Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques

Abstract

Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monitoring, the development of an intelligent anomaly detection system is proposed. Unlike other authors, where they use supervised techniques, this work proposes using unsupervised techniques due to the advantages they offer. These advantages include the lack of prior labeling of data, and the detection of anomalies previously not contemplated, among others. In the present work, an individualized methodology consisting of two phases is developed: characterizing the normal sitting pattern and determining abnormal samples. An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis. It can be concluded, among other aspects, that the utilization of dimensionality reduction techniques leads to improved results. Moreover, the normality characterization phase is deemed necessary for enhancing the system's learning capabilities. Additionally, employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.

Keywords

Sitting posture monitoring / Anomaly detection / Assistive technology / Pressure sensors / Unsupervised techniques / Individualization / Wheelchair

CRediT authorship contribution statement

Patrick Vermander: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Aitziber Mancisidor: Writing - review & editing, Writing - original draft, Supervision, Investigation, Funding acquisition, Formal analysis, Conceptualization. Raffaele Gravina: Writing - review & editing, Writing - original draft, Supervision, Investigation, Funding acquisition, Formal analysis. Itziar Cabanes: Writing - review & editing, Writing - original draft, Supervision, Project administration, Investigation, Funding acquisition, Formal analysis, Conceptualization. Giancarlo Fortino: Writing - review & editing, Writing - original draft, Supervision, Project administration, Investigation, Funding acquisition, Formal analysis.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work has been funded by: FEDER/Ministry of Science and Innovation - State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033, the Basque Government, IT1726-22, as well as by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Government. The work has also been partially supported by the Italian MIUR, PRIN 2020 Project “COMMON-WEARS”, N.2020HCWWLP, CUP: H23C22000230005; we also acknowledge co-funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, through the Italian MUR, PRIN 2022 Project ”COCOWEARS” (A framework for COntinuum COmputing WEARable Systems), N. 2022T2XNJE, CUP: H53D23003640006.

The authors would like to thank the Coordinating Federation of People with Physical and/or Organic Disabilities of Bizkaia (FEKOOR) and its health specialists, for their health advice throughout all phases of the development of this work.

References

[1] V. Costa, C. Sánchez, L. Perea, E. Rocon, A. Otero, R. Raya, Redesign based on clinical experience of a robotic walker for hip fracture rehabilitation, Rev. Iberoam. Autom. Inform. Ind. 20 (2023) 247-258.

[2] W.H. Organization, Wheelchair Provision Guidelines, World Health Organization, 2023.

[3] A.M. Koontz, D. Ding, Y.-K. Jan, S. De Groot, A. Hansen, Wheeled Mobility, 2015.

[4] S. Chopra, M. Kumar, S. Sood,Wearable posture detection and alert system, in: 2016 International Conference System Modeling & Advancement in Research Trends (SMART), IEEE, 2016, pp. 130-134.

[5] I. Kister, E. Chamot, A.R. Salter, G.R. Cutter, T.E. Bacon, J. Herbert, Disability in multiple sclerosis: a reference for patients and clinicians, Neurology 80 (11) (2013) 1018-1024.

[6] I.J. van Nes, B. Nienhuis, H. Latour, A.C. Geurts, Posturographic assessment of sit-ting balance recovery in the subacute phase of stroke, Gait Posture 28 (3) (2008) 507-512.

[7] B. Reuter, C. Gumbinger, T. Sauer, H. Wiethölter, I. Bruder, C. Diehm, P.A. Ringleb, W. Hacke, M.G. Hennerici, R. Kern, et al., Access, timing and frequency of very early stroke rehabilitation-insights from the Baden-Wuerttemberg stroke registry, BMC Neurol. 16 (2016) 1-10.

[8] A. Rghioui, J. Lloret, A. Oumnad, , Big data classification and Internet of things in healthcare, Int. J. E-Health Med. Commun. 11 (2) (2020) 20-37.

[9] M. Mock, K. Sweeting, Gait and posture-assessment in general practice, Aust. Fam. Phys. 36 (2007) 398-404.

[10] C.-W. Chien, J.-H. Lin, C.-H. Wang, I.-P. Hsueh, C.-F. Sheu, C.-L. Hsieh, Developing a short form of the postural assessment scale for people with stroke, Neurorehabil. Neural Repair 21 (1) (2007) 81-90.

[11] T. Klishkovskaia, A. Aksenov, A. Sinitca, A. Zamansky, O.A. Markelov, D. Kaplun, Development of classification algorithms for the detection of postures using non-marker-based motion capture systems, Appl. Sci. (Switzerland) 10 (2020) 4028-4043.

[12] X. Zhang, J. Fan, T. Peng, P. Zheng, C.K. Lee, R. Tang, A privacy-preserving and unobtrusive sitting posture recognition system via pressure array sensor and infrared array sensor for office workers, Adv. Eng. Inform. 53 (2022) 101690-101699.

[13] F. Tlili, R. Haddad, R. Bouallegue, R. Shubair, Design and architecture of smart belt for real time posture monitoring, Internet Things 17 (2022) 100472-100484.

[14] U. Jayasinghe, B. Janko, F. Hwang, W.S. Harwin, Classification of static postures with wearable sensors mounted on loose clothing, Sci. Rep. 13 (2023) 131-143.

[15] M.S. Mohammed, S. Sendra, J. Lloret, I. Bosch, et al., Systems and wbans for con-trolling obesity, J. Healthc. Eng. (2018) 1564748.

[16] C. Tavares, J. Silva, A. Mendes, L. Rebolo, Fatima Domingues, et al., Smart office chair for working conditions optimization, IEEE Access 11 (2023) 50497-50509.

[17] Z. Fan, X. Hu, W.-M. Chen, D.-W. Zhang, X. Ma,A deep learning based 2-dimensional hip pressure signals analysis method for sitting posture recognition, Biomed. Signal Process. Control 73 (2022) 103432-103440.

[18] L. Pereira, H. Plácido da Silva, A novel smart chair system for posture classification and invisible ecg monitoring, Sensors 23 (2023) 719-752.

[19] Y. Zhang, Y. Chen, J. Wang, Z. Pan, Unsupervised deep anomaly detection for multi-sensor time-series signals, IEEE Trans. Knowl. Data Eng. 35 (2021) 2118-2132.

[20] G. Fortino, A. Guzzo, M. Ianni, F. Leotta, M. Mecella, Predicting activities of daily living via temporal point processes: approaches and experimental results, Comput. Electr. Eng. 96 (2021) 107567-107579.

[21] T.-H. Tran, T.-L. Le, V.-N. Hoang, H. Vu, Continuous detection of human fall using multimodal features from kinect sensors in scalable environment, Comput. Methods Programs Biomed. 146 (2017) 151-165.

[22] N.R. Prasad, S. Almanza-Garcia, T.T. Lu, Anomaly detection, Comput. Mater. Con-tinua 14 (1) (2009) 1-22.

[23] D. Bibbo, M. Carli, S. Conforto, F. Battisti, A sitting posture monitoring instrument to assess different levels of cognitive engagement, Sensors 19 (3) (2019) 455-476.

[24] M.-C. Tsai, E.T.-H. Chu, C.-R. Lee, An automated sitting posture recognition system utilizing pressure sensors, Sensors 23 (2023) 5894-5915.

[25] J. Arshad, M.A. Ashraf, H.M. Asim, N. Rasool, M.H. Jaffery, S.I. Bhatti, Multi-mode electric wheelchair with health monitoring and posture detection using machine learning techniques, Electronics 12 (2023) 1132-1151.

[26] C. Ma, W. Li, J. Cao, J. Du, Q. Li, R. Gravina, Adaptive sliding window based activity recognition for assisted livings, Inf. Fusion 53 ( 2020) 55-65.

[27] C. Ma, J. Du, R. Gravina, Abnormal behavior detection based on activity level using fuzzy inference system for wheelchair users, Hum.-Cent. Comput. Inf. Sci. 12 (2022) 21-38.

[28] G. Liu, X. Li, C. Xu, L. Ma, H. Li, Fmcw radar-based human sitting posture detection, IEEE Access 11 (2023) 102746-102756.

[29] S.Y. Sheikh, M.T. Jilani, A ubiquitous wheelchair fall detection system using low-cost embedded inertial sensors and unsupervised one-class svm, J. Ambient Intell. Humaniz. Comput. 14 (2023) 147-162.

[30] J. Otamendi, A. Zubizarreta, E. Portillo, Machine learning-based gait anomaly de-tection using a sensorized tip: an individualized approach, Neural Comput. Appl. 35 (2023) 17443-17459.

[31] A.B. Nassif, M.A. Talib, Q. Nasir, F.M. Dakalbab, Machine learning for anomaly detection: a systematic review, IEEE Access 9 (2021) 78658-78700.

[32] G. Duan, H. Lv, H. Wang, G. Feng, Application of a dynamic line graph neural net-work for intrusion detection with semisupervised learning, IEEE Trans. Inf. Forensics Secur. 18 (2022) 699-714.

[33] M. Tariq, H. Majeed, M.O. Beg, F.A. Khan, A. Derhab, Accurate detection of sitting posture activities in a secure IoT based assisted living environment, Future Gener. Comput. Syst. 92 (2019) 745-757.

[34] Z.A. Khan, W. Sohn, Abnormal human activity recognition system based on r-transform and kernel discriminant technique for elderly home care, IEEE Trans. Consum. Electron. 57 (2011) 1843-1850.

[35] K.R. Kini, F. Harrou, M. Madakyaru, F. Kadri, Y. Sun, Automatic detection of unbal-anced sitting postures in wheelchairs using unlabeled sensor data, IEEE Sens. Lett. 6 (8) (2022) 1-4.

[36] M.S. Parwez, D.B. Rawat, M. Garuba, Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network, IEEE Trans. Ind. Inform. 13 (2017) 2058-2065.

[37] S. Shriram, E. Sivasankar,Anomaly detection on shuttle data using unsupervised learning techniques, in: 2019 International Conference on Computational Intelli-gence and Knowledge Economy (ICCIKE), IEEE, 2019, pp. 221-225.

[38] K.M. Ting, T. Washio, J.R. Wells, S. Aryal, Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors, Mach. Learn. 106 (2017) 55-91.

[39] L. Deecke, R. Vandermeulen, L. Ruff, S. Mandt, M. Kloft,Image anomaly detec-tion with generative adversarial networks, in:Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I 18, Springer, 2019, pp. 3-17.

[40] P. Vermander, A. Mancisidor, I. Cabanes, N. Perez, J. Torres-Unda, Intelligent sitting posture classifier for wheelchair users, IEEE Trans. Neural Syst. Rehabil. Eng. 31 (2023) 944-953.

[41] N. Perez, P. Vermander, E. Lara, A. Mancisidor, I. Cabanes, Sitting posture monitor-ing device for people with low degree of autonomy, in: International Conference on NeuroRehabilitation, Springer, 2020, pp. 305-310.

[42] R. Aziz, C. Verma, N. Srivastava, Dimension reduction methods for microarray data: a review, AIMS Bioeng. 4 (1) (2017) 179-197.

[43] S. Na, L. Xumin, G. Yong,Research on k-means clustering algorithm: an improved k-means clustering algorithm, in: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, IEEE, 2010, pp. 63-67.

[44] P. Vermander, A. Mancisidor, G. Fortino, I. Cabanes, R. Gravina,Unsupervised learning-based methodology for detection of postural anomalies in wheelchair users, in: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023, pp. 1443-1448.

[45] Z.G. Prodanoff, A. Penkunas, P. Kreidl,Anomaly detection in rfid networks using Bayesian blocks and dbscan, in: 2020 SoutheastCon, IEEE, 2020, pp. 1-7.

[46] P. Mangiameli, S.K. Chen, D. West, A comparison of som neural network and hier-archical clustering methods, Eur. J. Oper. Res. 93 (2) (1996) 402-417.

[47] M. Sakurada, T. Yairi,Anomaly detection using autoencoders with nonlinear dimen-sionality reduction, in:Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, 2014, pp. 4-11.

[48] H.R. Vega-Gutierrez, C. Castorena, R. Alejo, E.E. Granda-Gutierrez, Comparative study of methods to obtain the number of hidden neurons of an auto-encoder in a high-dimensionality context, IEEE Lat. Am. Trans. 18 (12) (2020) 2196-2203.

[49] Y. Ji, H. Lee, Event-based anomaly detection using a one-class svm for a hybrid electric vehicle, IEEE Trans. Veh. Technol. 71 (6) (2022) 6032-6043.

[50] T. Kurita, Principal Component Analysis (PCA), Springer, 2019.