JOURNAL ARTICLE

2025

• [Q1] Hoang NH, Liang Z. (2025) Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpO2 Signals: A Multi-Scale Feature Approach. Sensors  25(6), 1698. DOI: 10.3390/s25061698. [PubMed/SCI/Scopus]【First author is PhD student】

• [Q1] Hoang NH, Liang Z. (2025) AI-driven sleep apnea screening with overnight blood oxygen saturation: current practices and future directions. Front. Digit. Health - Health Informatics. [PubMed/SCI/Scopus]【First author is PhD student】

• [Q1] Saskovets M, Saponkova I, Liang Z. (2025) Effects of Sound Interventions on the Mental Stress Response in Adults: A Scoping Review. JMIR Mental Health. 22/11/2024:69120. DOI: 10.2196/preprints.69120.  [SCI/Scopus/PubMed]【First author is PhD student】

2024

• [Q1] ​Priyadarshana YHPP, Senanayake A, Liang Z, Piumarta I. (2024) Prompt Engineering for Digital Mental Health: A Short Review. Frontiers in Digital Health - Digital Mental Health 6:1410947. Doi: 10.3389/fdgth.2024.1410947.  [SCI/Scopus/PubMed]【First author is PhD student】

• Saskovets M, Liang Z, Piumarta I, Saponkova I. (2024) Effects of Sound Interventions on the Mental Stress Response in Adults:  Protocol for A Scoping Review. JMIR Research Protocols 13:e54030. Doi:10.2196/54030. [SCI/Scopus/PubMed]【First author is PhD student】

• [Q1] Liang Z, Melcer EF, Khotchasing K, Chen S, Hwang D, Hoang NH. (2024) The Role of Relevance in Shaping Perceptions of Sleep Hygiene Games Among University Students: Mixed Methods Study. JMIR Serious Games. Doi: 22/09/2024:64063. [SCI/Scopus/PubMed]  

• [Q1] ​Liang Z, Melcer E, Khotchasing K, Hoang NH. (2024) Co-design Personal Sleep Health Technology for and with University Students. Front. Digit. Health - Human Factors and Digital Health 6:1371808. Doi: 10.3389/fdgth.2024.1371808.  [SCI/Scopus/PubMed]  

• [Q1] ​ Liang Z. (2024) Developing Probabilistic Ensemble Machine Learning Models for Home-Based Sleep Apnea Screening using Overnight SpO2 Data at Varying Data Granularity. Sleep and Breathing.  Doi: 10.1007/s11325-024-03141-x. [SCI/Scopus/PubMed] 

• [Q2] ​Liang Z. (2024) More Haste, Less Speed?: Relationship between Response Time and Response Accuracy in Gamified Online Quizzes in an Undergraduate Engineering Course. Front. Educ. - Higher Education, 9. Doi: 10.3389/feduc.2024.1412954.  [SCI/Scopus] 

2023

• [Q1] ​​Nhung, H. H., Liang Z. (2023) Knowledge discovery in ubiquitous and personal sleep-tracking: a scoping review. JMIR mHealth and uHealth 11:e42750 [PubMed/SCI/Scopus]【First author is master student】

• [Q1] Liang Z. (2023) Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening. Digital Health 9: 1-19. [PubMed/SCI/Scopus]  

• [Q1] Ploderer B, Rodgers S, Liang Z. (2023) What’s keeping teens up at night? Reflecting on sleep and technology habits with teens. Personal and Ubiquitous Computing, 27, 249-270.  [SCI/Scopus] 

• Sirithummarak P, Liang Z. (2023) Developing a Cross-Platform Application for Integrating Real-time Time-series Data from Multiple Wearable Sensors. Engineering Proceedings 58(1):4. https://doi.org/10.3390/ecsa-10-16185. [Scopus]【First author is master student】

• Liang Z. (2023) Developing and Validating Ensemble Classifiers for At-Home Sleep Apnea Screening. Engineering Proceedings 58(1):49. https://doi.org/10.3390/ecsa-10-16184.  [Scopus] 

2022

• [Q2] ​Liang Z. (2022). Context-aware sleep health recommender systems (CASHRS): a narrative review.  Electronics 2022, 11(20), 3384. Doi: 10.3390/electronics1120338. [SCI/Scopus]

• [Q1] ​Liang Z. (2022). Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults. Frontiers in Medical Technology (Section: Medtech Data Analytics).  [PubMed/SCI/Scopus]

2021

• [Q1] ​Liang Z. (2021) What does sleeping brain tell about stress? A pilot fNIRS study into stress-related cortical hemodynamic features during sleep. Frontiers in Computer Science (Section: Mobile and Ubiquitous Computing) 3:774949. Doi: 10.3389/fcomp.2021.774949. [SCI /Scopus]  

• [Q1] ​Liang Z, Chapa-Martell MA. (2021) A multi-level classification approach for sleep stage prediction with processed data derived from consumer wearable activity trackers. Frontiers in Digital Health (Section: Health Informatics) 3:665946. Doi: 10.3389/fdgth.2021.665946.  [PubMed /Scopus]  

• Bertrand L, Cleyet-Marrel N, Liang Z. (2021) Recognizing eating activities in free-living environment using consumer wearable devices. Engineering Proceedings 6(1): 58. Doi:10.3390/I3S2021Dresden-10141. 【First authors are visiting master students】

2020

• [Q1] Liang Z, Ploderer B. (2020) “How does Fitbit measure brainwaves”: a qualitative study into the credibility of sleep-tracking technologies. PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 4(1):Article 17. [SCI/Scopus] 

2019

• [Q1] Liang Z, Chapa-Martell MA. (2019) Accuracy of Fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors. JMIR mHealth and uHealth 7(6):e13384, DOI:10.2196/13384. [PubMed/SCI/Scopus]  # Featured in Techheading

• [Q2] Liang Z, Chapa-Martell MA. (2019) Not all errors are created equal: influence of user characteristics on measuring errors of consumer wearable devices for sleep tracking. EAI Endorsed Transactions on Pervasive Health and Technology 18(15):e4.[SCI/Scopus] 

• [Q2] Liang Z, Yoshida Y, Iino N, Nishimura T, Chapa-Martell MA, Nishimura S.(2019) A pervasive sensing approach to automatic assessment of trunk coordination using mobile devices. EAI Endorsed Transactions on Pervasive Health and Technology 18(15):e5.  [SCI/Scopus] 

• Liang Z, Chapa-Martell MA. (2019) Measurement accuracy of consumer sleep tracking wristbands is associated to users’ age and sleep efficiency. The Journal of Physical Fitness and Sports Medicine 8(6):394.​

2018

• [Q1] Liang Z, Chapa-Martell MA. (2018) Validity of consumer activity wristbands and wearable EEG for measuring overall sleep parameters and sleep structure in free-living conditions. Journal of Healthcare Informatics Research 2 (1-2): 152-178. [PubMed/SCI/Scopus]  #Cited by UK Parliamentary Office of Sciences & Technology # Featured in Gizmodo

• Yoshida Y, Liang Z, Nishimura S, Konosu H, Nagao T, Nishimura T. (2018) Quality evaluation for sports coaching service: evaluate trunk torsion by mobile terminal. Transaction of Information Processing Society of Japan 59(2): 591-601. 

2016

• [Q1] Liang Z, Ploderer B, Liu W, Nagata Y, Bailey J, Kulik L, Li Y. (2016). SleepExplorer: A visualization tool to make sense of correlations between personal sleep data and contextual factors. Personal and Ubiquitous Computing 20(6): 985-1000. [SCI/Scopus]