Skip to main content

Advertisement

SpringerLink
Log in

Floor plan optimization for indoor environment based on multimodal data

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

Abstract

Designing an optimal indoor space is challenging in interior architecture. The optimal space design requires a comprehensive analysis of the living situation of residents in a space. However, it is extremely difficult to collect data from the space where daily life occurs. Many spatial analysis sensors are required because various daily life data must be collected precisely. Hence, it is difficult for indoor space designers to use the daily-life information of users when managing indoor layouts or floor plans. In this paper, we introduce a technique to solve this problem: simple mobile application (app) logs are used to identify the daily-life patterns of users in an indoor space, and the results are used to create the optimal space layout. We collect and process key information from the mobile app logs and Google app servers to generate a high-dimensional dataset required for user behavior analysis. Subsequently, we suggest a floor plan that minimizes the living cost using a two-dimensional genetic algorithm. Our method will facilitate the spatial analysis of currently inhabited indoor space and reduce the space utilization feedback costs of 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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

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

  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 Professional 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 Intell Humanized Comput 1–12

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

    Article  Google Scholar 

  6. Ullah A, Haydarov K, Haq I, Muhammad, Khan, Rho S, Lee M, Baik S (2019) A Cluster Separation Measure. IEEE Trans Pattern Analysis Mach Intell. PAMI-1 (2):224–227

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

    Article  Google Scholar 

  8. Byun N, Kim M (2015) A Study on Classification of Apartment Complexes Using Spatial Analysis Technique-Focused on Pedestrian Circulation in Apartment Complex. J Architectural Inst Korea Plan & Design 31(4):61–68

    Article  Google Scholar 

  9. Hinton G, Roweis S (2003) Stochastic neighbor embedding. In: Advances in neural information processing systems, pp 857–864

  10. Tenenbaum J, De Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. science 290(5500):2319–2323

  11. Saul L, Roweis S (2000) An introduction to locally linear embedding. Available at: http://www.cs.toronto.edu/roweis/lle/publications.Html

  12. Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 2579–2605

  13. 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

  14. Kang J, Seo S, Hong J (2011) Usage pattern analysis of smartphones. In: 2011 13th Asia-Pacific Network Operations and Management Symposium, pp 1–8

  15. Kang J, Seo S, Hong J (2011) Collect and analyze smart phone usage patterns for mobile network management. In: Proceedings of the 13th Asia-Pacific Network Operations and Management Symposium, Taipei, Taiwan, pp 21–23

  16. 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

  17. 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 Preventive Med 43(4):440–450

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. 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 

  20. Mafrur R, Nugraha I, 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 

  21. Jalali L, Oh H, Moazeni R, Jain R (2016) Human Behavior Analysis from Smartphone Data Streams. In: International Workshop on Human Behavior Understanding, pp 68–85

  22. Rivron V, Khan M, Charneau S, Chrisment I (2016) Exploring smartphone application usage logs with declared sociological information. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom), pp 266–273

  23. 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

  24. 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 

  25. 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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. 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 

  29. 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

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

  31. Wang G, Zhang X, Tang S, Zheng H, Zhao B (2016) Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp 225–236

  32. Farhan A, 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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  36. Lawrence S, Giles C, Tsoi A, Back A (1997) Face recognition: A convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113

    Article  Google Scholar 

  37. Casale P, Pujol O, Radeva P (2011) Human Activity Recognition from Accelerometer Data Using a Wearable Device. In: Proceedings of Pattern Recognition and Image Analysis, pp 289–296

  38. 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, IEEE

  39. Ann O (2014) Lau B (2014) Human activity recognition: A review, 4th IEEE International Conference on Control System. Computing and Engineering, ICCSCE

    Google Scholar 

  40. Ke S, Thuc H, Lee Y, Hwang J, Yoo J, Choi K (2013) a review on video-based human activity recognition. Computers 2(2):88–131

    Article  Google Scholar 

  41. Chan M, Esteve D, Escriba C, Campo E (2008) A Review of Smart Homes-Present State and Future Challenges. J Comput Methods Prog Biomed 91(1):55–81

    Article  Google Scholar 

  42. Singh D, Merdivan E, Psychoula I, Kropf J, Hanke S, Geist M, Holzinger A (2018) Human Activity Recognition using Recurrent Neural Networks, pp 267–274. arXiv:1804.07144

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

  44. Li Q, Zheng Y, Xie X, Chen Y, Liu W (2008) Ma W (2008) 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

  45. Kang S, Kim S (2021) Behavior analysis method for indoor environment based on app usage mining. J Supercomput 1–21

  46. Kang S, Kim Y, Park T, Kim C (2013) Automatic player behavior analysis system using trajectory data in a massive multiplayer online game. Multimed Tools Appl 66(3):383–404

    Article  Google Scholar 

  47. Wang T, Wong D (1991) An optimal algorithm for floorplan area optimization. In: Proceedings of the 27th ACM/IEEE Design Automation Conference, pp 180–186

  48. Rebaudengo M, Reorda M (1996) GALLO: A genetic algorithm for floorplan area optimization. IEEE Trans Comput-Aided Design of Integrated Circuits and Systems 15(8):943–951

    Article  Google Scholar 

  49. Wang T, Wong D (1992) Optimal floorplan area optimization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 11(8):992–1002

    Article  Google Scholar 

  50. Michalek J, Choudhary R, Papalambros P (2002) Architectural layout design optimization. Engineering optimization 34(5):461–484

    Google Scholar 

  51. Rodrigues E (2014) Automated floor plan design: generation, simulation, and optimization (Doctoral dissertation)

  52. Dogan T, Saratsis E, Reinhart C (2015) The optimization potential of floor-plan typologies in early design energy modeling. In: Proceedings of BS2015: 14th Conference of International Building Performance Simulation Association, Hyderabad, India, Dec

  53. Zawidzki M, Szklarski J (2020) Multi-objective optimization of the floor plan of a single story family house considering position and orientation. Advances in Engineering Software 141:102766

  54. Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural computation 11(1):229–242

    Article  Google Scholar 

  55. Applegate D, Bixby R, Chvátal V, Cook W (2011) The traveling salesman problem. Princeton university press

    Google Scholar 

  56. Olivier I, Smith D, and Holland J (1987) A study of permutation crossover operators on the travelling salesman problem. In: Proceeding Second International Conference on Genetic Algorithms, pp 224–230

  57. Goldberg D, Lingle R (1985) Alleles, loci, and the traveling salesman problem. In: Proceedings of International Conference on Genetic Algorithms and their Applications, pp 154–159

  58. Whitley L, Starkweather T, Fuquay D (1989) Scheduling problems and traveling salesmen: the genetic edge recombination operator. ICGA, pp 133–140

  59. Yun Y, Moon C (2011) Genetic algorithm approach for precedence-constrained sequencing problems. Journal of Intelligent Manufacturing 22(3):379–388

    Article  Google Scholar 

  60. Poon P, Carter J (1995) Genetic algorithm crossover operators for ordering applications. Computers & Operations Research 22(1):135–147

    Article  Google Scholar 

  61. Seo D, Moon B (2002) Voronoi Quantizied Crossover For Traveling Salesman Problem. In: GECCO, pp 544–552

  62. Hopper E, Turton B (1999) A genetic algorithm for a 2D industrial packing problem. Computers & Industrial Engineering 37(1–2):375–378

    Article  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.

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. Floor plan optimization for indoor environment based on multimodal data. J Supercomput 78, 2724–2743 (2022). https://doi.org/10.1007/s11227-021-03952-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03952-9

Keywords

Search

Navigation