Advertisement

Mobile Networks and Applications

, Volume 23, Issue 2, pp 216–226 | Cite as

emHealth: Towards Emotion Health Through Depression Prediction and Intelligent Health Recommender System

  • Shiqi Yang
  • Ping Zhou
  • Kui DuanEmail author
  • M. Shamim Hossain
  • Mohammed F. Alhamid
Article

Abstract

Depression is an important mental disease of global concern. Its complicated etiology and chronic clinical features make it difficult for users to be conscious of their own depression emotion and seriously threaten the patient’s life safety. With the development of e-commerce, intelligent recommender system has brought new opportunities to personalized health monitoring for the users with emotional distress. Therefore, this paper puts forward the emHealth system, which is an intelligent health recommendation system with depression prediction for emotion health. This paper explores the monitoring and improvement of users psychological and physiological conditions by pushing personalized therapy solutions to patients with emotional distress. Specifically, this paper first proposes the system architecture of emHealth. Then, we design personalized mobile phone Apps to collect emotional data of users with tendentious depressive mood, and find the five main external characteristics of depression by Pearson correlation analysis. We divide 1047 volunteers data into training set and test set, and construct prediction model of depression using decision tree and support vector machine algorithms. For the different external factors that lead to depression, we give personalized recommendation and intelligent decision-making solution, and push related emotional improvement suggestions to guide users behavior. Finally, a specific application scene is demonstrated where patient’s family member carry out psychological counseling for the patient, to verify the practicability and validity of the system. The beneficial effects of this system can meet the needs of the electronic market and can be promoted and popularized.

Keywords

E-commerce Smart devices Depression detection Recommender systems 

Notes

Acknowledgements

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for funding this research group No. (RG-1437-042).

References

  1. 1.
    Lin K, Song J, Luo J, Ji W, Shamim Hossain M, Ghoneim A (2017) Green video transmission in the mobile cloud networks. IEEE Trans Circuits Syst Video Technol 27(1):159–169CrossRefGoogle Scholar
  2. 2.
    Chen J, He K, Ruiying D, Xiang Y (2015) Dominating set and network coding-based routing in wireless mesh networks. IEEE Trans Parallel Distrib Syst 26(2):423–433CrossRefGoogle Scholar
  3. 3.
    Wan J, Zou C, Ullah S, Lai C-F, Zhou M, Wang X (2013) Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Netw 27(5):56–61CrossRefGoogle Scholar
  4. 4.
    Bultz BD, Carlson LE (2006) Emotional distress: the sixth vital sign–future directions in cancer care. Psycho-Oncology 15(2):93–95CrossRefGoogle Scholar
  5. 5.
    Chen M, Jun Y, Zhu X, Wang X, Liu M, J. Song. (2017) Smart home 2.0: Innovative smart home system powered by botanical iot and emotion detection. Mobile Networks and ApplicationsGoogle Scholar
  6. 6.
    Busso C, Deng Z, Yildirim S, Bulut M, Lee CM, Kazemzadeh A, Lee S, Neumann U, Narayanan S (2004) Analysis of emotion recognition using facial expressions, speech and multimodal information. In: International Conference on Multimodal Interfaces, pp 205–211Google Scholar
  7. 7.
    Chen M, Zhang Y, Qiu M, Guizani N, Hao Y (2017) SPHA: Smart Personal Health Advisor Based on Deep Analytics. IEEE CommunGoogle Scholar
  8. 8.
    Yang Z, Narayanan S (2016) Lightly-supervised utterance-level emotion identification using latent topic modeling of multimodal words. In: IEEE International Conference On Acoustics, Speech and Signal Processing, pp 2767–2771Google Scholar
  9. 9.
    Chen M, Zhou P, Fortino G (2017) Emotion communication system. IEEE Access 5:326–337CrossRefGoogle Scholar
  10. 10.
    Lin K, Chen M, Deng J, Hassan MM, Fortino G (2016) Enhanced fingerprinting and trajectory prediction for iot localization in smart buildings. IEEE Trans Autom Sci Eng 13(3):1294–1307CrossRefGoogle Scholar
  11. 11.
    Power M, Dalgleish T (2015) Cognition and emotion: from order to disorder. Psychology PressGoogle Scholar
  12. 12.
    Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5(1):8869–8879CrossRefGoogle Scholar
  13. 13.
    Hwang K, Chen M (2017) Big-data analytics for cloud IoT and cognitive learning. John Wiley & SonsGoogle Scholar
  14. 14.
    Lin K, Xia F, Wang W, Tian D, Song J (2016) System design for big data application in emotion-aware healthcare. IEEE Access 4:6901–6909CrossRefGoogle Scholar
  15. 15.
    He D, Chan S, Zhang Y, Yang H (2014) Lightweight and confidential data discovery and dissemination for wireless body area networks. IEEE J Biomed Health Inf 18(2):440–448CrossRefGoogle Scholar
  16. 16.
    Chen M, Ma Y, Li Y, Wu D, Zhang Y, Youn C (2017) Wearable 2.0: enable Human-cloud integration in next generation healthcare system. IEEE Commun 55(1):54–61CrossRefGoogle Scholar
  17. 17.
    Neugebauer R (1999) Mind matters: the importance of mental disorders in public health’s 21st century mission. Am J Public Health 89(9):1309–1311CrossRefGoogle Scholar
  18. 18.
    Ap Association (2013) The diagnostic and statistical manual of mental disorders fifth edition: Dsm-5. Psychiatry Res 189(1):158–159Google Scholar
  19. 19.
    Murray CJ, Lopez AD (1997) Alternative projections of mortality and disability by cause 1990-2020: Global burden of disease study. Lancet 349(9064):1498–1504CrossRefGoogle Scholar
  20. 20.
    Bhakta I, Sau A (2016) Prediction of depression among senior citizens using machine learning classifiers. Int J Comput Appl 144(7):11–16Google Scholar
  21. 21.
    Ma X, Di H, Wang Y, Wang Y (2016) Cost-sensitive two-stage depression prediction using dynamic visual clues. In: Asian Conference on Computer Vision, pp 338–351Google Scholar
  22. 22.
    Zhu Y, Shang Y, Shao Z, Guo G (2017) Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans Affective ComputGoogle Scholar
  23. 23.
    Chen M, Shi X, Zhang Y, Wu D, Guizani M (2017) Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans. Big DataGoogle Scholar
  24. 24.
    Wang K, Shao Y, Shu L, Zhu C, Zhang Y (2016) Mobile big data fault-tolerant processing for ehealth networks. IEEE Netw Mag 30(1):36–42CrossRefGoogle Scholar
  25. 25.
    Khan AM (2011) Personal state and emotion monitoring by wearable computing and machine learning. In: Bcs-hciGoogle Scholar
  26. 26.
    Tacconi D, Mayora O, Lukowicz P, Arnrich B, Setz C, Troster G, Haring C (2008) Activity and emotion recognition to support early diagnosis of psychiatric diseases. In: International conference on pervasive computing technologies for healthcare, 2008. pervasivehealth, pp 100–102Google Scholar
  27. 27.
    Shang W (2016) Depression management using electronic health record. Individual progression prediction. Master’s thesis University of WashingtonGoogle Scholar
  28. 28.
    Paradiso R, Bianchi AM, Lau K, Scilingo EP (2010) Psyche: personalised monitoring systems for care in mental health. In: Engineering in Medicine and Biology Society, vol 2010, pp 3602–3605Google Scholar
  29. 29.
    Wu C, Yoshinaga T, Ji Y, Murase T, Zhang Y (2017) A reinforcement learning-based data storage scheme for vehicular ad hoc networks. IEEE Trans Veh Technol 66(7):6336–6348CrossRefGoogle Scholar
  30. 30.
    Chen M, Jun Y, Hao Y, Mao S, Hwang K (2017) A 5g cognitive system for healthcare, big data and cognitive computing. Big Data Cognitive Comput 1(1)Google Scholar
  31. 31.
    Chen M, Miao Y, Hao Y, Kwang K (2017) Narrow Band Internet of Things. IEEE Access 7Google Scholar
  32. 32.
    Chen M, Qian Y, Hao Y, Li Y, Song J (2018) Data-driven computing and caching in 5G networks: architecture and delay analysis. IEEE Wirel Commun 25(1)Google Scholar
  33. 33.
    Chen M, Yang J (2018) 5G-Smart diabetes: towards personalized diabetes diagnosis with healthcare big data clouds. IEEE CommunGoogle Scholar
  34. 34.
    Schubert P, Ginsburg M (2010) Virtual communities of transaction: the role of personalization in electronic commerce. Electron Mark 10(1):45–55Google Scholar
  35. 35.
    Zou C, Zhang D, Wan J, Hassan MM, Lloret J (2017) Using concept lattice for personalized recommendation system design. IEEE Syst J 11(1):305–314CrossRefGoogle Scholar
  36. 36.
    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowledge Data Eng 17(6):734– 749CrossRefGoogle Scholar
  37. 37.
    Zhang Y, Chen M, Huang D, Di W, idoctor YL (2016) Personalized and professionalized medical recommendations based on hybrid matrix factorization. Futur Gener Comput Syst 66:30–35CrossRefGoogle Scholar
  38. 38.
    Dai W, Qiu L, Ana W, Qiu M (2016) Cloud infrastructure resource allocation for big data applications. IEEE Transactions on Big DataGoogle Scholar
  39. 39.
    Li Y, Gai K, Ming Z, Zhao H, Qiu M (2016) Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 12(4s): 67Google Scholar
  40. 40.
    Li J, Qiu M, Ming Z, Quan G, Qin X, Zonghua G (2012) Online optimization for scheduling preemptable tasks on iaas cloud systems. J Parallel Distributed Comput 72(5):666–677CrossRefGoogle Scholar
  41. 41.
    Tian D, Zhou J, Wang Y, Yingrong L, Xia H, Yi Z (2015) A dynamic and self-adaptive network selection method for multimode communications in heterogeneous vehicular telematics. IEEE Trans Intell Transp Syst 16(6):3033–3049CrossRefGoogle Scholar
  42. 42.
    Tian D, Zhou J, Sheng Z, Chen M, Ni Q, Leung VCM (2017) Self-organized relay selection for cooperative transmission in vehicular ad-hoc networks. IEEE Transactions on Vehicular TechnologyGoogle Scholar
  43. 43.
    Tian D, Zhou J, Sheng Z (2017) An adaptive fusion strategy for distributed information estimation over cooperative multi-agent networks. IEEE Trans Inf Theory 63(5):3076–3091MathSciNetzbMATHGoogle Scholar
  44. 44.
    Zung WWK (1965) A self-rating depression scale. Arch Gen Psychiatry 12(12):63CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Shiqi Yang
    • 1
  • Ping Zhou
    • 2
  • Kui Duan
    • 3
    Email author
  • M. Shamim Hossain
    • 4
  • Mohammed F. Alhamid
    • 4
  1. 1.State Grid, Hubei Jingzhou Power Supply CompanyJingzhouChina
  2. 2.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.School HospitalHuazhong University of Science and TechnologyWuhanChina
  4. 4.Department of Software Engineering, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations