Skip to main content

Advertisement

SpringerLink
Log in

Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Wearable devices equipped with sensors popularly used for health monitoring are capable of accumulating motion data providing objective measures of various physical activity and sleep attributes. Also, smartphone usage has grown to an extent where phones have become an integral part of lifestyle contributing to users’ screen viewing time. Behavior and behavioral attributes of individuals’ personal characteristics are significant components of lifestyle of which sleep, physical activity and screen viewing correspond to the most occupied time throughout the day and among the major lifestyle patterns affecting overall health. This study aims to assess sleep quality and behavioral health from wearables and smartphones using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Real time physical activity and sleep data have been collected from users’ smartwatches. A smartphone application is built to collect real time smartphone usage data. Sleep quality indicator (SleepQual) for assessment of daily sleep quality is calculated using sleep attributes collected from smartwatches. Correlation of SleepQual is evaluated with physical activity attributes and smartphone usage attributes using Pearson’s correlation. Highly correlated attributes along with sleep attributes are used to train the ANFIS model for sleep quality assessment. A novel behavioral health indicator (B. Health) is proposed which is evaluated using real time physical activity, sleep and screen time data. Attributes are ranked on the basis of Pearson’s correlation with B. Health to identify the most important contributors to behavioral health. Top ranked features are selected to train the ANFIS model for behavioral health assessment. Systematic Minority Oversampling Technique has been used for data augmentation. The ANFIS model achieves an accuracy of 91.69% for sleep quality assessment and 85.79% for behavioral health assessment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Mak, Y.W.; Wu, C.S.T.; Hui, D.W.S.; Lam, S.P.; Tse, H.Y.; Yu, W.Y.; Wong, H.T.: Association between screen viewing duration and sleep duration, sleep quality, and excessive daytime sleepiness among adolescents in Hong Kong. Int. J. Environ. Res. Public Health 11(11), 11201–11219 (2014)

    Article  Google Scholar 

  2. Holmberg, L.I.; Hellberg, D.: Behavioral and other characteristics of relevance for health in adolescents with self-perceived sleeping problems. Int. J. Adolesc. Med. Health 20(3), 353–365 (2008)

    Article  Google Scholar 

  3. Reilly, J.J.; Penpraze, V.; Hislop, J.; Davies, G.; Grant, S.; Paton, J.Y.: Objective measurement of physical activity and sedentary behaviour: review with new data. Arch. Dis. Child. 93(7), 614–619 (2008)

    Article  Google Scholar 

  4. Sylvia, L.G.; Bernstein, E.E.; Hubbard, J.L.; Keating, L.; Anderson, E.J.: Practical guide to measuring physical activity. J. Acad. Nutr. Diet. 114(2), 199–208 (2014)

    Article  Google Scholar 

  5. Vallance, J.K.; Buman, M.P.; Stevinson, C.; Lynch, B.M.: Associations of overall sedentary time and screen time with sleep outcomes. Am. J. Health Behav. 39(1), 62–67 (2015)

    Article  Google Scholar 

  6. Olds, T.S.; Maher, C.A.; Matricciani, L.: Sleep duration or bedtime? Exploring the relationship between sleep habits and weight status and activity patterns. Sleep 34(10), 1299–1307 (2011)

    Article  Google Scholar 

  7. Carter, B.; Rees, P.; Hale, L.; Bhattacharjee, D.; Paradkar, M.S.: Association between portable screen-based media device access or use and sleep outcomes: a systematic review and meta-analysis. JAMA Pediatr. 170(12), 1202–1208 (2016)

    Article  Google Scholar 

  8. Anderson, M.; Jiang, J.: Teens, social media & technology 2018. Pew Research Center 31(2018), 1673–1689 (2018)

    Google Scholar 

  9. Arora, A.; Chakraborty, P.; Bhatia, M.P.S.: Problematic use of digital technologies and its impact on mental health during COVID-19 pandemic: assessment using machine learning. In: Arpaci, I., Al-Emran, M., Al-Sharafi, M.A., Marques, G. (eds.) Emerging Technologies During the Era of COVID-19 Pandemic (2020) (in press)

  10. Arora, A.; Chakraborty, P.; Bhatia, M.P.S.: Analysis of data from wearable sensors for sleep quality estimation and prediction using deep learning. Arab. J. Sci. Eng. 45(12), 10793–10812 (2020)

    Article  Google Scholar 

  11. Jacobs, D.R., Jr.; Ainsworth, B.E.; Hartman, T.J.; Leon, A.S.: A simultaneous evaluation of 10 commonly used physical activity questionnaires. Med. Sci. Sports Exerc. 25(1), 81–91 (1993)

    Article  Google Scholar 

  12. Shephard, R.J.: Limits to the measurement of habitual physical activity by questionnaires. Br. J. Sports Med. 37(3), 197–206 (2003)

    Article  Google Scholar 

  13. Smith, K.A.; Gallagher, M.; Hays, A.E.; Goss, F.L.; Robertson, R.: Development of the physical activity index as a measure of total activity load and total kilocalorie expenditure during submaximal walking. J. Phys. Act. Health 9(6), 757–764 (2012)

    Article  Google Scholar 

  14. Corder, K.; Brage, S.; Ekelund, U.: Accelerometers and pedometers: methodology and clinical application. Curr. Opin. Clin. Nutr. Metab. Care 10(5), 597–603 (2007)

    Article  Google Scholar 

  15. Tudor-Locke, C.; Brashear, M.M.; Johnson, W.D.; Katzmarzyk, P.T.: Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese US men and women. Int. J. Behav. Nutr. Phys. Act. 7(1), 1–11 (2010)

    Article  Google Scholar 

  16. Rachele, J.N.; McPhail, S.M.; Washington, T.L.; Cuddihy, T.F.: Practical physical activity measurement in youth: a review of contemporary approaches. World Journal of Pediatrics 8(3), 207–216 (2012)

    Article  Google Scholar 

  17. de Arriba-Pérez, F.; Caeiro-Rodríguez, M.; Santos-Gago, J.M.: How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators. J. Ambient. Intell. Humaniz. Comput. 9(4), 897–917 (2018)

    Article  Google Scholar 

  18. Montgomery-Downs, H.E.; Insana, S.P.; Bond, J.A.: Movement toward a novel activity monitoring device. Sleep Breath. 16(3), 913–917 (2012)

    Article  Google Scholar 

  19. Poirier, J.; Bennett, W.L.; Jerome, G.J.; Shah, N.G.; Lazo, M.; Yeh, H.C.; Clark, J.M.; Cobb, N.K.: Effectiveness of an activity tracker-and internet-based adaptive walking program for adults: a randomized controlled trial. J. Med. Internet Res. 18(2), e34 (2016)

  20. Lu, T.C.; Fu, C.M.; Ma, M.H.M.; Fang, C.C.; Turner, A.M.: Healthcare applications of smart watches: a systematic review. Appl. Clin. Inform. 7(3), 850 (2016)

    Article  Google Scholar 

  21. Hawi, N.S.; Samaha, M.: To excel or not to excel: strong evidence on the adverse effect of smartphone addiction on academic performance. Comput. Educ. 98, 81–89 (2016)

    Article  Google Scholar 

  22. LeBourgeois, M.K.; Hale, L.; Chang, A.M.; Akacem, L.D.; Montgomery-Downs, H.E.; Buxton, O.M.: Digital media and sleep in childhood and adolescence. Pediatrics 140(Supplement 2), S92–S96 (2017)

    Article  Google Scholar 

  23. Thomée, S.; Härenstam, A.; Hagberg, M.: Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC Public Health 11(1), 66 (2011)

    Article  Google Scholar 

  24. Vernon, L.; Modecki, K.L.; Barber, B.L.: Mobile phones in the bedroom: trajectories of sleep habits and subsequent adolescent psychosocial development. Child Dev. 89(1), 66–77 (2018)

    Article  Google Scholar 

  25. Chen, M.; Mao, S.; Liu, Y.: Big data: a survey. Mobile Netw. Appl. 19(2), 171–209 (2014)

    Article  Google Scholar 

  26. Kumar, A.; Jaiswal, A.: Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurrency Comput. Pract. Exp. 32(1), e5107 (2020)

  27. Jordan, M.I.; Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  28. Shatte, A.B.; Hutchinson, D.M.; Teague, S.J.: Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 49(9), 1426–1448 (2019)

    Article  Google Scholar 

  29. Kumar, A.; Arora, A.: An ANFIS-based compatibility scorecard for IoT integration in websites. J. Supercomput. 1–29 (2019)

  30. Foti, K.E.; Eaton, D.K.; Lowry, R.; McKnight-Ely, L.R.: Sufficient sleep, physical activity, and sedentary behaviors. Am. J. Prev. Med. 41(6), 596–602 (2011)

    Article  Google Scholar 

  31. Stone, M.R.; Stevens, D.; Faulkner, G.E.: Maintaining recommended sleep throughout the week is associated with increased physical activity in children. Prev. Med. 56(2), 112–117 (2013)

    Article  Google Scholar 

  32. Brand, S.; Gerber, M.; Beck, J.; Hatzinger, M.; Pühse, U.; Holsboer-Trachsler, E.: High exercise levels are related to favorable sleep patterns and psychological functioning in adolescents: a comparison of athletes and controls. J. Adolesc. Health 46(2), 133–141 (2010)

    Article  Google Scholar 

  33. Wu, X.; Tao, S.; Zhang, Y.; Zhang, S.; Tao, F. Low physical activity and high screen time can increase the risks of mental health problems and poor sleep quality among Chinese college students. PLoS ONE 10(3), e0119607 (2015)

  34. Xu, F.; Adams, S.K.; Cohen, S.A.; Earp, J.E.; Greaney, M.L.: Relationship between physical activity, screen time, and sleep quantity and quality in US adolescents aged 16–19. Int. J. Environ. Res. Public Health 16(9), 1524 (2019)

    Article  Google Scholar 

  35. Kredlow, M.A.; Capozzoli, M.C.; Hearon, B.A.; Calkins, A.W.; Otto, M.W.: The effects of physical activity on sleep: a meta-analytic review. J. Behav. Med. 38(3), 427–449 (2015)

    Article  Google Scholar 

  36. Feng, Q.; Du, Y.; Ye, Y.L.; He, Q.Q.: Associations of physical activity, screen time with depression, anxiety and sleep quality among Chinese college freshmen. PLoS ONE 9(6), e100914 (2014)

  37. Infante, S.; Zapatero, L.; Zubeldia, J. 2020. 10. Anderson M, Jiang J. Teens, Social Media & Technology 2018. Pew Research Center; 2018. Available from: https://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/11. Curtis BL, Ashford RD, Magnuson KI, Ryan-Pettes SR. J. Investig. Allergol. Clin. Immunol. 30(2), 133–155 (2014)

  38. Johansson, A.E.; Petrisko, M.A.; Chasens, E.R.: Adolescent sleep and the impact of technology use before sleep on daytime function. J. Pediatr. Nurs. 31(5), 498–504 (2016)

    Article  Google Scholar 

  39. Christensen, M.A.; Bettencourt, L.; Kaye, L.; Moturu, S.T.; Nguyen, K.T.; Olgin, J.E.; Pletcher, M.J.; Marcus, G.M.: Direct measurements of smartphone screen-time: relationships with demographics and sleep. PLoS ONE 11(11), e0165331 (2016)

  40. Lou, Z.; Wang, L.; Jiang, K.; Wei, Z.; Shen, G.: Reviews of wearable healthcare systems: Materials, devices and system integration. Mater. Sci. Eng. R Rep. 140, 100523 (2020)

  41. Papa, A.; Mital, M.; Pisano, P.; Del Giudice, M.: E-health and wellbeing monitoring using smart healthcare devices: an empirical investigation. Technol. Forecast. Soc. Change 153, 119226 (2020)

  42. Sherratt, R.S.; Nilanjan, D.: Low-power wearable healthcare sensors 892 (2020)

  43. Li, S.; Ma, Z.; Cao, Z.; Pan, L.; Shi, Y.: Advanced wearable microfluidic sensors for healthcare monitoring. Small 16(9), 1903822 (2020)

    Article  Google Scholar 

  44. Wang, Y.; Chao, M.; Wan, P.; Zhang, L.: A wearable breathable pressure sensor from metal-organic framework derived nanocomposites for highly sensitive broad-range healthcare monitoring. Nano Energy 70 104560 (2020)

  45. Pang, Y.; Yang, Z.; Yang, Y.; Ren, T.L.: Wearable electronics based on 2D materials for human physiological information detection. Small 16(15), 1901124 (2020)

    Article  Google Scholar 

  46. Jin, J.; Gao, B.; Yang, S.; Zhao, B.; Luo, L.; Woo, W.L.: Attention-block deep learning based features fusion in wearable social sensor for mental wellbeing evaluations. IEEE Access 8, 89258–89268 (2020)

    Article  Google Scholar 

  47. Tazawa, Y.; Liang, K.C.; Yoshimura, M.; Kitazawa, M.; Kaise, Y.; Takamiya, A.; Kishi, A.; Horigome, T.; Mitsukura, Y.; Mimura, M.; Kishimoto, T.: Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning. Heliyon 6(2), e03274 (2020)

  48. Zhao, S.; Zhao, Q.; Zhang, X.; Peng, H.; Yao, Z.; Shen, J.; Yao, Y.; Jiang, H.; Hu, B.: Wearable EEG-based real-time system for depression monitoring. In: International Conference on Brain Informatics, pp. 190–201. Springer, Cham (2017, November)

  49. Sandulescu, V.; Andrews, S.; Ellis, D.; Bellotto, N.; Mozos, O.M.: Stress detection using wearable physiological sensors. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, pp. 526–532. Springer, Cham (2015)

  50. Gjoreski, M.; Gjoreski, H.; Luštrek, M.; Gams, M.: Continuous stress detection using a wrist device: in laboratory and real life. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 1185–1193 (2016, September)

  51. Alharthi, R.; Alharthi, R.; Guthier, B.; El Saddik, A.: CASP: context-aware stress prediction system. Multimedia Tools and Applications 78(7), 9011–9031 (2019)

    Article  Google Scholar 

  52. Rabbi, M.; Ali, S.; Choudhury, T.; Berke, E.: Passive and in-situ assessment of mental and physical well-being using mobile sensors. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 385–394 (2011, September)

  53. Kamdar, M.R.; Wu, M.J.: PRISM: a data-driven platform for monitoring mental health. In: Biocomputing 2016: Proceedings of the Pacific Symposium, pp. 333–344 (2016)

  54. Kumar, A.; Sharma, K.; Sharma, A.: Hierarchical deep neural network for mental stress state detection using IoT based biomarkers. Pattern Recognit. Lett. (2021)

  55. Hossain, H.S.; Ramamurthy, S.R.; Khan, M.A.A.H.; Roy, N.: An active sleep monitoring framework using wearables. ACM Trans. Interact. Intell. Syst. (TiiS) 8(3), 1–30 (2018)

    Article  Google Scholar 

  56. Sathyanarayana, A.; Joty, S.; Fernandez-Luque, L.; Ofli, F.; Srivastava, J.; Elmagarmid, A.; Arora, T.; Taheri, S.: Sleep quality prediction from wearable data using deep learning. JMIR mHealth and uHealth 4(4), e125 (2016)

  57. Patel, P.; Kim, J.Y.; Brooks, L.J.: Accuracy of a smartphone application in estimating sleep in children. Sleep Breath. 21(2), 505–511 (2017)

    Article  Google Scholar 

  58. Toon, E.; Davey, M.J.; Hollis, S.L.; Nixon, G.M.; Horne, R.S.; Biggs, S.N.: Comparison of commercial wrist-based and smartphone accelerometers, actigraphy, and PSG in a clinical cohort of children and adolescents. J. Clin. Sleep Med. 12(3), 343–350 (2016)

    Article  Google Scholar 

  59. Tal, A.; Shinar, Z.; Shaki, D.; Codish, S.; Goldbart, A.: Validation of contact-free sleep monitoring device with comparison to polysomnography. J. Clin. Sleep Med. 13(3), 517–522 (2017)

    Article  Google Scholar 

  60. Bhat, S.; Ferraris, A.; Gupta, D.; Mozafarian, M.; DeBari, V.A.; Gushway-Henry, N.; Gowda, S.P.; Polos, P.G.; Rubinstein, M.; Seidu, H.; Chokroverty, S.: Is there a clinical role for smartphone sleep apps? Comparison of sleep cycle detection by a smartphone application to polysomnography. J. Clin. Sleep Med. 11(7), 709–715 (2015)

    Article  Google Scholar 

  61. Behar, J.; Roebuck, A.; Shahid, M.; Daly, J.; Hallack, A.; Palmius, N.; Stradling, J.; Clifford, G.D.: SleepAp: an automated obstructive sleep apnoea screening application for smartphones. IEEE J. Biomed. Health Inform. 19(1), 325–331 (2014)

    Article  Google Scholar 

  62. Nakano, H.; Hirayama, K.; Sadamitsu, Y.; Toshimitsu, A.; Fujita, H.; Shin, S.; Tanigawa, T.: Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept. J. Clin. Sleep Med. 10(1), 73–78 (2014)

    Article  Google Scholar 

  63. Scott, H.; Lack, L.; Lovato, N.: A pilot study of a novel smartphone application for the estimation of sleep onset. J. Sleep Res. 27(1), 90–97 (2018)

    Article  Google Scholar 

  64. Chen, Z.; Lin, M.; Chen, F.; Lane, N.D.; Cardone, G.; Wang, R.; Li, T.; Chen, Y.; Choudhury, T.; Campbell, A.T.: Unobtrusive sleep monitoring using smartphones. In: 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, pp. 145–152. IEEE (2013, May)

  65. Farhan, A.A.; Lu, J.; Bi, J.; Russell, A.; Wang, B.; Bamis, A.: Multi-view bi-clustering to identify smartphone sensing features indicative of depression. In: 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 264–273. IEEE (2016, June)

  66. Stütz, T.; Kowar, T.; Kager, M.; Tiefengrabner, M.; Stuppner, M.; Blechert, J.; Wilhelm, F.H.; Ginzinger, S.: Smartphone based stress prediction. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 240–251. Springer, Cham (2015, June)

  67. Cao, B.; Zheng, L.; Zhang, C.; Yu, P.S.; Piscitello, A.; Zulueta, J.; Ajilore, O.; Ryan, K.; Leow, A.D.: Deepmood: modeling mobile phone typing dynamics for mood detection. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 747–755 (2017, August)

  68. Maxhuni, A.; Hernandez-Leal, P.; Morales, E.F.; Sucar, L.E.; Osmani, V.; Muńoz-Meléndez, A.; Mayora, O.: Using intermediate models and knowledge learning to improve stress prediction. In: Applications for Future Internet, pp. 140–151. Springer, Cham (2017)

  69. Shin, C.; Dey, A.K.: Automatically detecting problematic use of smartphones. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 335–344 (2013, September)

  70. Lawanont, W.; Inoue, M.: A development of classification model for smartphone addiction recognition system based on smartphone usage data. In: International Conference on Intelligent Decision Technologies, pp. 3–12. Springer, Cham (2017, June)

  71. Ellis, D.A.; Davidson, B.I.; Shaw, H.; Geyer, K.: Do smartphone usage scales predict behavior? Int. J. Hum Comput Stud. 130, 86–92 (2019)

    Article  Google Scholar 

  72. Kim, S.K.; Kang, H.B.: An analysis of smartphone overuse recognition in terms of emotions using brainwaves and deep learning. Neurocomputing 275, 1393–1406 (2018)

    Article  Google Scholar 

  73. de Arriba Pérez, F.; Gago, J.M.S.; Rodríguez, M.C.: Calculation of sleep indicators in students using smartphones and wearables. In: New Advances in Information Systems and Technologies, pp. 169–178. Springer, Cham (2016)

  74. Sano, A.; Phillips, A.J.; Amy, Z.Y.; McHill, A.W.; Taylor, S.; Jaques, N.; Czeisler, C.A.; Klerman, E.B.; Picard, R.W.: Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1–6. IEEE (2015, June)

  75. Cvetković, B.; Gjoreski, M.; Šorn, J.; Maslov, P.; Luštrek, M.: Monitoring physical activity and mental stress using wrist-worn device and a smartphone. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 414–418. Springer, Cham (2017, September)

  76. Buysse, D.J.; Reynolds, C.F., III.; Monk, T.H.; Berman, S.R.; Kupfer, D.J.: The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28(2), 193–213 (1989)

    Article  Google Scholar 

  77. Tudor-Locke, C.; Bassett, D.R.: How many steps/day are enough? Sports Med. 34(1), 1–8 (2004)

    Article  Google Scholar 

  78. Bassett, D.R.; Toth, L.P.; LaMunion, S.R.; Crouter, S.E.: Step counting: a review of measurement considerations and health-related applications. Sports Med. 47(7), 1303–1315 (2017)

    Article  Google Scholar 

  79. Council on Communications and Media. Children, adolescents, and the media. Pediatrics 132, 958–961 (2013). https://pediatrics.aappublications.org/content/132/5/958

  80. Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  MATH  Google Scholar 

  81. Gnanambal, S.; Thangaraj, M.; Meenatchi, V.T.; Gayathri, V.: Classification algorithms with attribute selection: an evaluation study using WEKA. Int. J. Adv. Netw. Appl. 9(6), 3640–3644 (2018)

    Google Scholar 

  82. Kumar, A.; Arora, A.: A filter-wrapper based feature selection for optimized website quality prediction. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 284–291. IEEE (2019, February)

  83. Arora, A.: Soft Computing Techniques for Web Quality Analytics (Doctoral dissertation) (2019)

  84. Han, P.; Li, L.; Zhang, H.; Guan, L.; Marques, C.; Savović, S.; Ortega, B.; Min, R.; Li, X.: Low-cost plastic optical fiber sensor embedded in mattress for sleep performance monitoring. Opt. Fiber Technol. 64, 102541 (2021)

  85. Kumar, A.; Arora, A.: Website quality analytics using Metaheuristic based optimization. Recent Adv. Comput. Sci. Commun. 14(3), 901–921 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India

    Anshika Arora, Pinaki Chakraborty & M. P. S. Bhatia

Authors
  1. Anshika Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pinaki Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. P. S. Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anshika Arora.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Appendix: Smartphone Application Screen

Appendix: Smartphone Application Screen

The following Figs. 

Fig. 21
figure 21

Total smartphone usage

Full size image

21 and

Fig. 22
figure 22

Night time smartphone usage

Full size image

22 present smartphone application screen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arora, A., Chakraborty, P. & Bhatia, M.P.S. Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique. Arab J Sci Eng 47, 1999–2024 (2022). https://doi.org/10.1007/s13369-021-06078-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06078-5

Keywords

Search

Navigation