• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Outlier based Human Fall Detection using One-Class Classification
  • VIEWS 2083
  • PDF 340

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i30.821

Year: 2021, Volume: 14, Issue: 30, Pages: 2494-2503

Original Article

Outlier based Human Fall Detection using One-Class Classification

Rashmi Gupta1*, Jeetendra Kumar1

1Assistant Professor, Atal Bihari Vajpayee University, Bilaspur (C.G.), India

*Corresponding Author
Email: [email protected]

Received Date:11 May 2021, Accepted Date:13 August 2021, Published Date:13 September 2021

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: This work aims to develop a human fall detection method that is trained using data of routine movement of people only collected from accelerometer sensor to stay away from irregular fall detection model. This work also aims to analyze the effect of calculated features on the fall detection model. Methodology: In the proposed method, The fall detection model is built using one-class classification. At first, data of accelerometer sensor in three directions has been used to detect the fall events. Five feature vectors i.e. resultant, variance, standard deviation, Root Mean square, and Euclidean Norm have been calculated. These four features along with one class SVM have been used to build fall detection model in ways (1) Using only resultant features only (2) Using all calculated features. This model was trained using only activities of daily living (ADL) and tested on both daily living activities and fall activities. Findings: It is found that when the model was built using all calculated features, the sensitivity was 100% and specificity was 94.92% when mobile is in the pocket and; sensitivity was 100% and specificity was 93.61% when mobile is in handbag which is better than when the model was built using resultant features only. We have also studied the effect of individual features on this fall detection model and it is found that variance played a very important role to classify fall activities and ADL activities. Novelty - The proposed fall detection method is built using one-class classification so that the proposed model will be reliable to detect falls in real life. In the proposed work, the effect of features on the fall detection model is also analyzed.

Keywords: One class classification; accelerometer; outlier detection; fall detection; Bagging classifier

References

  3. Gia T, Tcarenko I, Sarker V, Rahmani A, Westerlund T, Tenhunen H. IoT-based fall detection system with energy efficient sensor nodes. NORCAS 2016 - 2nd IEEE NORCAS Conference. 2016.
  4. Yacchirema D, Puga JSD, Palau C, Esteve M. Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Personal and Ubiquitious compting. 2019;23:801–817. Available from: https://doi.org/10.1007/s00779-018-01196-8
  5. Mubashir M, Shao L, Seed L. A survey on fall detection: Principles and approaches. Neurocomputing. 2013;100:144–152. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0925231212003153
  6. Tax D. One-class classification. thesis
  7. Khan SS, Madden MG. A survey of recent trends in one class classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010;6206. Available from: https://doi.org/10.1007/978-3-642-17080-5_21
  8. Moya MM, Koch MW, Hostetler LD, Moya MM, Koch MW, Hostetler LD. One-class classifier networks for target recognition applications. STIN. 1993;93:24043. Available from: https://ui.adsabs.harvard.edu/abs/1993STIN...9324043M/abstract
  10. Bishop CM. Novelty detection and neural network validation. IEEE Proceding of confrence on Vision. 1994;141(4):217–222. Available from: https://ieeexplore.ieee.org/document/318023/
  13. Sarkar P, Sinha D. Application on Pervasive Computing in Healthcare - A Review. Indian Journal of Science and Technology. 2017;10(3):1–10. doi: 10.17485/ijst/2017/v10i3/110619
  14. Tolkiehn M, Atallah L, Lo B, Yang GZ. Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011;p. 369–372. Available from: https://pubmed.ncbi.nlm.nih.gov/22254325/
  15. Fang SH, Liang YC, Chiu KM. Developing a mobile phone-based fall detection system on Android platform. 2012 Computing, Communications and Applications Conference. 2012;p. 143–146. Available from: http://ieeexplore.ieee.org/document/6154019/
  17. Ryu JT, Moon BH. Fall Detection Algorithm based on Peaks of Voltage Measurements from the Accelerometer. Indian Journal of Science and Technology. 2016;9(45):1–6. doi: 10.17485/ijst/2016/v9i45/106768
  18. Prince PGK, Hemamalini R, Rajkumar RI. LabVIEW based Abnormal Muscular Movement and Fall Detection using MEMS Accelerometer during the Occurrence of Seizure. Indian Journal of Science and Technology. 2014;7(10):1625–1631. doi: 10.17485/ijst/2014/v7i10.9
  19. Albert MV, Kording K, Herrmann M, Jayaraman A. Fall Classification by Machine Learning Using Mobile Phones. Plos One. 2012;7(5):e36556. Available from: https://dx.plos.org/10.1371/journal.pone.0036556
  20. Shibuya N, Nukala BT, Rodriguez AI, Tsay J, Nguyen TQ, Zupancic S. A real-time fall detection system using a wearable gait analysis sensor and a Support Vector Machine (SVM) classifier. 2015 8th International Conference on Mobile Computing and Ubiquitous Networking. 2015;p. 66–67. Available from: https://ieeexplore.ieee.org/document/7061032
  21. Wang J, Zhang Z, Li B, Lee S, Sherratt R. An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Transaction on consumer electronics. 2014;60(1):23–29. Available from: https://ieeexplore.ieee.org/document/6780921
  22. Huynh QT, Tran BQ. Time-Frequency Analysis of Daily Activities for Fall Detection. Signals. 2021;2021(2):1–12. Available from: https://www.mdpi.com/2624-6120/2/1/1/htm
  23. Liu N, Zhang D, Su Z, Wang T. Preimpact Fall Detection for Elderly Based on Fractional Domain. Mathematical Problem in Engineering. 2021. Available from: https://www.hindawi.com/journals/mpe/2021/6661034/
  24. Panhwar M, Shah SMS, Shah SMZS, SMZAS, Chowdhry BS. Smart Phone Based Fall Detection using Auto Regression Modeling in a Non-Restrictive Setting. Indian Journal of Science and Technology. 2017;10(5):1–6. doi: 10.17485/ijst/2017/v10i5/111274
  25. Raghu G, Ponraj AS. Remote Safety Assistance and Health Monitoring System. Indian Journal of Science and Technology. 2016;9(45):1–6. doi: 10.17485/ijst/2016/v9i45/99611
  26. Madhubala JS, Umamakeswari A. A Vision based Fall Detection System for Elderly People. Indian Journal of Science and Technology. 2015;8(S9):1–9. doi: 10.17485/ijst/2015/v8iS9/65545
  27. Bilal M, Fergani B, Fleury A. Integrating Prior Knowledge in Weighted SVM for Human Activity Recognition in Smart Home. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017:233–239. Available from: https://link.springer.com/chapter/10.1007/978-3-319-66188-9_20
  28. Wan Q, Li Y, Li C, Pal R. Gesture recognition for smart home applications using portable radar sensors. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014;p. 6414–6417. doi: 10.1109/EMBC.2014.6945096
  29. Alegre F, Amehraye A, Evans N. Evans “A one-class classification approach to generalised speaker verification spoofing countermeasures using local binary patterns. IEEE 6th International Conference on Biometrics: Theory, Applications and Systems. 2013. Available from: https://ieeexplore.ieee.org/abstract/document/6712706/
  31. Wan M, Shang W, Zeng P. Double Behavior Characteristics for One-Class Classification Anomaly Detection in Networked Control Systems. IEEE transactions on Information Forensics and Security. 2017;12(12):3011–3023. Available from: https://ieeexplore.ieee.org/abstract/document/7987719/
  32. Krawczyk B, Filipczuk P. Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition. Engineering Application of Artificial Intelligence. 2014;31:126–135. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0952197613001917
  33. Kemmler M, Rodner E, Denzler J. One-class classification with gaussian processes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2011;p. 489–500. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0031320313002574
  34. Leng Q, Qi H, Miao J, Zhu W, Su G. One-class classification with extreme learning machine. Mathematical Problems in Engineering. 2015. Available from: https://www.hindawi.com/journals/mpe/2015/412957/abs/
  35. Medrano C, Igual R, Plaza I, Castro M. Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones. PLoS One. 2014;9(4):94811. Available from: https://dx.plos.org/10.1371/journal.pone.0094811
  36. Medrano C, Igual R, García-Magariño I, Plaza I, Azuara G. Combining novelty detectors to improve accelerometer-based fall detection. Medical and Biological Engineering and Computing. 2017;(10) 1849–1858. Available from: http://link.springer.com/10.1007/s11517-017-1632-z
  37. Micucci D, Mobilio M, Napoletano P, Tisato F. Falls as anomalies? An experimental evaluation using smartphone accelerometer data. Journal of Ambient Intelligent and Humanized Computing. 2017;8(1):87–99. Available from: http://link.springer.com/10.1007/s12652-015-0337-0
  38. Sheikh SY, Jilani MT. A ubiquitous wheelchair fall detection system using low-cost embedded inertial sensors and unsupervised one-class SVM. Journal of Ambient Intelligent and Humanized Computing. 2021;2021:1–16. Available from: https://link.springer.com/article/10.1007/s12652-021-03279-6
  39. Vavoulas G, Pediaditis M, Spanakis EG, Tsiknakis M. The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones. 13th IEEE International Conference on BioInformatics and BioEngineering. 2013. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0031320313002574
  40. Breiman L. Bagging predictors. Machine Learning. 1996;24(2):123–140. Available from: https://link.springer.com/article/10.1007/BF00058655
  41. Chang CC, Lin CJ. LIBSVM: A Library for support vector machines. ACM Transaction on Intelligent System and Technology. 2011;2(3). Available from: https://dl.acm.org/doi/abs/10.1145/1961189.1961199

Copyright

© 2021 Gupta & Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

  • 13 September 2021

Year: 2021, Volume: 14, Issue: 30

Article Metrics


Post Graduate Fellowship in Research Communication

More articles

DON'T MISS OUT!

Subscribe now for latest articles and news.