Skip to main content
SpringerLink
Log in

Behavior analysis method for indoor environment based on app usage mining

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With the recent development of information and communication technologies, the utilization areas of spatial information are increasing rapidly while merging with various industries and technologies. This study introduces a system that can analyze the space utilization of users at low cost in indoor environment using simple smartphone app logs. We collect and process important information from mobile app logs and Google app server and generate a high-dimensional dataset required to analyze user behaviors. In addition, user behaviors are classified and clustered by applying a VGG classifier and a clustering algorithm based on t-stochastic neighbor embedding (t-SNE). Our system can easily acquire a large amount of data required for deep learning network learning without additional sensors for spatial analysis and enhance the accuracy of network classification and cluster through these data. Our methodology can assist spatial analysis for indoor environments in which people are living and help reduce the cost of space utilization feedback from users.

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

Similar content being viewed by others

References

  1. Electronics and Telecommunications Research Institute, ETRI 2019 Technology Report (2019)

  2. Lee S, Min C, Yoo C, Song J (2013) Understanding customer malling behavior in an urban shopping mall using smartphones. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication pp 901–910

  3. Guo B, Wang Z, Wang P, Xin T, Zhang D, Yu Z (2020) Deepstore: understanding customer behaviors in unmanned stores. IT Prof 22(3):55–63

    Article  Google Scholar 

  4. Du H, Yu Z, Guo B, Han Q, Chen C (2020) GroupShop: monitoring group shopping behavior in real world using mobile devices. J Ambient Int Human Comput 1–12. https://doi.org/10.1007/s12652-019-01673-9

  5. Mun SH, Kwak Y, Huh JH (2019) A case-centered behavior analysis and operation prediction of AC use in residential buildings. Energy and Buildings 188:137–148

    Article  Google Scholar 

  6. Sangalli A, Pagliano L, Causone F, Salvia G, Morello E (2019) Energy efficiency and occupants’ behavior: analysis of a public housing case study. In 51st AiCARR international conference. the human dimension of building energy performance, pp 643–656

  7. Ullah A, Haydarov K, Haq I, Khan Muhammad, Rho S, Lee MY, Baik SW (2019) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAMI 1(2):224–227

    Google Scholar 

  8. Choi J, Kim M, Byun N (2013) Quantitative analysis on the spatial configuration of Korean apartment complexes. J Asian Archit Build Eng 12(2):277–284

    Article  Google Scholar 

  9. Byun N, Kim M (2015) A study on classification of apartment complexes using spatial analysis technique-focused on pedestrian circulation in apartment complex. J Archit Inst Korea Plan Des 31(4):61–68

    Article  Google Scholar 

  10. Tsutsumi M, Yoshida Y, Seya H, Kawaguchi Y (2007) Spatial analysis of Tokyo apartment market. In world conference of spatial econometrics association

  11. Verkasalo H (2010) Analysis of smartphone user behavior. In 2010 ninth international conference on mobile business and 2010 ninth global mobility roundtable (ICMB-GMR), pp. 258–263

  12. Kang JM, Seo SS, Hong JWK (2011) Usage pattern analysis of smartphones. In 2011 13th Asia-Pacific network operations and management symposium, pp. 1-8

  13. Harman M, Jia Y, Zhang Y (2012) App store mining and analysis: MSR for app stores. In 2012 9th IEEE working conference on mining software repositories (MSR), pp. 108–111

  14. Chaix B, Kestens Y, Perchoux C, Karusisi N, Merlo J, Labadi K (2012) An interactive mapping tool to assess individual mobility patterns in neighborhood studies. Am J Prev Med 43(4):440–450

    Article  Google Scholar 

  15. Kelly D, Smyth B, Caulfield B (2013) Uncovering measurements of social and demographic behavior from smartphone location data. IEEE Trans Hum Mach Syst 43(2):188–198

    Article  Google Scholar 

  16. Hamka F, Bouwman H, De Reuver M, Kroesen M (2014) Mobile customer segmentation based on smartphone measurement. Telemat Inform 31(2):220–227

    Article  Google Scholar 

  17. Mafrur R, Nugraha IGD, Choi D (2015) Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose. Human-centric Comput Inform Sci 5(1):31

    Article  Google Scholar 

  18. Jalali L, Oh H, Moazeni R, Jain R (2016) Human behavior analysis from smartphone data streams. In: International workshop on human behavior understanding, Springer, Cham, pp 68–85. https://doi.org/10.1007/978-3-319-46843-3_5

  19. Yamamoto N, Ochiai K, Inagaki A, Fukazawa Y, Kimoto M, Kiriu K, Maeda T (2018) Physiological stress level estimation based on smartphone logs. In 2018 eleventh international conference on mobile computing and ubiquitous network (ICMU), pp. 1–6

  20. Lee Y, Park I, Cho S, Choi J (2018) Smartphone user segmentation based on app usage sequence with neural networks. Telemat Inform 35(2):329–339

    Article  Google Scholar 

  21. Fukazawa Y, Ito T, Okimura T, Yamashita Y, Maeda T, Ota J (2019) Predicting anxiety state using smartphone-based passive sensing. J Biomed Inform 93:103151

    Article  Google Scholar 

  22. Sarker IH, Colman A, Han J (2019) Recencyminer: mining recency-based personalized behavior from contextual smartphone data. J Big Data 6(1):49

    Article  Google Scholar 

  23. Chan M, Estève D, Escriba C, Campo E (2008) A review of smart homes-Present state and future challenges. Comput Methods Prog Biomed 91(1):55–81

    Article  Google Scholar 

  24. Casale P, Pujol O, Radeva P (2011) Human activity recognition from accelerometer data using a wearable device. In Iberian conference on pattern recognition and image analysis, pp 289–296

  25. Fujimoto T, Nakajima H, Tsuchiya, N, Marukawa H, Kuramoto K, Kobashi S, Hata Y (2013) Wearable human activity recognition by electrocardiograph and accelerometer. In 2013 IEEE 43rd international symposium on multiple-valued logic, pp 12–17

  26. Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma WY (2008, November) Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 1–10

  27. Ke SR, Thuc HLU, Lee YJ, Hwang JN, Yoo JH, Choi KH (2013) A review on video-based human activity recognition. Computers 2(2):88–131

    Article  Google Scholar 

  28. Ann OC, Theng LB (2014) Human activity recognition: a review. In 2014 IEEE international conference on control system, computing and engineering (ICCSCE 2014). pp 389–393

  29. Fallmann S, Kropf J (2016) Human activity recognition of continuous data using Hidden Markov Models and the aspect of including discrete data. Intl IEEE conferences pp 121–126

  30. Dilawari A, Khan MU, Rehman Z, Awan KM, Mehmood I, Rho S (2020) Toward generating human-centered video annotations. Circ Syst Signal Process (CSSP) 39(1):857–883

    Article  Google Scholar 

  31. Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, Holzinger A (2018) Human activity recognition using recurrent neural networks. In: International cross-domain conference for machine learning and knowledge extraction, Springer, Cham, pp 267–274. https://doi.org/10.1007/978-3-319-66808-6_18

  32. Yang Z, Su X (2012) Customer behavior clustering using SVM. Phys Proc 33:1489–1496

    Article  Google Scholar 

  33. Perdisci R, Ariu D, Giacinto G (2013) Scalable fine-grained behavioral clustering of http-based malware. Comput Netw 57(2):487–500

    Article  Google Scholar 

  34. Bauckhage C, Sifa R, Drachen A, Thurau C, Hadiji F (2014) Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels. In 2014 IEEE conference on computational intelligence and games, pp 1–8

  35. Drachen A, Thurau C, Sifa R, & Bauckhage C (2014) A comparison of methods for player clustering via behavioral telemetry. arXiv preprint arXiv:1407.3950

  36. De Leoni M, van der Aalst WM, Dees M (2016) A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf Syst 56:235–257

    Article  Google Scholar 

  37. Farhan AA, Lu J, Bi J, Russell A, Wang B, Bamis A (2016) 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

  38. Wang G, Zhang X, Tang S, Zheng H, Zhao BY (2016) Unsupervised clickstream clustering for user behavior analysis. In Proceedings of the 2016 CHI conference on human factors in computing systems, pp 225–236

  39. Peach RL, Yaliraki SN, Lefevre D, Barahona M (2019) Data-driven unsupervised clustering of online learner behaviour. NPJ Sci Learn 4(1):1–11

    Article  Google Scholar 

  40. Kang S, Kim Y, Kim S (2020) Automated spatiotemporal classification based on smartphone app logs. Electronics 9(5):755

    Article  Google Scholar 

  41. Kang SJ, Kim YB, Park T, Kim CH (2013) Automatic player behavior analysis system using trajectory data in a massive multiplayer online game. Multi Tools Appl 66(3):383–404

    Article  Google Scholar 

  42. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  43. Jang SH, Cho SB (2008) Evolving neural NPCs with layered influence map in the real-time simulation game ‘Conqueror’. In 2008 IEEE symposium on computational intelligence and games, pp 385–388

  44. Elshamarka I, Saman ABS (2012) Design and implementation of a robot for maze-solving using flood-fill algorithm. Int J Comput Appl 56(5). http://eprints.utp.edu.my/8621/

  45. Hinton GE, Roweis ST (2003) Stochastic neighbor embedding. Adv Neural Inf Process Syst 15:857–864

    Google Scholar 

  46. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  47. Saul LK, Roweis ST (2000) An introduction to locally linear embedding. unpublished. Available at: http://www.cs.toronto.edu/~roweis/lle/publications.html

  48. Maaten LVD, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(Nov):2579–2605

    MATH  Google Scholar 

  49. Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Statistics-theory Methods 3(1):1–27

    Article  MathSciNet  Google Scholar 

  50. Davies David L, Bouldin Donald W (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAMI 1(2):224–227

    Article  Google Scholar 

  51. Nan H, Li M, Fan L, Tong MD (2019) 3D res-inception network transfer learning for multiple label crowd behavior recognition. KSII Trans Internet Inf Syst 13(3):1450–1463

    Google Scholar 

  52. Wang Y (2020) User online behavior based on big data distributed clustering algorithm. Int J Adv Robot Syst 17(2):1729881420917293

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hongik University, Sejong, Korea

    Shinjin Kang

  2. Jeju National University, Jeju, Korea

    Soo Kyun Kim

Authors
  1. Shinjin Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soo Kyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soo Kyun Kim.

Ethics declarations

Funding

This work was supported by the Natonal Reserach Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2019R1A2C1002525).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, S., Kim, S.K. Behavior analysis method for indoor environment based on app usage mining. J Supercomput 77, 7455–7475 (2021). https://doi.org/10.1007/s11227-020-03532-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03532-3

Keywords

Search

Navigation