Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.

Biosignal Processing and Computational NeuroScience Lab Institute of Space Technology Pakistan

Department of Biomedical Engineering The Chinese University of Hong Kong Hong Kong

Faculty of Information Technology Brno University of Technology Brno Czech Republic

Institute of Biomedical Technologies Auckland University of Technology Auckland New Zealand

NewVative Technologies Ltd Islamabad Pakistan

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Feng J., Wick J., Bompiani E., Aiona M. Applications of gait analysis in pediatric orthopaedics. Curr. Orthop. Pract. 2016;27(4):455–464.

Saboor A., Kask T., Kuusik A., Alam M.M., Le Moullec Y., Niazi I.K., Zoha A., Ahmad R. Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review. IEEE Access. 2020;8:167 830–167 864.

Ravi D.K., Gwerder M., Ignasiak N.K., Baumann C.R., Uhl M., Van Dieen W., Taylor Jaap H., Singh N.B. Revealing the optimal thresholds for movement performance: a systematic review and meta-analysis to benchmark pathological walking behaviour. Neurosci. Biobehav. Rev. 2020;108:24–33. PubMed

König N., Singh N.B., Von Beckerath J., Janke L., Taylor W.R. Is gait variability reliable? An assessment of spatio-temporal parameters of gait variability during continuous overground walking. Gait Posture. 2014;39(1):615–617. PubMed

Taborri J., Palermo E., Rossi S., Cappa P. Gait partitioning methods: a systematic review. Sensors. 2016;16(1):66. PubMed PMC

Catalfamo P., Ghoussayni S., Ewins D. Gait event detection on level ground and incline walking using a rate gyroscope. Sensors. 2010;10(6):5683–5702. PubMed PMC

Sabatini A.M., Martelloni C., Scapellato S., Cavallo F. Assessment of walking features from foot inertial sensing. IEEE Trans. Biomed. Eng. 2005;52(3):486–494. PubMed

Alahakone A.U., Senanayake S.A., Senanayake C.M. 2010 IEEE Asia Pacific Conference on Circuits and Systems. IEEE; 2010. Smart wearable device for real time gait event detection during running; pp. 612–615.

Prasanth H., Caban M., Keller U., Courtine G., Ijspeert A., Vallery H., Von Zitzewitz J. Wearable sensor-based real-time gait detection: a systematic review. Sensors. 2021;21(8):2727. PubMed PMC

Bejarano N.C., Ambrosini E., Pedrocchi A., Ferrigno G., Monticone M., Ferrante S. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. Springer; 2014. An adaptive real-time algorithm to detect gait events using inertial sensors; pp. 1799–1802.

Chia Bejarano N., Ambrosini E., Pedrocchi A., Ferrigno G., Monticone M., Ferrante S. A novel adaptive, real-time algorithm to detect gait events from wearable sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 2015;23(3):413–422. PubMed

Azhar M.A., Gouwanda D., Gopalai A.A. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) IEEE; 2014. Development of an intelligent real-time heuristic-based algorithm to identify human gait events; pp. 573–576.

Formento P.C., Acevedo R., Ghoussayni S., Ewins D. Gait event detection during stair walking using a rate gyroscope. Sensors. 2014;14(3):5470–5485. PubMed PMC

Rueterbories J., Spaich E.G., Andersen O.K. Gait event detection for use in fes rehabilitation by radial and tangential foot accelerations. Med. Eng. Phys. 2014;36(4):502–508. PubMed

Aung M.S., Thies S.B., Kenney L.P., Howard D., Selles R.W., Findlow A.H., Goulermas J.Y. Automated detection of instantaneous gait events using time frequency analysis and manifold embedding. IEEE Trans. Neural Syst. Rehabil. Eng. 2013;21(6):908–916. PubMed

González R.C., López A.M., Rodriguez-Uría J., Alvarez D., Alvarez J.C. Real-time gait event detection for normal subjects from lower trunk accelerations. Gait Posture. 2010;31(3):322–325. PubMed

Abaid N., Cappa P., Palermo E., Petrarca M., Porfiri M. Gait detection in children with and without hemiplegia using single-axis wearable gyroscopes. PLoS ONE. 2013;8(9) PubMed PMC

Diao Z., Quan H., Lan L., Han Y. 2013 Seventh International Conference on Sensing Technology (ICST) IEEE; 2013. Analysis and compensation of mems gyroscope drift; pp. 592–596.

Zhao H., Wang Z., Qiu S., Wang J., Xu F., Wang Z., Shen Y. Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion. Inf. Fusion. 2019;52:157–166.

Negi S., Garg K., Prajapat M., Sharma N. A standalone real-time gait phase detection using fuzzy-logic implementation in Arduino nano. SN Comput. Sci. 2022;3(1):1–7. PubMed

Pappas I., Popovic M., Keller T., Dietz V., Morari M. A reliable gait phase detection system. IEEE Trans. Neural Syst. Rehabil. Eng. 2001;9(2):113–125. PubMed

Lee S.-W., Mase K., Kogure K. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE; 2006. Detection of spatio-temporal gait parameters by using wearable motion sensors; pp. 6836–6839. PubMed

Hanlon M., Anderson R. Real-time gait event detection using wearable sensors. Gait Posture. 2009;30(4):523–527. PubMed

Jasiewicz J.M., Allum J.H., Middleton J.W., Barriskill A., Condie P., Purcell B., Li R.C.T. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. Gait Posture. 2006;24(4):502–509. PubMed

Figueiredo J., Felix P., Costa L., Moreno J.C., Santos C.P. Gait event detection in controlled and real-life situations: repeated measures from healthy subjects. IEEE Trans. Neural Syst. Rehabil. Eng. 2018;26(10):1945–1956. PubMed

Tiwari A., Joshi D. An infrared sensor-based instrumented shoe for gait events detection on different terrains and transitions. IEEE Sens. J. 2020;20(18):10 779–10 791.

Godiyal A.K., Verma H.K., Khanna N., Joshi D. A force myography-based system for gait event detection in overground and ramp walking. IEEE Trans. Instrum. Meas. 2018;67(10):2314–2323.

Song M., Kim J. An ambulatory gait monitoring system with activity classification and gait parameter calculation based on a single foot inertial sensor. IEEE Trans. Biomed. Eng. 2017;65(4):885–893. PubMed

Nguyen N.D., Bui D.T., Truong P.H., Jeong G.-M. Classification of five ambulatory activities regarding stair and incline walking using smart shoes. IEEE Sens. J. 2018;18(13):5422–5428.

Chen M., Yan J., Xu Y. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2009. Gait pattern classification with integrated shoes; pp. 833–839.

Catalfamo Formento P.A.L., Acevedo R.C., Ghoussayni S., Ewins D. 2014. Gait event detection on stair walking using a rate gyroscope. PubMed PMC

Ledoux E.D. Inertial sensing for gait event detection and transfemoral prosthesis control strategy. IEEE Trans. Biomed. Eng. 2018;65(12):2704–2712. PubMed

Das R., Hooda N., Kumar N. A novel approach for real-time gait events detection using developed wireless foot sensor module. IEEE Sens. Lett. 2019;3(6):1–4.

Chen M., Yan J., Xu Y. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2009. Gait pattern classification with integrated shoes; pp. 833–839.

https://cdn.sparkfun.com/assets/c/4/6/8/b/2010-10-26-DataSheet-FSR406-Layout2.pdf Force sensitive resister, fsr 406, [Online]. Available:

Farahbod F. Telemetryviewer. http://www.farrellf.com/TelemetryViewer/ [Online]. Available:

GitHub Github tockn/mpu-6050 Arduino library. https://github.com/tockn/MPU6050_tockn.git [Online]. Available:

Mahony R., Hamel T., Pflimlin J.-M. Nonlinear complementary filters on the special orthogonal group. IEEE Trans. Autom. Control. 2008;53(5):1203–1218.

Mariani B., Rouhani H., Crevoisier X., Aminian K. Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait Posture. 2013;37(2):229–234. PubMed