1. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3. doi: 10.1016/j.sleh.2014.12.010 29073412
2. Laposky AD, Van Cauter E, Diez-Roux AV. Reducing health disparities: the role of sleep deficiency and sleep disorders. Sleep medicine. 2016;18:3–6.
3. Kanagasabai T, Chaput J-P. Sleep duration and the associated cardiometabolic risk scores in adults. Sleep Health. 2017;3(3):195–203. doi: 10.1016/j.sleh.2017.03.006 28526258
4. Hennig T, Krkovic K, Lincoln TM. What predicts inattention in adolescents? An experience-sampling study comparing chronotype, subjective, and objective sleep parameters. Sleep medicine. 2017;38:58–63. doi: 10.1016/j.sleep.2017.07.009 29031757
5. Marshall NS, Wong KK, Cullen SR, Knuiman MW, Grunstein RR. Sleep apnea and 20-year follow-up for all-cause mortality, stroke, and cancer incidence and mortality in the Busselton Health Study cohort. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine. 2014;10(4):355.
6. Behar J, Roebuck A, Domingos JS, Gederi E, Clifford GD. A review of current sleep screening applications for smartphones. Physiological measurement. 2013;34(7):R29. doi: 10.1088/0967-3334/34/7/R29 23771214
7. Ko P-RT, Kientz JA, Choe EK, Kay M, Landis CA, Watson NF. Consumer sleep technologies: a review of the landscape. Journal of clinical sleep medicine. 2015;11(12):1455–61. doi: 10.5664/jcsm.5288 26156958
8. de Zambotti M, Baker FC, Colrain IM. Validation of sleep-tracking technology compared with polysomnography in adolescents. Sleep. 2015;38(9):1461–8. doi: 10.5665/sleep.4990 26158896
9. Gautam A, Naik VS, Gupta A, Sharma S, Sriram K, editors. An smartphone-based algorithm to measure and model quantity of sleep. Communication Systems and Networks (COMSNETS), 2015 7th International Conference on; 2015: IEEE.
10. Landry GJ, Best JR, Liu-Ambrose T. Measuring sleep quality in older adults: a comparison using subjective and objective methods. Frontiers in aging neuroscience. 2015;7:166. doi: 10.3389/fnagi.2015.00166 26441633
11. Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, Colantonio A. The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis. Sleep medicine reviews. 2016;25:52–73. doi: 10.1016/j.smrv.2015.01.009 26163057
12. Sano A, Phillips AJ, Amy ZY, McHill AW, Taylor S, Jaques N, et al., editors. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN); 2015: IEEE.
13. Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, et al. Sleep quality prediction from wearable data using deep learning. JMIR mHealth and uHealth. 2016;4(4):e125. doi: 10.2196/mhealth.6562 27815231
14. Min J-K, Doryab A, Wiese J, Amini S, Zimmerman J, Hong JI, editors. Toss'n'turn: smartphone as sleep and sleep quality detector. Proceedings of the SIGCHI conference on human factors in computing systems; 2014: ACM.
15. Matsumoto D, Hwang HC, Frank MG. The body: Postures, gait, proxemics, and haptics. 2016.
16. Kramer RS, Gottwald VM, Dixon TA, Ward R. Different cues of personality and health from the face and gait of women. Evolutionary Psychology. 2012;10(2):147470491201000208.
17. Sun B, Zhang Z, Liu X, Hu B, Zhu T. Self-esteem recognition based on gait pattern using Kinect. Gait & posture. 2017;58:428–32.
18. Goldman SE, Stone KL, Ancoli-Israel S, Blackwell T, Ewing SK, Boudreau R, et al. Poor sleep is associated with poorer physical performance and greater functional limitations in older women. SLEEP-NEW YORK THEN WESTCHESTER-. 2007;30(10):1317.
19. Agmon M, Shochat T, Kizony R. Sleep quality is associated with walking under dual-task, but not single-task performance. Gait & posture. 2016;49:127–31.
20. O’Dowd S, Galna B, Morris R, Lawson RA, McDonald C, Yarnall AJ, et al. Poor sleep quality and progression of gait impairment in an incident Parkinson’s disease cohort. Journal of Parkinson's disease. 2017;7(3):465–70. doi: 10.3233/JPD-161062 28671141
21. Hori H, Ikenouchi-Sugita A, Yoshimura R, Nakamura J. Does subjective sleep quality improve by a walking intervention? A real-world study in a Japanese workplace. BMJ open. 2016;6(10):e011055. doi: 10.1136/bmjopen-2016-011055 27797982
22. Markwald RR, Melanson EL, Smith MR, Higgins J, Perreault L, Eckel RH, et al. Impact of insufficient sleep on total daily energy expenditure, food intake, and weight gain. Proceedings of the National Academy of Sciences. 2013;110(14):5695–700.
23. Waters RL, Mulroy S. The energy expenditure of normal and pathologic gait. Gait & posture. 1999;9(3):207–31.
24. Li S, Cui L, Zhu C, Li B, Zhao N, Zhu T. Emotion recognition using Kinect motion capture data of human gaits. PeerJ. 2016;4:e2364. doi: 10.7717/peerj.2364 27672492
25. Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. Gait & posture. 2014;39(4):1062–8.
26. Chang C-Y, Lange B, Zhang M, Koenig S, Requejo P, Somboon N, et al., editors. Towards pervasive physical rehabilitation using Microsoft Kinect. 2012 6th international conference on pervasive computing technologies for healthcare (PervasiveHealth) and workshops; 2012: IEEE.
27. Chang Y-J, Chen S-F, Huang J-D. A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in developmental disabilities. 2011;32(6):2566–70. doi: 10.1016/j.ridd.2011.07.002 21784612
28. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research. 1989;28(2):193–213. doi: 10.1016/0165-1781(89)90047-4 2748771
29. Gwosdek P, Grewenig S, Bruhn A, Weickert J, editors. Theoretical foundations of gaussian convolution by extended box filtering. International Conference on Scale Space and Variational Methods in Computer Vision; 2011: Springer.
30. Van Loan C. Computational frameworks for the fast Fourier transform: Siam; 1992.
31. Kumar GG, Sahoo SK, Meher PK. 50 Years of FFT Algorithms and Applications. Circuits, Systems, and Signal Processing. 2019:1–34.
32. Weisstein EW. Fast fourier transform. 2015.
33. Schneider M, Merkert D, Kabel M. FFT‐based homogenization for microstructures discretized by linear hexahedral elements. International Journal for Numerical Methods in Engineering. 2017;109(10):1461–89.
34. Pearson correlation coefficient. Available from: https://en.wikipedia.org/wiki/Correlation_coefficient.
35. p value. Available from: https://en.wikipedia.org/wiki/P-value
36. T-test. https://en.wikipedia.org/wiki/Student%27s_t-test
37. Cawley GC, Talbot NL. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research. 2010;11(Jul):2079–107.
38. Mukaka MM. A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal. 2012;24(3):69–71. 23638278
39. Nixon MS, Carter JN. Automatic recognition by gait. Proceedings of the IEEE. 2006;94(11):2013–24.
40. Ayaki M, Negishi K, Tsubota K. Rejuvenation effects of cataract surgery with clear intra-ocular lens implantation on gait speed, sleep quality, and metabolic parameters. Investigative Ophthalmology & Visual Science. 2014;55(13):2540-.
41. Ayaki M, Muramatsu M, Negishi K, Tsubota K. Improvements in sleep quality and gait speed after cataract surgery. Rejuvenation research. 2013;16(1):35–42. doi: 10.1089/rej.2012.1369 23145881