Abstract
Providing reliable information on human activities and behaviors is an extremely important goal in various application areas such as healthcare, entertainment, and security. Within the working environment, a correct identification of the actual performed tasks can provide an effective support in the assessment of the risk associated to the execution of the task itself, and thus preventing the development of work-related musculoskeletal diseases. In this perspective, wearable-based Human Activity Recognition systems have been representing a prominent application. This study aimed to compare three different classification approaches appointed from supervised learning techniques, namely k-Nearest Neighbors, Support Vector Machine and Decision Tree. Motion data, related to several working activities realized in the large-scale retail distribution, were collected by using a full-body system based on 17 Inertial Measurement Units (MVN Analyze, XSens). Reliable features in both time- and frequency-domain were first extracted from raw 3D accelerations and angular rates data, and further processed by Principal Component Analysis, with 95% threshold. The classification models were validated via 10-fold cross-validation on a defined training dataset. k-Nearest Neighbors classifier, which provide the best results on the training session, was eventually tested for generalization on additional data acquired on few specific tasks. As a result, considering 5 main macro activities, k-Nearest Neighbors provided a classification accuracy of 80.1% and a computational time of 1865.5 s. To test the whole assessment process, the activities labelled by the classification model as handling of low loads at high frequency were automatically evaluated for risk exposure via OCRA Checklist method.
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John Dian, F., Vahidnia, R., Rahmati, A.: Wearables and the Internet of Things (IoT), applications, opportunities, and challenges: a survey. IEEE Access 2020(8), 69200–69211 (2020). https://doi.org/10.1109/ACCESS.2020.2986329
Kumari, P., Mathew, L., Syal, P.: Increasing trend of wearables and multimodal interface for human activity monitoring: a review. Biosens. Bioelectron. 90, 298–307 (2017). https://doi.org/10.1016/j.bios.2016.12.001
Rast, F.M., Labruyère, R.: Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. J. NeuroEng. Rehabil. 17(1), 1–19 (2020). https://doi.org/10.1186/s12984-020-00779-y
Baig, M.M., Afifi, S., Gholam Hosseini, H., Mirza, F.: A systematic review of wearable sensors and IoT-based monitoring applications for older adults–a focus on ageing population and independent living. J. Med. Syst. 43(8), 1–11 (2019). https://doi.org/10.1007/s10916-019-1365-7
Wang, Y., Cang, S., Yu, H.: A survey on wearable sensor modality centred human activity recognition in health care. Expert Syst. Appl. 137, 167–190 (2019). https://doi.org/10.1016/j.eswa.2019.04.057
Raval, R.M., Prajapati, H.B., Dabhi, V.K.: Survey and analysis of human activity recognition in surveillance videos. Intell. Decision Technol. 13(2), 271–294 (2019). https://doi.org/10.3233/IDT-170035
Meng, Z., Zhang, M., Guo, C., Fan, Q., Zhang, H., Gao, N., et al.: Recent progress in sensing and computing techniques for human activity recognition and motion analysis. Electronics 9, 1357 (2020). https://doi.org/10.3390/electronics9091357
Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A.: A robust human activity recognition system using smartphone sensors and deep learning. Futur. Gener. Comput. Syst. 81, 307–313 (2017). https://doi.org/10.1016/j.future.2017.11.029
Yurtman, A., Barshan, B.: Activity recognition invariant to sensor orientation with wearable motion sensors. Sensors 17, 1838 (2017). https://doi.org/10.3390/s17081838
Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Sens. J. 15, 1192–1209 (2013). https://doi.org/10.1109/SURV.2012.110112.00192
Demrozi, F., Pravadelli, G., Bihorac, A., Rashidi, P.: Human activity recognition using inertial, physiological and environmental sensors: a comprehensive survey. IEEE Sens. J. 8, 210816–210836 (2020). https://doi.org/10.1109/ACCESS.2020.3037715
Yuan, G., Wang, Z., Meng, F., Yan, Q., Xia, S.: An overview of human activity recognition based on smartphone. IEEE Sens. J. 39, 288–306 (2019). https://doi.org/10.1108/SR-11-2017-0245
Lopez-Nava, I.H., Munoz-Melendez, A.: Wearable inertial sensors for human motion analysis: a review. IEEE Sens. J. 16, 7821–7834 (2016). https://doi.org/10.1109/JSEN.2016.2609392
Sztyle, T., Stuckenschmidt, H., Petrich, W.: Position-aware activity recognition with wearable devices. Pervasive Mob. Comput. 38, 281–295 (2017). https://doi.org/10.1016/j.pmcj.2017.01.008
Ranavolo, A., Draicchio, F., Varrecchia, T., Silvetti, A., Iavicoli, S.: Wearable monitoring devices for biomechanical risk assessment at work: current status and future challenges—a systematic review. Int. J. Environ. Res. Public Health 15, 2001 (2018). https://doi.org/10.3390/ijerph15092001
Lenzi, S.E., Standoli, C.E., Andreoni, G., Perego, P., Lopomo, N.F.: Comparison among standard method, dedicated toolbox and kinematic-based approach in assessing risk of developing upper limb musculoskeletal disorders. In: Ahram, T.Z. (ed.) Advances in Human Factors in Wearable Technologies and Game Design. Advances in Intelligent Systems and Computing, vol. 795, pp. 135–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94619-1_13
ISO. 11228–1:2021. Ergonomics—Manual handling—Part 1: Lifting, lowering and carrying. International Organization for Standardization. Geneva, Switzerland (2021)
ISO. 11228–2:2007. Ergonomics—Manual handling—Part 2: Pushing and pulling. International Organization for Standardization. Geneva, Switzerland (2007)
ISO. 11228–3:2007. Ergonomics—Manual handling—Part 3: Handling of low loads at high frequency. International Organization for Standardization. Geneva, Switzerland (2007)
Colombini, D., Occhipinti, E.: The OCRA method (OCRA index and checklist). updates with special focus on multitask analysis. In: Karkwoski, W., Salvendy, G. (eds.) Conference Proceedings. AHFE 2008 Las Vegas, July 2008. ISBN 978–1- 60643–712–4 (2008)
Standoli, C.E., Lenzi, S.E., Lopomo, N.F., Perego, P., Andreoni, G.: The evaluation of existing large-scale retailers’ furniture using DHM. In: Proceedings of the Congress of the International Ergonomics Association, Florence, Italy, August 2018. Springer, Cham (2018). eBook ISBN 978-3-319-96080-7, https://doi.org/10.1007/978-3-319-96080-7
Lenzi, S.E., Standoli, C.E., Andreoni, G., Perego, P., Lopomo, N.F.: A software toolbox to improve time-efficiency and reliability of an observational risk assessment method. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). Advances in Intelligent Systems and Computing, vol. 820, pp. 689–708. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96083-8_86
Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. IEEE Sens. J. 15, 31314–31338 (2015). https://doi.org/10.3390/s151229858
Zhang, M., Sawchuk, A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th International ICST Conference Body Area Networks (2011). https://doi.org/10.4108/icst.bodynets.2011.247018
Sarcevic, P., Pletl, S., Kincses, Z.: Comparison of time-and frequency-domain features for movement classification using data from wrist-worn sensors. In: 2 EEE 15th International Symposium on Intelligent Systems and Informatics (SISY) (2017). https://doi.org/10.1109/SISY.2017.8080564
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2008). https://doi.org/10.1007/s10115-007-0114-2
Garcia, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012). https://doi.org/10.1109/TPAMI.2011.142
Garcia-Ceja, E., Brena, R.F.: An improved three-stage classifier for activity recognition. Int. J. Pattern Recognit. Artif. Intell. 32(01), 1860003 (2018). https://doi.org/10.1142/S0218001418600030
Acknowledgement
Prof. Lopomo and all the authors would like to thank Simone Bertè for the efforts he realized in the preliminary analysis of the data, which defined the basis for this work.
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Andreoni, G., Cassiolas, G., Standoli, C.E., Lenzi, S.E., Perego, P., Lopomo, N.F. (2022). Automatic Classification of Working Activities for Risk Assessment in Large-Scale Retail Distribution by Using Wearable Sensors: A Preliminary Analysis. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design. HCII 2022. Lecture Notes in Computer Science, vol 13320. Springer, Cham. https://doi.org/10.1007/978-3-031-06018-2_10
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