Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults
Open accessPublished version of a preprint
Abstract
Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Gait speed and survival in older adults.
Studenski, S. (2011) · JAMA · doi:10.1001/jama.2010.1923 · Cited by 4,558 · see citing works
Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability
Guralnik, J. M., Ferrucci, L., Simonsick, E. M., Salive, M. E., & Wallace, R. B. (1995) · New England Journal of Medicine · doi:10.1056/nejm199503023320902 · Cited by 4,083 · see citing works
Religious Orders Study and Rush Memory and Aging Project
Bennett, D. A., Buchman, A. S., Boyle, P. A., Barnes, L. L., Wilson, R. S., & Schneider, J. A. (2018) · Journal of Alzheimer's Disease · doi:10.3233/jad-179939 · Cited by 1,260 · see citing works
Self-citationDoherty, A., Jackson, D., Hammerla, N., Plötz, T., Olivier, P., Granat, M. H., White, T., van Hees, V. T., Trenell, M. I., Owen, C. G., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169649 · Cited by 1,250 · see citing works
Overview and Findings from the Rush Memory and Aging Project
Bennett, D. A., Schneider, J. A., Buchman, A. S., Barnes, L. L., Boyle, P. A., & Wilson, R. S. (2012) · Current Alzheimer Research · doi:10.2174/156720512801322663 · Cited by 837 · see citing works
Self-citationLim, A. S. P., Kowgier, M., Yu, L., Buchman, A. S., & Bennett, D. A. (2013) · Sleep · doi:10.5665/sleep.2802 · Cited by 793 · see citing works
Self-citationOverview and Findings from the Religious Orders Study
Bennett, D. A., Schneider, J. A., Arvanitakis, Z., & Wilson, R. S. (2012) · Current Alzheimer Research · doi:10.2174/156720512801322573 · Cited by 647 · see citing works
Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data
Feehan, L. M., Geldman, J., Sayre, E. C., Park, C., Ezzat, A. M., Yoo, J. Y., Hamilton, C. B., & Li, L. C. (2018) · JMIR mHealth and uHealth · doi:10.2196/10527 · Cited by 616 · see citing works
Balance and gait in the elderly: A contemporary review
Osoba, M., Rao, A. K., Agrawal, S. K., & Lalwani, A. K. (2019) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.252 · Cited by 506 · see citing works
Mirelman, A., Herman, T., Brozgol, M., Dorfman, M., Sprecher, E., Schweiger, A., Giladi, N., & Hausdorff, J. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040297 · Cited by 461 · see citing works
Self-citationMhyre, T. R., Boyd, J. T., Hamill, R. W., & Maguire‐Zeiss, K. A. (2012) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-94-007-5416-4_16 · Cited by 442 · see citing works
Multi-task Self-Supervised Learning for Human Activity Detection
Saeed, A., Ozcelebi, T., & Lukkien, J. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3328932 · Cited by 346 · see citing works
Long-term unsupervised mobility assessment in movement disorders
Warmerdam, E., Hausdorff, J. M., Atrsaei, A., Zhou, Y., Mirelman, A., Aminian, K., Espay, A. J., Hansen, C., Evers, L. J. W., Keller, A., et al. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30397-7 · Cited by 306 · see citing works
Self-citationSabia, S., van Hees, V. T., Shipley, M. J., Trenell, M. I., Hagger-Johnson, G., Elbaz, A., Kivimaki, M., & Singh-Manoux, A. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwt330 · Cited by 304 · see citing works
van Hees, V. T., Renström, F., Wright, A., Gradmark, A., Catt, M., Chen, K. Y., Löf, M., Bluck, L. J. C., Pomeroy, J. J., Wareham, N. J., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022922 · Cited by 289 · see citing works
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250 · see citing works
Hillel, I., Gazit, E., Nieuwboer, A., Avanzino, L., Rochester, L., Cereatti, A., Croce, U. D., Rikkert, M. G. M. O., Bloem, B. R., Pelosin, E., et al. (2019) · European Review of Aging and Physical Activity · doi:10.1186/s11556-019-0214-5 · Cited by 226 · see citing works
Self-citationMobility in Older Community-Dwelling Persons: A Narrative Review
Freiberger, E., Sieber, C. C., & Kob, R. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.00881 · Cited by 200 · see citing works
Normal Changes in Gait and Mobility Problems in the Elderly
Cruz-Jiménez, M. (2017) · Physical Medicine and Rehabilitation Clinics of North America · doi:10.1016/j.pmr.2017.06.005 · Cited by 186 · see citing works
Latham, N. K., Mehta, V., Nguyen, A. M., Jette, A. M., Olarsch, S., Papanicolaou, D. A., & Chandler, J. (2008) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2008.04.016 · Cited by 174 · see citing works
Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis
Schalkamp, A., Peall, K. J., Harrison, N. A., & Sandor, C. (2023) · Nature Medicine · doi:10.1038/s41591-023-02440-2 · Cited by 172 · see citing works
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
Yuan, H., Chan, S., Creagh, A. P., Tong, C., Acquah, A., Clifton, D. A., & Doherty, A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01062-3 · Cited by 166 · cited 6× · see citing works
“The first stage involved the training of an SSL model, utilizing the pre-trained UK Biobank model of Yuan et al. 38 .”
InfluentialContrastive Predictive Coding for Human Activity Recognition
Haresamudram, H., Essa, I., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3463506 · Cited by 150 · see citing works
Micó-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Kirk, C., Soltani, A., Küderle, A., Gazit, E., Salis, F., Alcock, L., et al. (2023) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01198-5 · Cited by 149 · see citing works
Self-citationTechnical validation of real-world monitoring of gait: a multicentric observational study
Mazzà, C., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Bonci, T., Brown, P. M., Brozgol, M., Buckley, E. E., Carsin, A., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-050785 · Cited by 119 · see citing works
Self-citationAssessing the State of Self-Supervised Human Activity Recognition Using Wearables
Haresamudram, H., Essa, I., & Plötz, T. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550299 · Cited by 107 · cited 2× · see citing works
“We attempted to address this by standardizing both datasets (MAP and Mobilise-D) using a zero-mean unit-variance whitening 35 .”
Organizing and Analyzing the Activity Data in NHANES
Leroux, A., Di, J., Smirnova, E., McGuffey, E. J., Cao, Q., Mokhtari, E. B., Tabacu, L. M., Zipunnikov, V. M., Urbanek, J. K., & Crainiceanu, C. M. (2019) · Statistics in Biosciences · doi:10.1007/s12561-018-09229-9 · Cited by 100 · see citing works
Pulmonary Function, Muscle Strength, and Incident Mobility Disability in Elders
Buchman, A. S., Boyle, P. A., Leurgans, S. E., Evans, D. A., & Bennett, D. A. (2009) · Proceedings of the American Thoracic Society · doi:10.1513/pats.200905-030rm · Cited by 90 · see citing works
Self-citationFalls Risk in Relation to Activity Exposure in High-Risk Older Adults
Din, S. D., Galna, B., Lord, S., Nieuwboer, A., Bekkers, E. M. J., Pelosin, E., Avanzino, L., Bloem, B. R., Rikkert, M. G. M. O., Nieuwhof, F., et al. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa007 · Cited by 89 · see citing works
Self-citationA multi-sensor wearable system for the assessment of diseased gait in real-world conditions
Salis, F., Bertuletti, S., Bonci, T., Caruso, M., Scott, K., Alcock, L., Buckley, E. E., Gazit, E., Hansen, C., Schwickert, L., et al. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1143248 · Cited by 85 · see citing works
Self-citationKirk, C., Küderle, A., Micó-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Soltani, A., Gazit, E., Salis, F., Alcock, L., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-51766-5 · Cited by 83 · see citing works
Self-citationEffects of Aging on Arm Swing during Gait: The Role of Gait Speed and Dual Tasking
Mirelman, A., Bernad‐Elazari, H., Nobel, T., Thaler, A., Peruzzi, A., Plotnik, M., Giladi, N., & Hausdorff, J. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0136043 · Cited by 83 · see citing works
Self-citationBurq, M., Rainaldi, E., Ho, K. C., Chen, C., Bloem, B. R., Evers, L. J. W., Helmich, R. C. G., Myers, L. J., Marks, W. J., & Kapur, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00607-8 · Cited by 75 · see citing works
Incident parkinsonism in older adults without Parkinson disease
Buchman, A. S., Leurgans, S. E., Yu, L., Wilson, R. S., Lim, A. S. P., James, B. D., Shulman, J. M., & Bennett, D. A. (2016) · Neurology · doi:10.1212/wnl.0000000000003059 · Cited by 74 · see citing works
Self-citationReal-World Gait Speed Estimation Using Wrist Sensor: A Personalized Approach
Soltani, A., Dejnabadi, H., Savary, M., & Aminian, K. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2914940 · Cited by 66 · see citing works
Circadian disturbances and frailty risk in older adults
Cai, R., Gao, L., Gao, C., Yu, L., Zheng, X., Bennett, D. A., Buchman, A. S., Hu, K., & Li, P. (2023) · Nature Communications · doi:10.1038/s41467-023-42727-z · Cited by 65 · see citing works
Self-citationHausdorff, J. M., Hillel, I., Shustak, S., Din, S. D., Bekkers, E. M. J., Pelosin, E., Nieuwhof, F., Rochester, L., & Mirelman, A. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx187 · Cited by 61 · see citing works
Self-citationBrodie, M. A. D., Pliner, E. M., Ho, A., Li, K., Chen, Z., Gandevia, S. C., & Lord, S. R. (2018) · Medical Hypotheses · doi:10.1016/j.mehy.2018.07.015 · Cited by 57 · see citing works
Buchman, A. S., Dawe, R. J., Leurgans, S. E., Curran, T. A., Truty, T., Yu, L., Barnes, L. L., Hausdorff, J. M., & Bennett, D. A. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz160 · Cited by 53 · see citing works
Self-citationA method for gait events detection based on low spatial resolution pressure insoles data
Salis, F., Bertuletti, S., Bonci, T., Croce, U. D., Mazzà, C., & Cereatti, A. (2021) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2021.110687 · Cited by 45 · see citing works
Self-citationBuchman, A. S., Leurgans, S. E., Weiss, A., VanderHorst, V., Mirelman, A., Dawe, R. J., Barnes, L. L., Wilson, R. S., Hausdorff, J. M., & Bennett, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086262 · Cited by 43 · see citing works
Self-citationReal-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study
Kluge, F., Brand, Y. E., Micó-Amigo, M. E., Bertuletti, S., D’Ascanio, I., Gazit, E., Bonci, T., Kirk, C., Küderle, A., Palmerini, L., et al. (2024) · JMIR Formative Research · doi:10.2196/50035 · Cited by 40 · see citing works
Self-citationBrodie, M. A. D., Lovell, N. H., Canning, C. G., Menz, H. B., Delbaere, K., Redmond, S. J., Latt, M. D., Sturnieks, D. L., Menant, J. C., Smith, S., et al. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944988 · Cited by 32 · see citing works
Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults
von Coelln, R., Dawe, R. J., Leurgans, S. E., Curran, T. A., Truty, T., Yu, L., Barnes, L. L., Shulman, J. M., Shulman, L. M., Bennett, D. A., et al. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.06.012 · Cited by 32 · see citing works
Self-citationDevelopment and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers
Chan, L. L. Y., Choi, T. C. M., Lord, S. R., & Brodie, M. A. D. (2022) · Scientific Reports · doi:10.1038/s41598-022-20327-z · Cited by 32 · see citing works
Brand, Y. E., Schwartz, D., Gazit, E., Buchman, A. S., Gilad-Bachrach, R., & Hausdorff, J. M. (2022) · Sensors · doi:10.3390/s22187094 · Cited by 30 · see citing works
Self-citationReal-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation
Soltani, A., Paraschiv-Ionescu, A., Dejnabadi, H., Marques-Vidal, P. M., & Aminian, K. (2020) · IEEE Access · doi:10.1109/access.2020.2998842 · Cited by 29 · see citing works
Self-citationKeren, K., Busse, M. E., Fritz, N. E., Muratori, L. M., Gazit, E., Hillel, I., Scheinowitz, M., Gurevich, T., Inbar, N., Omer, N., et al. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.719442 · Cited by 27 · see citing works
Self-citationAn Objective Methodology for the Selection of a Device for Continuous Mobility Assessment
Bonci, T., Keogh, A., Din, S. D., Scott, K., & Mazzà, C. (2020) · Sensors · doi:10.3390/s20226509 · Cited by 26 · see citing works
The value of walking: A systematic review on mobility and healthcare costs
Wohlrab, M., Klenk, J., Delgado-Ortiz, L., Chambers, M., Rochester, L., Zuchowski, M., Schwab, M., Becker, C., & Jaeger, S. U. (2022) · European Review of Aging and Physical Activity · doi:10.1186/s11556-022-00310-3 · Cited by 26 · see citing works
Self-citationSmall, S. R., Chan, S., Walmsley, R., Fritsch, L. V., Acquah, A., Mertes, G., Feakins, B. G., Creagh, A., Strange, A., Matthews, C. E., et al. (2023) · doi:10.1101/2023.02.20.23285750 · Cited by 22 · see citing works
Bianchini, E., Galli, S., Alborghetti, M., Carolis, L. D., Zampogna, A., Hansen, C., Vuillerme, N., Suppa, A., & Pontieri, F. E. (2023) · Sensors · doi:10.3390/s23218971 · Cited by 19 · see citing works
Zhao, A., Cui, E., Leroux, A., Lindquist, M. A., & Crainiceanu, C. M. (2023) · Journal of Neurology · doi:10.1007/s00415-023-11939-0 · Cited by 14 · see citing works
Κουδουνά, Σ., Evangelopoulos, D. S., Sarantis, M., Chronopoulos, E., Dontas, I. A., & Pneumaticos, S. G. (2023) · Life · doi:10.3390/life13081748 · Cited by 10 · see citing works
Buchman, A. S., Wang, T., Oveisgharan, S., Zammit, A. R., Yu, L., Li, P., Hu, K., Hausdorff, J. M., Lim, A. S. P., & Bennett, D. A. (2023) · Sensors · doi:10.3390/s23084152 · Cited by 7 · see citing works
Self-citationDon't freeze: Finetune encoders for better Self-Supervised HAR
Rey, V. F., Nshimyimana, D., & Lukowicz, P. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3610790 · Cited by 5 · see citing works
Lin, W., Karahanoğlu, F. I., Psaltos, D., Adamowicz, L., Santamaria, M., Cai, X., Demanuele, C., & Di, J. (2023) · Sensors · doi:10.3390/s23208542 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Digital Technologies for Symptom Monitoring in Parkinson Disease
Chunga, N., Reddy, V., Barbosa, W., & Adams, J. (2026) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-026-01481-7 · Cited by 9 · see citing works
Utility of synthetic musculoskeletal gaits for generalizable healthcare applications
Yamada, Y., Kobayashi, M., Shinkawa, K., Bilal, E., Liao, J., Nemoto, M., Ota, M., Nemoto, K., & Arai, T. (2025) · Nature Communications · doi:10.1038/s41467-025-61292-1 · Cited by 7 · see citing works
Stefanou, E. E., Bitilis, P., Bouchouras, G., & Kotis, K. (2025) · Applied Sciences · doi:10.3390/app151910541 · Cited by 7 · see citing works
Megaritis, D., Alcock, L., Scott, K., Hiden, H., Cereatti, A., Vogiatzis, I., & Din, S. D. (2025) · Bioengineering · doi:10.3390/bioengineering12101108 · Cited by 5 · see citing works
Self-citationPost, E., van Laarhoven, T., Raykov, Y. P., Little, M. A., Nonnekes, J. H., Heskes, T., Bloem, B. R., & Evers, L. J. W. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01578-z · Cited by 4 · see citing works
Gao, S., Zhou, X., Koffman, L. J., Wanigatunga, A. A., Schrack, J. A., Crainiceanu, C. M., & Muschelli, J. (2025) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaf034 · Cited by 4 · see citing works
Phase-Specific Gait Characterization and Plantar Load Progression Analysis Using Smart Insoles
Nanayakkara, T., Herath, H. M. K. K. M. B., Malekroodi, H. S., Madusanka, N., Yi, M., & Lee, B.-I. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-3141-7_18 · Cited by 3 · see citing works
Brand, Y. E., Buchman, A. S., Kluge, F., Palmerini, L., Becker, C., Cereatti, A., Maetzler, W., Vereijken, B., Yarnall, A. J., Rochester, L., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02528-2 · Cited by 4 · see citing works
Self-citationMartínez-Cid, S., Herrera, V., Schez-Sobrino, S., Monekosso, D., Glez-Morcillo, C., & Vallejo, D. (2025) · IEEE Access · doi:10.1109/access.2025.3626013 · Cited by 3 · see citing works
Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers
Lorenzen, N. R., Jennum, P. J., Mignot, E., & Brink-Kjaer, A. (2025) · 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252605 · Cited by 2 · see citing works
Toward a Foundation Model for Healthcare Applications on Single-Camera Gait Analysis
Yamada, Y., Kobayashi, M., Nemoto, M., Ota, M., Nemoto, K., & Arai, T. (2025) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh67620.2025.00011 · Cited by 1 · see citing works
Real-World Gait Speed Estimation: An AI-Based Approach for Adaptive Wearable Devices Integration
Zanoletti, M., Vallati, C., Carbonaro, N., Greco, A., Tognetti, A., & Laurino, M. (2025) · IEEE Access · doi:10.1109/access.2025.3648857 · Cited by 1 · see citing works
From measurement to meaning: why real-world mobility requires more than sensors
Jansen, C.-P., & Klenk, J. (2025) · Age and Ageing · doi:10.1093/ageing/afaf381 · Cited by 1 · see citing works
A digital mirror of frailty? What daily-life mobility measures add
Hausdorff, J. M., Mirelman, A., & Becker, C. (2026) · Age and Ageing · doi:10.1093/ageing/afag043 · Cited by 1 · see citing works
Self-citationGreco, G., Petrelli, A. C., Poli, L., Fischetti, F., & Cataldi, S. (2026) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk11010106 · Cited by 2 · see citing works
Dharmansyah, D., Rahayuwati, L., Pramukti, I., & Mutyara, K. (2026) · Gerontology · doi:10.1159/ger/acxag001 · Cited by 1 · see citing works
Urkude, G., Misal, A. K., Choubey, A., & Choubey, S. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11809-y
Personalized Robotic Achilles Tendon Utilizing a Semi-Passive Spring with Switching Stiffness *
Seong, M., Heo, H., Lee, H., & Choi, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245843
Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics
Yang, Y., Wang, H., Li, E., Zheng, Y., Chen, Y., & Wu, X. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1799845
- Authors
- Brand, Y. E., Kluge, F., Palmerini, L., Paraschiv-Ionescu, A., Becker, C., Cereatti, A., Maetzler, W., Sharrack, B., Vereijken, B., Yarnall, A. J., Rochester, L., Del Din, S., Muller, A., Buchman, A. S., Hausdorff, J. M., & Perlman, O.
- Affiliations
- Tel Aviv University (IL)
- Novartis (Switzerland) (CH)
- University of Bologna (IT)
- École Polytechnique Fédérale de Lausanne (CH)
- University Hospital Heidelberg (DE)
- Politecnico di Torino (IT)
- University Hospital Schleswig-Holstein (DE)
- Sheffield Teaching Hospitals NHS Foundation Trust (GB)
- Norwegian University of Science and Technology (NO)
- Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
- Rush University Medical Center (US)
- Corresponding author
- Perlman, Or
- ORCID
- Venue
- Scientific Reports
- Published
- 2024-09-06
- Volume
- 14
- Issue
- 1
- Pages
- 20854-20854
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11379690
- OpenAlex
- W4402307918
- Cited by
- 19
- References
- 58
- Citations by year
- 2026: 6 · 2025: 13
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PORTO@Iris (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- MACAU: Open Access Repository of Kiel University (University Library Kiel) (submittedVersion, cc-by)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- National Institutes of Health (R01AG017917, R01AG056352, R01AG79133, R01AT012228)
- Innovative Medicines Initiative (820820)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- gait detection, self-supervised learning, wrist-worn accelerometers, older adults, gait disturbance, Parkinson's disease, activities of daily living, walking time, mobility impairment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
