The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.

1. Adams, J. M., & Cerny, K. (2018). Observational gait analysis: A visual guide. SLACK Incorporated.
2. Ancillao, A., Tedesco, S., Barton, J., & O’Flynn, B. (2018). Indirect measurement of ground reaction forces and moments by means of wearable inertial sensors: A systematic review. Sensors, 18(8), Article 2564. https://doi.org/10.3390/s18082564
3. Assaiante, C. (1998). Development of locomotor balance control in healthy children. Neuroscience & Biobehavioral Reviews, 22(4), 527–532. https://doi.org/10.1016/S0149-7634(97)00040-7
4. Bao, T., Zaidi, S. A. R., Xie, S., Yang, P., & Zhang, Z. Q. (2020). A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography. IEEE Transactions on Instrumentation and Measurement, 70, 1–9. https://doi.org/10.1109/TIM.2020.3026049
5. Barr, K. M., Miller, A. L., & Chapin, K. B. (2010). Surface electromyography does not accurately reflect rectus femoris activity during gait: Impact of speed and crouch on vasti-to-rectus crosstalk. Gait & Posture, 32(3), 363–368. https://doi.org/10.1016/j.gaitpost.2010.06.005
6. Benson, L. C., Räisänen, A. M., Clermont, C. A., & Ferber, R. (2022). Is this the real life, or is this just laboratory? A scoping review of IMU-based running gait analysis. Sensors, 22(5), Article 1722. https://doi.org/10.3390/s22051722
7. Birch, I., Nirenberg, M., Vernon, W., & Birch, M. (2020). Forensic gait analysis: Principles and practice. Taylor & Francis.
8. Burton, W. S., 2nd, Myers, C. A., & Rullkoetter, P. J. (2021). Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living. Journal of Biomechanics, 123, Article 110439. https://doi.org/10.1016/j.jbiomech.2021.110439
9. Camargo, J., Ramanathan, A., Flanagan, W., & Young, A. (2021). A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions. Journal of Biomechanics, 119, Article 110320. https://doi.org/10.1016/j.jbiomech.2021.110320
10. Clèriges, A., Valverde, S., Bernal, J., Freixenet, J., Oliver, A., & Lladó, X. (2019). Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Computers in Biology and Medicine, 115, Article 103487. https://doi.org/10.1016/j.compbiomed.2019.103487
11. Cohen, R., Mitchell, C., Dotan, R., Gabriel, D., Klentrou, P., & Falk, B. (2010). Do neuromuscular adaptations occur in endurance-trained boys and men? Applied Physiology, Nutrition, and Metabolism, 35(4), 471–479. https://doi.org/10.1139/H10-031
12. den Otter, A. R., Geurts, A. C. H., Mulder, T., & Duysens, J. (2004). Speed related changes in muscle activity from normal to very slow walking speeds. Gait & Posture, 19(3), 270–278. https://doi.org/10.1016/S0966-6362(03)00071-7
13. Escalona, M. J., Bourbonnais, D., Goyette, M., Le Flem, D., Duclos, C., & Gagnon, D. H. (2021). Effects of varying overground walking speeds on lower-extremity muscle synergies in healthy individuals. Motor Control, 25(2), 234–251. https://doi.org/10.1123/mc.2020-0044
14. International Society of Electrophysiology and Kinesiology. (2018). Standards for reporting EMG data. Journal of Electromyography and Kinesiology, 42, I–II. https://doi.org/10.1016/j.jelekin.2018.08.001
15. Jain, E., Anthony, L., Aloba, A., Castonguay, A., Cuba, I., & Shaw, A. (2016). Is the motion of a child perceivably different from the motion of an adult? ACM Transactions on Applied Perception, 13(4), Article 22. https://doi.org/10.1145/2947614
16. Karatsidis, A., Bellusci, G., Schepers, H. M., de Zee, M., Andersen, M. S., & Veltink, P. H. (2017). Estimation of ground reaction forces and moments during gait using only inertial motion capture. Sensors, 17(1), Article 75. https://doi.org/10.3390/s17010075
17. Kelly, H. D. (2020). Forensic gait analysis (1st ed.). CRC Press.
18. Ketkar, N., & Moolayil, J. (2021). Deep learning with Python: Learn best practices of deep learning models with PyTorch. Apress.
19. Kwan, R. L. C., Zheng, Y. P., & Cheing, G. L. Y. (2010). The effect of aging on the biomechanical properties of plantar soft tissues. Clinical Biomechanics, 25(6), 601–605. https://doi.org/10.1016/j.clinbiomech.2010.04.003
20. Levine, D., Richards, J., & Whittle, M. W. (2012). Whittle's gait analysis (5th ed.). Elsevier Health Sciences.
21. Lou, Y., Wang, R., Mai, J., Wang, N., & Wang, Q. (2019). IMU-based gait phase recognition for stroke survivors. Robotica, 37(12), 2195–2208. https://doi.org/10.1017/S026357471900057X
22. Lu, Y., Wang, H., Zhou, B., Wei, C., & Xu, S. (2022). Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models. Expert Systems with Applications, 203, Article 117340. https://doi.org/10.1016/j.eswa.2022.117340
23. Mesin, L. (2020). Crosstalk in surface electromyogram: Literature review and some insights. Physical and Engineering Sciences in Medicine, 43(2), 481–492. https://doi.org/10.1007/s13246-020-00863-1
24. Monoli, C., Fuentez-Pérez, J. F., Cau, N., Capodaglio, P., Galli, M., & Tuhtan, J. A. (2021). Land and underwater gait analysis using wearable IMU. IEEE Sensors Journal, 21(9), 11192–11202. https://doi.org/10.1109/JSEN.2021.3061652
25. Nene, A., Byrne, C., & Hermens, H. (2004). Is rectus femoris really a part of quadriceps?: Assessment of rectus femoris function during gait in able-bodied adults. Gait & Posture, 20(1), 1–13. https://doi.org/10.1016/S0966-6362(03)00091-2
26. OpenSim. (2022). How CMC works. OpenSim Documentation. https://simtk-confluence.stanford.edu:8443/display/OpenSim/How+CMC+Works
27. Pearson, R. K. (2016). Median filters and some extensions. In M. Gabbouj (Ed.), Nonlinear digital filtering with Python: An introduction (1st ed., pp. 93–124). CRC Press.
28. Riley, P. O., DellaCroce, U., & Kerrigan, D. C. (2001). Effect of age on lower extremity joint moment contributions to gait speed. Gait & Posture, 14(3), 264–270. https://doi.org/10.1016/S0966-6362(01)00133-3
29. Roelker, S. A., Caruthers, E. J., Hall, R. K., Pelz, N. C., Chaudhari, A. M. W., & Siston, R. A. (2020). Effects of optimization technique on simulated muscle activations and forces. Journal of Applied Biomechanics, 36(4), 259–278. https://doi.org/10.1123/jab.2019-0260
30. Sarshar, M., Polturi, S., & Schega, L. (2021). Gait phase estimation by using LSTM in IMU-based gait analysis—Proof of concept. Sensors, 21(17), Article 5749. https://doi.org/10.3390/s21175749
31. Schicketmueller, A., Lamprecht, J., Hofmann, M., Sailer, M., & Rose, G. (2020). Gait event detection for stroke patients during robot-assisted gait training. Sensors, 20(12), Article 3399. https://doi.org/10.3390/s20123399
32. Silder, A., Heiderscheit, B., & Thelen, D. G. (2008). Active and passive contributions to joint kinetics during walking in older adults. Journal of Biomechanics, 41(7), 1520–1527. https://doi.org/10.1016/j.jbiomech.2008.02.016
33. Stetter, B. J., Krafft, F. C., Ringhof, S., Stein, T., & Sell, S. (2020). A machine learning and wearable sensor based approach to estimate external knee flexion and adduction moments during various locomotion tasks. Frontiers in Bioengineering and Biotechnology, 8, Article 9. https://doi.org/10.3389/fbioe.2020.00009
34. Suzuki, K. (2011). Artificial neural networks: Methodological advances and biomedical applications. IntechOpen.
35. Trinler, U., Leboeuf, F., Hollands, K., Jones, R., & Baker, R. (2018). Estimation of muscle activation during different walking speeds with two mathematical approaches compared to surface EMG. Gait & Posture, 64, 266–273. https://doi.org/10.1016/j.gaitpost.2018.06.115
36. Trojaniello, D., Pelosin, E., Avanzino, L., Mirelman, A., Hausdorff, J., & Della Croce, U. (2014). Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: Application to elderly, hemiparetic, parkinsonian and choreic gait. Journal of NeuroEngineering and Rehabilitation, 11, Article 152. https://doi.org/10.1186/1743-0003-11-152
37. Van Criekinge, T., Saeys, W., Hallemans, A., Van de Walle, P., Vereeck, L., De Hertogh, W., & Truijen, S. (2018). Age-related differences in muscle activity patterns during walking in healthy individuals. Journal of Electromyography and Kinesiology, 41, 124–131. https://doi.org/10.1016/j.jelekin.2018.05.009
38. Wang, J., Dai, Y., & Si, X. (2021). Analysis and recognition of human lower limb motions based on electromyography (EMG) signals. Electronics, 10(20), Article 2473. https://doi.org/10.3390/electronics10202473
39. Zabre-Gonzalez, E.V., Amieva-Alvarado, D., & Beardsley, S.A. (2021). Prediction of EMG activation profiles from gait kinematics and kinetics during multiple terrains. In Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 6326–6329). IEEE. https://doi.org/10.1109/EMBC46164.2021.9630919