estimating the potential information gain of each EMA question using question-answer prediction models, this method can prioritize the presentation of the most informative question in a question-by-question sequence and skip uninformative questions. We evaluated the proposed method by simulating question omission using four real-world datasets from three different EMA studies. When compared against the random question omission approach that skips 50% of the questions, our method reduces imputation errors by 15%-52%. In surveys with five answer options for each question, our method can reduce the mean survey length by 34%-56% with a real-time prediction accuracy of 72%-95% for the skipped questions. The proposed method may either allow more constructs to be surveyed without adding user burden or reduce response burden for more sustainable longitudinal EMA data collection.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"8 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.","authors":"Daniel A Adler, Yuewen Yang, Thalia Viranda, Xuhai Xu, David C Mohr, Anna R VAN Meter, Julia C Tartaglia, Nicholas C Jacobson, Fei Wang, Deborah Estrin, Tanzeem Choudhury","doi":"10.1145/3699755","DOIUrl":"10.1145/3699755","url":null,"abstract":"<p><p>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 suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.</p>","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"8 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11620792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}