Frontiers in Computer Science | Mobile and Ubiquitous Computing section | New and Recent Articles https://www.frontiersin.org/journals/computer-science/sections/mobile-and-ubiquitous-computing RSS Feed for Mobile and Ubiquitous Computing section in the Frontiers in Computer Science journal | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-05-12T08:16:01.681+00:00 60 https://www.frontiersin.org/articles/10.3389/fcomp.2026.1763420 https://www.frontiersin.org/articles/10.3389/fcomp.2026.1763420 <![CDATA[Hardware and software system for adaptive precision agriculture management within the consolidation of modern agrotechnologies in the crop production sector of the Kyrgyz Republic]]> 2026-03-03T00:00:00Z Original Research Alex KarpovOleg DorofeevYuliya SmirnovaIrina KulibabaYana Beresneva https://www.frontiersin.org/articles/10.3389/fcomp.2026.1700489 https://www.frontiersin.org/articles/10.3389/fcomp.2026.1700489 <![CDATA[Bursting the bubble: data leakage and inflated deep learning accuracy in multivariate time-series frailty classification]]> 2026-02-23T00:00:00Z Original Research Charmayne Mary Lee HughesYan Zhang 98% accuracy), exceeding values typically observed in comparable studies.MethodsWe conducted a methodological re-evaluation and replication of this pipeline to assess the robustness of reported performance. Corrections included subject-wise data partitioning, feature scaling within training folds, and non-overlapping sliding time windows applied separately to each subset to prevent potential leakage.ResultsReimplementation of the original pipeline reproduced the previously reported high accuracy. After applying the corrected framework, overall recall and precision decreased (50.9 and 52.3%, respectively), providing a more conservative, data-specific estimate of model generalizability. Per-class analysis indicated reductions across all categories, with Frail-class recall dropping to 21.4%, highlighting the particular challenge of identifying high-risk individuals.ConclusionThe findings suggest that methodological factors, such as data leakage, likely contributed to the previously reported high performance. Under rigorous controls, frailty prediction is challenging, particularly for the frail class, underscoring the need for careful evaluation of model generalizability.SignificanceThis study illustrates the importance of transparent, methodologically sound pipelines in clinical AI research. By providing a reproducible framework for frailty prediction, we aim to support future studies in obtaining realistic performance estimates and developing clinically meaningful models.]]> https://www.frontiersin.org/articles/10.3389/fcomp.2026.1752141 https://www.frontiersin.org/articles/10.3389/fcomp.2026.1752141 <![CDATA[A biometric dataset for unconditioned gait identification using onboard smartphone sensors]]> 2026-01-27T00:00:00Z Data Report Ladeh Sardar AbdulrahmanAzhin T. SabirHalgurd S. Maghdid https://www.frontiersin.org/articles/10.3389/fcomp.2025.1663987 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1663987 <![CDATA[Classification of smartphone users as adult or child in both constrained and non-constrained environments using mRMR-based feature selection and an ensemble classifier]]> 2026-01-06T00:00:00Z Original Research Nikhat H. FaheemSaad Yunus Sait https://www.frontiersin.org/articles/10.3389/fcomp.2025.1696178 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1696178 <![CDATA[Assisting annotators of wearable activity recognition datasets through automated sensor-based suggestions]]> 2025-11-13T00:00:00Z Original Research Lukas GünthermannIvor SimpsonPhil BirchDaniel Roggen https://www.frontiersin.org/articles/10.3389/fcomp.2025.1575404 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1575404 <![CDATA[Unobtrusive stress detection using wearables: application and challenges in a university setting]]> 2025-08-18T00:00:00Z Original Research Peter NeigelAndrew VargoBenjamin TagKoichi Kise https://www.frontiersin.org/articles/10.3389/fcomp.2025.1597143 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1597143 <![CDATA[Deep learning for human locomotion analysis in lower-limb exoskeletons: a comparative study]]> 2025-08-13T00:00:00Z Original Research Omar CoserChristian TamantiniMatteo TortoraLeonardo FuriaRosa SiciliaLoredana ZolloPaolo Soda https://www.frontiersin.org/articles/10.3389/fcomp.2025.1569205 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1569205 <![CDATA[Improving IMU based human activity recognition using simulated multimodal representations and a MoE classifier]]> 2025-08-12T00:00:00Z Original Research Lala Shakti Swarup RayQingxin XiaVitor Fortes ReyKaishun WuPaul Lukowicz https://www.frontiersin.org/articles/10.3389/fcomp.2025.1585632 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1585632 <![CDATA[Mobile application based on KDD to predict high-crime areas and promote sustainability in citizen security in a district of Lima-Perú]]> 2025-08-11T00:00:00Z Original Research Hugo Vega-HuertaJavier Vilca VelasquezNicolas Anicama EspinozaGisella Luisa Elena Maquen-NiñoLuis Guerra-GradosJorge Pantoja-CollantesOscar Benito-PachecoJuan Carlos Lázaro-GuillermoAdegundo Camara-FigueroaJavier Cabrera-DíazRubén Gil-CalvoFrida López-Córdova https://www.frontiersin.org/articles/10.3389/fcomp.2025.1464348 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1464348 <![CDATA[Trusting AI: does uncertainty visualization affect decision-making?]]> 2025-02-07T00:00:00Z Original Research Jonatan ReyesAnil Ufuk BatmazMarta Kersten-Oertel https://www.frontiersin.org/articles/10.3389/fcomp.2025.1514933 https://www.frontiersin.org/articles/10.3389/fcomp.2025.1514933 <![CDATA[WIMUSim: simulating realistic variabilities in wearable IMUs for human activity recognition]]> 2025-01-23T00:00:00Z Original Research Nobuyuki OishiPhil BirchDaniel RoggenPaula Lago https://www.frontiersin.org/articles/10.3389/fcomp.2024.1525382 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1525382 <![CDATA[Towards an intelligent energy conservation approach for context-aware systems in smart environments]]> 2024-12-19T00:00:00Z Original Research Umar MahmudShariq HussainTehmina Karamat https://www.frontiersin.org/articles/10.3389/fcomp.2024.1478851 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1478851 <![CDATA[Detection and monitoring of stress using wearables: a systematic review]]> 2024-12-18T00:00:00Z Systematic Review Anuja PingeVinaya GadDheryta JaisighaniSurjya GhoshSougata Sen https://www.frontiersin.org/articles/10.3389/fcomp.2024.1465793 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1465793 <![CDATA[Editorial: Wearable computing, volume II]]> 2024-08-07T00:00:00Z Editorial Bo ZhouCheng ZhangBashima Islam https://www.frontiersin.org/articles/10.3389/fcomp.2024.1379788 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1379788 <![CDATA[A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensors]]> 2024-07-18T00:00:00Z Original Research Alexander HoelzemannKristof Van Laerhoven https://www.frontiersin.org/articles/10.3389/fcomp.2024.1394397 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1394397 <![CDATA[Energy-efficient, low-latency, and non-contact eye blink detection with capacitive sensing]]> 2024-06-11T00:00:00Z Original Research Mengxi LiuSizhen BianZimin ZhaoBo ZhouPaul Lukowicz https://www.frontiersin.org/articles/10.3389/fcomp.2024.1385392 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1385392 <![CDATA[A magnetometer-based method for in-situ syncing of wearable inertial measurement units]]> 2024-04-19T00:00:00Z Original Research Thomas J. GilbertZexiao LinSally DayAntonia F. de C. HamiltonJamie A. Ward https://www.frontiersin.org/articles/10.3389/fcomp.2024.1379925 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1379925 <![CDATA[A comprehensive evaluation of marker-based, markerless methods for loose garment scenarios in varying camera configurations]]> 2024-04-05T00:00:00Z Original Research Lala Shakti Swarup RayBo ZhouSungho SuhPaul Lukowicz 10 cm). However, in scenarios involving everyday activities with basic and swift motions, markerless MoCap outperforms marker-based alternatives. This positions markerless MoCap as an advantageous and economical choice for wearable studies. The inclusion of a multicamera deep learning MoCap method in the benchmarking tool further expands the scope, allowing researchers to assess the capabilities of cutting-edge technologies in diverse motion capture scenarios.]]> https://www.frontiersin.org/articles/10.3389/fcomp.2024.1365500 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1365500 <![CDATA[Augmenting context with power information for green context-awareness in smart environments]]> 2024-03-07T00:00:00Z Original Research Umar MahmudShariq Hussain https://www.frontiersin.org/articles/10.3389/fcomp.2024.1347424 https://www.frontiersin.org/articles/10.3389/fcomp.2024.1347424 <![CDATA[Where to mount the IMU? Validation of joint angle kinematics and sensor selection for activities of daily living]]> 2024-02-29T00:00:00Z Original Research Lena UhlenbergOliver Amft