Abstract
How people walk in natural conditions often reveals key insights into health, quality of life and independence. However, topological properties of the environment in which walking takes place should be taken into account for accurate and reliable gait assessments. Here, to begin to tackle this problem a novel smartphone-based gait assessment framework is introduced, and the implementation and performance analysis of two of its data processing modules are described. The first module employs a fuzzy rough nearest neighbour classifier for automatic labelling of walking environments. Tested on a publicly available dataset, the fuzzy classifier reached up to 82% classification accuracy outperforming other state-of-the-art classical machine learning methods and matching deep neural networks reported in the literature. The second module employs a novel algorithm to detect steps and extract temporal gait parameters (such as stride time, double support time and step variability) from heel strike and toe off time points. Tested on a new dataset collected for this study, the algorithm successfully detected more than 97% of the steps and estimated gait parameters with high accuracy and precision matching with the performance levels achieved in controlled laboratory trials.
M. T. Bunker and A. Sher—Contributed to this work equally.
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References
Tao, W., Liu, T., Zheng, R., Feng, H.: Gait analysis using wearable sensors. Sensors 12(2), 2255–2283 (2012)
Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., Savatier, X.: A study of vicon system positioning performance. Sensors 17(7), 1591 (2017)
Cleland, B.T., Arshad, H., Madhavan, S.: Concurrent validity of the GAITRite electronic walkway and the 10-m walk test for measurement of walking speed after stroke. Gait Posture 68, 458–460 (2019)
Bei, S., Zhen, Z., Xing, Z., Taocheng, L., Qin, L.: Movement disorder detection via adaptively fused gait analysis based on kinect sensors. IEEE Sens. J. 18(17), 7305–7314 (2018)
Tarashansky, A., Vathsangam, H., Sukhatme, G.S.: A study of position independent algorithms for phone-based gait frequency detection. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5984–5987. IEEE, August 2014
Weiss, A., Sharifi, S., Plotnik, M., van Vugt, J.P., Giladi, N., Hausdorff, J.M.: Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil. Neural Repair 25(9), 810–818 (2011)
Kowalsky, D.B., Rebula, J.R., Ojeda, L.V., Adamczyk, P.G., Kuo, A.D.: Human walking in the real world: interactions between terrain type, gait parameters, and energy expenditure. PloS One 16(1), e0228682 (2021)
Yang, M., Zheng, H., Wang, H., McClean, S., Harris, N.: Assessing the utility of smart mobile phones in gait pattern analysis. Health Technol. 2(1), 81–88 (2012). https://doi.org/10.1007/s12553-012-0021-8
Luo, Y., Zheng, H., Chen, Y., Giang, W.C., Hu, B.: Influences of smartphone operation on gait and posture during outdoor walking task. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 64, no. 1, pp. 1723–1727. SAGE Publications, Los Angeles, December 2020
Avvenuti, M., Carbonaro, N., Cimino, M.G., Cola, G., Tognetti, A., Vaglini, G.: Smart shoe-assisted evaluation of using a single trunk/pocket-worn accelerometer to detect gait phases. Sensors 18(11), 3811 (2018)
Manor, B., et al.: Smartphone app-based assessment of gait during normal and dual-task walking: demonstration of validity and reliability. JMIR mHealth uHealth 6(1), e36 (2018)
Zhong, R., Rau, P.L.P.: A mobile phone-based gait assessment app for the elderly: development and evaluation. JMIR mHealth uHealth 8(5), e14453 (2020)
Silsupadol, P., Prupetkaew, P., Kamnardsiri, T., Lugade, V.: Smartphone-based assessment of gait during straight walking, turning, and walking speed modulation in laboratory and free-living environments. IEEE J. Biomed. Health Inform. 24(4), 1188–1195 (2019)
Khandelwal, S., Wickström, N.: Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database. Gait Posture 51, 84–90 (2017)
Tawaki, Y., Nishimura, T., Murakami, T.: Monitoring of gait features during outdoor walking by simple foot mounted IMU system. In: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, pp. 3413–3418. IEEE, October 2020
Iluz, T., et al.: Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting. J. Neuroeng. Rehabil. 11(1), 1–9 (2014)
Luo, Y., Coppola, S.M., Dixon, P.C., Li, S., Dennerlein, J.T., Hu, B.: A database of human gait performance on irregular and uneven surfaces collected by wearable sensors. Sci. Data 7(1), 1–9 (2020)
Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2008)
Sher, A., et al.: Automatic gait analysis during steady and unsteady walking using a smartphone. Under review (2021)
Hu, B., Li, S., Chen, Y., Kavi, R., Coppola, S.: Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world. Appl. Ergon. 96, 103414 (2021)
Del Din, S., Godfrey, A., Rochester, L.: Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson’s disease: toward clinical and at home use. IEEE J. Biomed. Health Inform. 20(3), 838–847 (2015)
Bilney, B., Morris, M., Webster, K.: Concurrent related validity of the GAITRite® walkway system for quantification of the spatial and temporal parameters of gait. Gait Posture 17(1), 68–74 (2003)
Jensen, R., Cornelis, C.: Fuzzy-rough nearest neighbour classification and prediction. Theoret. Comput. Sci. 412(42), 5871–5884 (2011)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)
Frank, E., Hall, M.A., Witten, I.H.: The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Burlington (2016)
Acknowledgements
This work is supported by Aberystwyth University (Presidential PhD scholarship to AS), Welsh Government (S\(\hat{e}\)r Cymru Cofund Fellowship, grant No. 663830-AU167, to OA, and S\(\hat{e}\)r Cymru Tackling Covid-19, grant No. 009 to FVP and OA, Health and Care Research Wales (PhD Studentship to FVP and OA), Public Health Wales - Stroke Implementation Group (Stroke Research, Innovation and Education fund to FVP and OA) and European Commission (H2020-MSCA-RISE-2019, grant No. 873178, to OA). We also thank Joy Welch Educational Charitable Trust, James Pantyfedwen Foundation and German Academic Exchange Service RISE Worldwide program for their financial support.
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Bunker, M.T., Sher, A., Akpokodje, V., Villagra, F., Parthaláin, N.M., Akanyeti, O. (2022). Towards Fuzzy Context-Aware Automatic Gait Assessments in Free-Living Environments. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_41
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