JOURNAL ARTICLE

2023

Nhung, H. H., Liang Z. (2023) Knowledge discovery in ubiquitous and personal sleep-tracking: a scoping review. JMIR mHealth and uHealth 11:e42750. (Impact factor: 5.65, Q1) [PubMed/SCI/Scopus]【First author is master student】
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. (Impact factor: 3.06, Q1) [SCI/Scopus]

Liang Z. (2022). Context-aware sleep health recommender systems (CASHRS): a narrative review. Electronics 2022, 11(20), 3384. Doi: 10.3390/electronics1120338 (Impact factor: 2.69, Q2) [SCI/Scopus]
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]

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]
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. (Impact factor: 2.20, Q2) [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】

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. (Impact factor: 4.16, Q1) [SCI/Scopus]

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. (Impact Factor: 5.65, Q1) [PubMed/SCI/Scopus] # Featured in Techheading
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. (Impact Factor: 1.68, Q3) [SCI/Scopus]
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. (Impact Factor: 1.68, Q3) [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.

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. (Impact Factor: 3.28, Q1) [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.

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. (Impact factor: 3.06, Q1) [SCI/Scopus]