Abstract
Lower back pain problems are increasing these days because of a sedentary lifestyle and working habits such as using laptops or computers for long hours and sitting on chairs for continuous duration. Back injury occurrence is also very common in athletes and workers where lifting of heavyweights are required. This research work aims to classify subjects and analyze discomfort at lower back by performing wireless data acquisition using accelerometer sensor. The designed hardware consisting of accelerometer sensor, NRF wireless module, and micro-controller was used to implement node-hub architecture. This research work shows the classification of subjects based on lower back vibration data. Multiple classification algorithms such as decision trees, random forest, naive Bayes, and support vector machine were applied to perform subject classification after performing experiments. The analysis of data shows that the subjects could be classified based on the discomfort level of the lower back using accelerometer data. Such a kind of study could be used for the prediction of the core strength of lower back and treatment of lower back problems by analyzing vibrational unrest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
V. Oliverio, O.B. Poli-Neto, Case study: classification algorithms comparison for the multi-label problem of chronic pelvic pain diagnosing, in IEEE 33rd International Conference on Data Engineering, pp. 15071509 (2017)
S. Taghvaei, Y. Hirata, K. Kosuge, Visual human action classification for control of a passive walker, in IEEE 7th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO) (2017), p. 15
D. Urukalo, P. Blazevic, S. Charles, J.-P. Carta, The TeachWear healthcare wearable device, in 27th IEEE International Symposium on Robot and Human Interactive Communication (2018), pp. 638–643
W. Du, O.M. Omisore, H. Li, K. Ivanov, S. Han, L. Wang, Recognition of chronic low back pain during lumbar spine movements based on surface electromyography signals. IEEE Access 6, 65027–65042 (2018)
M. Molnar, M. Kok, T. Engel, H. Kaplick, F. Mayer, T. Seel, A method for lower back motion assessment using wearable 6D inertial sensors, in 21st IEEE International Conference on Information Fusion (FUSION) (2018), p. 799806
W. Xu, C. Ortega-Sanchez, I. Murray, Measuring human joint movement with IMUs, in 15th IEEE Student Conference on Research and Development (2018), pp. 172–177
O. Chutatape, K. Naonueng, R. Phoophuangpairoj, Detection of improper postures leading to dislocation of hip prosthesis by a smartphone, in 14th IEEE International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (2017), pp. 415–418
W. Kam, K. O’Sullivan, W.S. Mohammed, E. Lewis, Low cost portable sensor for real-time monitoring of lower back bending, in 25th IEEE International Conference on Optical Fiber Sensors (2017)
D.-M. Dobrea, M.-C. Dobrea, A warning wearable system used to identify poor body postures, in IEEE International Conference on Advances in Wireless and Optical Communications (2018), pp. 55–60
G.A. Sandag, N.E. Tedry, S. Lolong, Classification of lower back pain using K-Nearest Neighbor algorithm, in 6th IEEE International Conference on Cyber and IT Service Management (CITSM 2018) (2018), pp. 1–5
F. Galbusera, G. Casaroli, T. Bassani, Artificial intelligence and machine learning in spine research. JOR Spine, Jan 2019
G. Shabrina, B.M. Iqbal, D.H. Syaifullah, Effect of shoes on lower extremity pain and low back pain during prolonged standing on a sloping medium, in IEEE International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), vol. 3 (2018), pp. 181–187
T. Nezam, R. Boostani, V. Abootalebi, K. Rastegar, A novel classification strategy to distinguish five levels of pain using the EEG signal features. IEEE Trans. Affect. Comput. 1–9 (2018)
A.A. Abdullah, A. Yaakob, Z. Ibrahim, Prediction of spinal abnormalities using machine learning techniques, in IEEE International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) (2018), pp. 1–6
J. Estrada, L. Vea, Sitting posture recognition for computer users using smartphones and a web camera, in IEEE Region 10 Conference (TENCON), pp. 1520–1525 (2017)
Y. Tian, J. Zhang, L. Chen, Y. Geng, X. Wang, Single wearable accelerometer-based human activity recognition via kernel discriminant analysis and QPSO-KELM classifier. IEEE Access 7, 109216–109227 (2019)
K. Ikegami, H. Hirata, H. Ishihara, S. Guo, Development of a chair preventing low back pain with sitting person doing hand working at the same time, in IEEE international conference on mechatronics and automation (2018), pp. 1760–1764
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chowdhary, R.S., Basu, M. (2021). Discomfort Analysis at Lower Back and Classification of Subjects Using Accelerometer. In: Dash, S.S., Das, S., Panigrahi, B.K. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 1172. Springer, Singapore. https://doi.org/10.1007/978-981-15-5566-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-5566-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5565-7
Online ISBN: 978-981-15-5566-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)