Monitoring sleep with a smartphone accelerometer

STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT

Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works

Akufo and Ibarapa

Beckett, A. H., Rowland, M., & TURNER, P. (1965) · The Lancet · doi:10.1016/s0140-6736(65)91037-8 · Cited by 16,917 · see citing works

Epidemiology of insomnia: what we know and what we still need to learn

Ohayon, M. M. (2002) · Sleep Medicine Reviews · doi:10.1053/smrv.2002.0186 · Cited by 3,743 · see citing works

Automatic sleep/wake identification from wrist activity.

Cole, R., Kripke, D., Gruen, W., Mullaney, D., & Gillin, J. (1992) · Sleep · doi:10.1093/sleep/15.5.461 · Cited by 1,971 · see citing works

Psychiatric Illness in General Practice: A Detailed Study Using a New Method of Case Identification

Goldberg, D. P., & Blackwell, B. (1970) · BMJ · doi:10.1136/bmj.2.5707.439 · Cited by 1,121 · see citing works

Insomnia and health-related quality of life

Kyle, S. D., Morgan, K., & Espie, C. A. (2010) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2009.07.004 · Cited by 520 · see citing works

Actigraphy Validation with Insomnia

Lichstein, K. L., Stone, K. C., James Donaldson, R., Nau, S. D., Soeffing, J. P., Murray, D. M., Lester, K. W., & Aguillard, R. N. (2006) · SLEEP · doi:10.1093/sleep/29.2.232 · Cited by 502 · see citing works

Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography - a systematic review

van de Water, A. T. M., HOLMES, A., & Hurley, D. A. (2010) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2009.00814.x · Cited by 454 · see citing works

Direct Comparison of Two New Actigraphs and Polysomnography in Children and Adolescents

Meltzer, L. J., Walsh, C. M., Traylor, J. T., & Westin, A. M. L. (2011) · SLEEP · doi:10.5665/sleep.1608 · Cited by 339 · see citing works

Accuracy of sleep perceptions among insomnia sufferers and normal sleepers

Means, M. (2003) · Sleep Medicine · doi:10.1016/s1389-9457(03)00057-1 · Cited by 217 · see citing works

Actigraphy in the Assessment of Insomnia: A Quantitative Approach

Natale, V., Plazzi, G., & Martoni, M. (2009) · SLEEP · doi:10.1093/sleep/32.6.767 · Cited by 183 · see citing works

Wrist actigraphic scoring for sleep laboratory patients: algorithm development

Kripke, D. F., Hahn, E. K., Grizas, A. P., WADIAK, K. H., Loving, R. T., Poceta, J. S., Shadan, F. F., CRONIN, J. W., & Kline, L. E. (2010) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2010.00835.x · Cited by 103 · see citing works

Comparison of Two Different Actigraphs with Polysomnography in Healthy Young Subjects

Tonetti, L., Pasquini, F., Fabbri, M., Belluzzi, M., & Natale, V. (2008) · Chronobiology International · doi:10.1080/07420520801897228 · Cited by 103 · see citing works

Evaluation of immobility time for sleep latency in actigraphy

Chae, K. Y., Kripke, D. F., Poceta, J. S., Shadan, F. F., Jamil, S. M., CRONIN, J. W., & Kline, L. E. (2008) · Sleep Medicine · doi:10.1016/j.sleep.2008.07.009 · Cited by 96 · see citing works

Validity of a Short Insomnia Questionnaire: the SDQ

Violani, C., Devoto, A., Lucidi, F., Lombardo, C., & Russo, P. M. (2004) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2003.06.002 · Cited by 88 · see citing works

Smartphone Sensors for Health Monitoring and Diagnosis

Majumder, S., & Deen, M. J. (2019) · Sensors · doi:10.3390/s19092164 · Cited by 440 · see citing works

Systematic review of smartphone-based passive sensing for health and wellbeing

Cornet, V. P., & Holden, R. J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.12.008 · Cited by 406 · see citing works

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works

A Review and Taxonomy of Activity Recognition on Mobile Phones

İncel, Ö. D., Köse, M., & Ersoy, C. (2013) · BioNanoScience · doi:10.1007/s12668-013-0088-3 · Cited by 234 · see citing works

Consumer sleep tracking devices: a review of mechanisms, validity and utility

Kolla, B. P., Mansukhani, S., & Mansukhani, M. P. (2016) · Expert Review of Medical Devices · doi:10.1586/17434440.2016.1171708 · Cited by 158 · see citing works

Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios

De Arriba-Pérez, F., Caeiro-Rodríguez, M., & Santos-Gago, J. (2016) · Sensors · doi:10.3390/s16091538 · Cited by 153 · see citing works

A review of current sleep screening applications for smartphones

Behar, J. A., Roebuck, A., Domingos, J. S., Gederi, E., & Clifford, G. D. (2013) · Physiological Measurement · doi:10.1088/0967-3334/34/7/r29 · Cited by 130 · see citing works

Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

Bhat, S., Ferraris, A., Gupta, D., Mozafarian, M., DeBari, V. A., Gushway-Henry, N., Gowda, S. P., Polos, P. G., Rubinstein, M., Seidu, H., et al. (2015) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.4840 · Cited by 130 · see citing works

Resilience Training for Work-Related Stress Among Health Care Workers: Results of a Randomized Clinical Trial Comparing In-Person and Smartphone-Delivered Interventions

Mistretta, E. G., Davis, M. C., Temkit, M., Lorenz, C., Darby, B., & Stonnington, C. M. (2018) · Journal of Occupational & Environmental Medicine · doi:10.1097/jom.0000000000001285 · Cited by 114 · see citing works

Smartwatch: Performance evaluation for long-term heart rate monitoring

Phan, D. H., Lee, Y. S., Pathirana, P. N., & Seneviratne, A. P. (2015) · International Symposium on Bioelectronics and Bioinformatics (ISBB) · doi:10.1109/isbb.2015.7344944 · Cited by 100 · see citing works

The role of actigraphy in the assessment of primary insomnia: a retrospective study

Natale, V., Léger, D., Martoni, M., Bayon, V., & Erbacci, A. (2013) · Sleep Medicine · doi:10.1016/j.sleep.2013.08.792 · Cited by 98 · see citing works

SleepCoacher

Daskalova, N., Metaxa, D., Tran, A., Nugent, N. R., Boergers, J., McGeary, J. E., & Huang, J. J. S. (2016) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2984511.2984534 · Cited by 89 · see citing works

Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence

Fino, E., & Mazzetti, M. (2018) · Sleep And Breathing · doi:10.1007/s11325-018-1661-3 · Cited by 87 · see citing works

Promises and Challenges in the Use of Consumer-Grade Devices for Sleep Monitoring

Roomkham, S., Lovell, D. R., Cheung, J., & Perrin, D. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2811735 · Cited by 84 · see citing works

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks

Sano, A., Chen, W. '., Lopez-Martinez, D., Taylor, S., & Picard, R. W. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2867619 · Cited by 71 · see citing works

Challenges and Emerging Technologies within the Field of Pediatric Actigraphy

Galland, B. C., Meredith-Jones, K., Terrill, P. I., & Taylor, R. W. (2014) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2014.00099 · Cited by 69 · see citing works

SenseHAR

Jeyakumar, J. V., Lai, L., Suda, N., & Srivastava, M. B. (2019) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3356250.3360032 · Cited by 56 · see citing works

Accuracy of a smartphone application in estimating sleep in children

Patel, P. D., Kim, J. Y., & Brooks, L. J. (2016) · Sleep And Breathing · doi:10.1007/s11325-016-1425-x · Cited by 55 · see citing works

Comparison between paper and electronic sleep diary

Tonetti, L., Mingozzi, R., & Natale, V. (2016) · Biological Rhythm Research · doi:10.1080/09291016.2016.1191689 · Cited by 51 · see citing works

mHealth Technologies Towards Parkinson's Disease Detection and Monitoring in Daily Life: A Comprehensive Review

Zhang, H., Song, C., Rathore, A. S., Huang, M.-C., Zhang, Y., & Xu, W. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2991813 · Cited by 48 · see citing works

Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review

O’Mahony, A. M., Garvey, J. F., & McNicholas, W. T. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd-sleep-2020-003 · Cited by 45 · see citing works

(Not so) Smart sleep tracking through the phone: Findings from a polysomnography study testing the reliability of four sleep applications

Fino, E., Plazzi, G., Filardi, M., Marzocchi, M., Pizza, F., Vandi, S., & Mazzetti, M. (2019) · Journal of Sleep Research · doi:10.1111/jsr.12935 · Cited by 44 · see citing works

Elevator ride comfort monitoring and evaluation using smartphones

Zhang, Y., Sun, X., Zhao, X., & Su, W. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.12.005 · Cited by 37 · see citing works

How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators

de Arriba-Pérez, F., Rodríguez, M. C., & Santos-Gago, J. M. (2017) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0477-5 · Cited by 36 · see citing works

Current and Future Roles of Consumer Sleep Technologies in Sleep Medicine

Goldstein, C. A. (2020) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2020.05.001 · Cited by 34 · see citing works

Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study

Taeger, J., Bischoff, S., Hagen, R., & ten Rak, K. (2020) · JMIR mhealth and uhealth · doi:10.2196/19346 · Cited by 29 · see citing works

Feasibility and acceptability of wrist actigraph in assessing sleep quality and sleep quantity: A home-based pilot study in healthy volunteers

Noor, Z. M., Smith, A. J., Smith, S. S., & Nissen, L. M. (2013) · Health · doi:10.4236/health.2013.58a2010 · Cited by 26 · see citing works

Sleep apps

Lorenz, C., & Williams, A. J. (2017) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000000425 · Cited by 24 · see citing works

Towards the use of commercial wrist wearables in education

de Arriba-Pérez, F., Rodríguez, M. C., & Santos-Gago, J. M. (2017) · Experiment@International Conference · doi:10.1109/expat.2017.7984354 · Cited by 23 · see citing works

Sleep, Prospective Memory, and Immune Status among People Living with HIV

Faraut, B., Tonetti, L., Malmartel, A., Grabar, S., Ghosn, J., Viard, J., Natale, V., & Léger, D. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18020438 · Cited by 22 · see citing works

Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study

Niemeijer, K., Mestdagh, M., & Kuppens, P. (2021) · Journal of Medical Internet Research · doi:10.2196/25643 · Cited by 21 · see citing works

Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique

Ammae, O., Korpela, J., & Maekawa, T. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.03.022 · Cited by 20 · see citing works

An Exploratory Study of AI and Big Data, and It's Future in the United StatesPreprint

Surya, L. (2015) · SSRN Electronic Journal · Cited by 18 · see citing works

Automatic Tracking of Behavior With Smartphones: Potential for Behavior Change Interventions

Piwek, L., & Joinson, A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802690-8.00005-0 · Cited by 15 · see citing works

Feasibility Study for Unconstrained Respiration Monitoring Based on Multi-Way Approach Using an Acceleration and Force Sensing Module

Park, S., & Shin, H. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2695565 · Cited by 15 · see citing works

A survey of people-centric sensing studies utilizing mobile phone sensors

Bayındır, L. (2017) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-170446 · Cited by 15 · see citing works

Sleep Applications to Assess Sleep Quality

Fietze, I. (2016) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2016.08.008 · Cited by 15 · see citing works

The effect of window length on accuracy of smartphone-based activity recognition.

Bashir, S. A., Doolan, D. C., & Petrovski, A. (2016) · BCU Open Access Repository (Birmingham City University) · Cited by 11 · see citing works

Sleep Sound Classification Using ANC-Enabled Earbuds

Christofferson, K., Chen, X., Wang, Z., Mariakakis, A. T., & Wang, Y. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767394 · Cited by 9 · see citing works

Sleep Tracking Apps’ Design Choices: A Review

Hosszu, A., Rosner, D. E., & Flaherty, M. (2019) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs.2019.00078 · Cited by 8 · see citing works

Smartphone-based sensors in health and wellness monitoring–Perspectives and assessment of the emerging future

Pramanik, H. S., Pal, A., Kirtania, M., Chakravarty, T., & Ghose, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823696-3.00018-0 · Cited by 8 · see citing works

On the Analysis and Classification of Sleep Stages from Cardiorespiratory Activity

Long, X., & Long, X. (2015) · Cited by 8 · see citing works

A new hybrid method for rigid and flexible rotor balancing without phase response measurements

Ahmed, A. A. B., Touache, A., Hakimi, A. E., & Chamat, A. (2020) · Australian Journal of Mechanical Engineering · doi:10.1080/14484846.2020.1842616 · Cited by 8 · see citing works

Health Status Prediction with Local-Global Heterogeneous Behavior Graph

Ma, X., Yang, X., Gao, J., & Xu, C. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3457893 · Cited by 7 · see citing works

Smart Textile Systems for Loneliness Monitoring in Older People Care: A Review of Sensing and Design Innovations

Zhou, Y., Ratcliffe, J., Molteni, E., Patel, A. S., Liu, J., Mexia, N., Rees, J., Matcham, F., Antonelli, M., Tinker, A., et al. (2025) · Advanced Electronic Materials · doi:10.1002/aelm.202500300 · Cited by 6 · see citing works

Relationship Among Macronutrients, Dietary Components, and Objective Sleep Variables Measured by Smartphone Apps: Real-World Cross-Sectional Study

Seol, J., Iwagami, M., Kayamare, M. C. T., & Yanagisawa, M. (2025) · Journal of Medical Internet Research · doi:10.2196/64749 · Cited by 6 · see citing works

Objective measurement of sleep by smartphone application: comparison with actigraphy and relation to self-reported sleep

Maynard, T. R., Appleman, E. R., Cronin‐Golomb, A., & Neargarder, S. A. (2021) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/emed.2021.00057 · Cited by 6 · see citing works

Cognitive infocommunication for monitoring and improving well-being of people

Vágner, A. (2017) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2017.8268224 · Cited by 6 · see citing works

Smartphone Mediated Tracking and Analysis of Sleep Patterns in Indian College Students

Vaghela, M., & Sasidhar, K. (2022) · Human-Centric Intelligent Systems · doi:10.1007/s44230-022-00014-y · Cited by 5 · see citing works

Sleep detection using de-identified depth data

Krüger, B., Vögele, A., Lassiri, M., Herwartz, L., Terkatz, T., Weber⋆, A., García, C., Fietze, I., & Penzel, T. (2014) · Journal of Multimedia · Cited by 5 · see citing works

Relapse prediction: A meteorology-inspired mobile model

Bishop, F. M. (2016) · Health Psychology Open · doi:10.1177/2055102916665934 · Cited by 5 · see citing works

Design and development of a method for detecting sleep roll-over counts using accelerometer ADXL335

Acharya, J. (2019) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v10i1.pp477-485 · Cited by 4 · see citing works

Association of sleep patterns assessed by a smartphone application with work productivity loss among Japanese employees

Seol, J., Iwagami, M., & Yanagisawa, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02155-3 · Cited by 3 · see citing works

Contactless on-bed activity sensing using first-reflection echolocation

Griffith, H., Shi, Y. R., & Biswas, S. K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591832 · Cited by 2 · see citing works

Monitoring sleep quality with non-invasive sensors

Rinta‐Koski, O. (2013) · Aaltodoc (Aalto University) · Cited by 2 · see citing works

Practical Implementation and Integration of AI in Mental Healthcare

Goyal, S. B., & Fiorini, L. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-91147-7_18 · Cited by 1 · see citing works

Temporal changes in sleep parameters and body mass index after using a sleep-tracking app with gamification

Iwagami, M., Seol, J., & Yanagisawa, M. (2024) · Sleep Health · doi:10.1016/j.sleh.2025.03.001 · Cited by 1 · see citing works

Multimodal Sleep Measurement and Alignment Analysis in Outpatients With Major Depressive Episode: Observational Study

Mahir, A., Luong, N., Baryshnikov, I., Martikkala, A., Isometsä, E. T., & Aledavood, T. (2025) · JMIR mhealth and uhealth · doi:10.2196/82465 · Cited by 1 · see citing works

Sleep quality monitoring with ambient and mobile sensing

Kahveci, A. Y., Alemdar, H. O., & Ersoy, C. (2015) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2015.7129871 · Cited by 1 · see citing works

Sleep Detection Using a Depth Camera

Krüger, B., Vögele, A., Herwartz, L., Terkatz, T., Weber⋆, A., García, C., Fietze, I., & Penzel, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09144-0_57 · Cited by 1 · see citing works

Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil

Lemes, L. C., Triches, M. I., Carvalho, C., Cardoso, V. D. F., & Sato, T. D. O. (2024) · Sleep Science · doi:10.1055/s-0044-1779606 · Cited by 1 · see citing works

The potential of personal devices in large-scale sleep studies

Roomkham, S. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.203907 · Cited by 1 · see citing works

Sleep Estimation from Low Frequency Smartphone Sensors via Bayesian Hidden Markov ModelPreprint

Byun, A. J. S., Li, Y., Cong, S., Dhima, A., Wang, S., Flathers, M., & Torous, J. B. (2025) · Research Square · doi:10.21203/rs.3.rs-7217304/v1 · Cited by 1 · see citing works

Konzeption und Realisierung eines Sleep Monitoring Frameworks für iOS

Reiter, A. (2015)

Automatic detection of sleep onset in healthy adults

Le, K. Q., Dinh, A. T. N., Tran, B. H., & Linh, H. Q. (2017) · Science and Technology Development Journal · doi:10.32508/stdj.v20ik3.1080

Obstructive Sleep Apnea Analysis Techniques

Hüzmeli, E. D., Katayıfçı, N., & Hüzmeli̇, İ. (2021) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-8018-9.ch003

The Role of mHealth in Improving Health and Medication Management

(2020) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-3832-6.ch002

Digital Shift: Assessment of Mental States Through Passive Mobile Sensing

Krajchevska, E., Petreska, N., Handjiski, O., Andovska, S., Ilijoski, B., Lameski, P., Ribarski, P., & Tojtovska, B. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-04206-5_15

On Design and Implementation of Health Reminder System Based on Apple iOS Platform

Ge, S., Huang, H., & Liou, J.-C. (2021) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice54393.2021.00118

Personalized Sleep Stage Estimation Based on Time Series Probability of Estimation for Each Label with Wearable 3-Axis Accelerometer

Nakari, I., Nakashima, M., & Takadama, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35132-7_40

Smartphone-Measured Sleep Health Risk Factors Detect Suicidal Thoughts and Behaviors Among Adolescents

Kirshenbaum, J. S., Pagliaccio, D., Trivedi, E., Galfalvy, H., Joyce, K. J., Durham, K., Porta, G., Bloom, P. A., Tse, T., Blanchard, A. M., et al. (2026) · JAACAP Open · doi:10.1016/j.jaacop.2026.06.001

Design and Development of Smart Textile Loneliness Monitoring Systems for Older People

Zhou, Y., Rees, J., & Liu, W. (2025) · International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles (E-Textiles) · doi:10.1109/e-textiles67498.2025.11686519

Objective Measurement of Sleep by Smartphone Application: Comparison with Actigraphy and Relation to Cognition, Mood, and Self-Reported Sleep

Maynard, T. R. (2017) · Journal of international women's studies

Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph

Roomkham, S., Hittle, M., Cheung, J., Lovell, D. R., Mignot, E. J.-M., & Perrin, D. (2019) · F1000Research · doi:10.12688/f1000research.19020.1 · Cited by 31 · see citing works

Sleep detection with an accelerometer actigraph: comparisons with polysomnography

Jean‐Louis, G., Kripke, D. F., Cole, R. J., Assmus, J. D., & Langer, R. D. (2001) · Physiology & Behavior · doi:10.1016/s0031-9384(00)00355-3 · Cited by 267 · see citing works

Comparison of Sleep Features Across Smartphone Sensors, Actigraphy, and Diaries Among Young Adults: Longitudinal Observational Study

Kirshenbaum, J. S., Crowley, R. N., Latham, M. D., Pagliaccio, D., Auerbach, R. P., & Allen, N. B. (2025) · JMIR Formative Research · doi:10.2196/67455 · Cited by 2 · see citing works

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography

Chase, J. D., Busa, M. A., STAUDENMAYER, J. W., & Sirard, J. R. (2022) · Sensors · doi:10.3390/s22135041 · Cited by 29 · see citing works