| Validity of consumer activity wristbands and wearable EEG for measuring overall sleep parameters and sleep structure in free-living conditions Z Liang, MA Chapa-Martell Journal of Healthcare Informatics Research 2 (1-2), 152-178, 2018 | 76 | 2018 |
| SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors Z Liang, B Ploderer, W Liu, Y Nagata, J Bailey, L Kulik, Y Li Personal and Ubiquitous Computing 20 (6), 985-1000, 2016 | 75 | 2016 |
| Real-time urban traffic amount prediction models for dynamic route guidance systems Z Liang, Y Wakahara EURASIP Journal on Wireless Communications and Networking 2014 (1), 1-13, 2014 | 60 | 2014 |
| Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and The Effect of User-Specific Factors Z Liang, MA Chapa-Martell JMIR mHealth uHealth, 2019 | 47 | 2019 |
| Sleep tracking in the real world: a qualitative study into barriers for improving sleep Z Liang, B Ploderer Proceedings of the 28th Australian Conference on Computer-Human Interaction …, 2016 | 41 | 2016 |
| City traffic prediction based on real-time traffic information for intelligent transport systems Z Liang, Y Wakahara 2013 13th International Conference on ITS Telecommunications (ITST), 378-383, 2013 | 27 | 2013 |
| Antioxidant action and protective and reparative effects of lentinan on oxidative damage in HaCaT cells Y Zi, B Zhang, B Jiang, X Yang, Z Liang, W Liu, C He, L Liu Journal of Cosmetic Dermatology 17 (6), 1108-1114, 2018 | 25 | 2018 |
| Framing Self-quantification for Individual-level Preventive Health Care Z Liang, MA Chapa-Martell HEALTHINF 2015, 336-343, 2015 | 21 | 2015 |
| A personalized approach for detecting unusual sleep from time series sleep-tracking data Z Liang, MAC Martell, T Nishimura 2016 IEEE International Conference on Healthcare Informatics (ICHI), 18-23, 2016 | 20 | 2016 |
| Is Fitbit fit for sleep-tracking? Sources of measurement errors and proposed countermeasures Z Liang, B Ploderer, MA Chapa-Martell Proceedings of the 11th EAI International Conference on Pervasive Computing …, 2017 | 18 | 2017 |
| Mining hidden correlations between sleep and lifestyle factors from quantified-self data Z Liang, MAC Martell, T Nishimura Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 16 | 2016 |
| Washing away your sins in the brain: physical cleaning and priming of cleaning recruit different brain networks after moral threat H Tang, X Lu, R Su, Z Liang, X Mai, C Liu Social cognitive and affective neuroscience 12 (7), 1149-1158, 2017 | 15 | 2017 |
| A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining Z Liang, B Ploderer, MAC Martell, T Nishimura International Symposium on Intelligent Computing Systems, 83-96, 2016 | 14 | 2016 |
| Combining resampling and machine learning to improve sleep-wake detection of Fitbit wristbands Z Liang, MA Chapa-Martell 2019 IEEE International Conference on Healthcare Informatics (ICHI), 1-3, 2019 | 13 | 2019 |
| Pro-reactive route recovery with automatic route shortening in wireless ad hoc networks Z Liang, Y Taenaka, T Ogawa, Y Wakahara 2011 Tenth International Symposium on Autonomous Decentralized Systems, 57-64, 2011 | 13 | 2011 |
| A Quantified-Self Framework for Exploring and Enhancing Personal Productivity G White, Z Liang, S Clarke Proc of CBMI 19, 2019 | 12 | 2019 |
| Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices Z Liang, T Nishimura 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), 1-5, 2017 | 12 | 2017 |
| A route guidance system with personalized rerouting for reducing traveling time of vehicles in urban areas Z Liang, Y Wakahara 17th International IEEE Conference on Intelligent Transportation Systems …, 2014 | 11 | 2014 |
| "How does Fitbit measure brainwaves": a qualitative study into the credibility of sleep-tracking technologies Z Liang, B Ploderer Proc ACM Interact Mob Wearable Ubiquitous Technol (IMWUT) 4 (1), Article 17, 2020 | 10 | 2020 |
| A Multi-level Classification Approach for Sleep Stage Prediction with Processed Data Derived from Consumer Wearable Activity Trackers Z Liang, MA Chapa-Martell Frontiers in Digital Health 3, 49, 2021 | 9 | 2021 |
Takuichi NishimuraNational Institute of Advanced Industrial Science and Technology (AIST)Потвърден имейл адрес: ni.aist.go.jp
