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Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units

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Abstract

The wearable powered exoskeletons have obtained great interest in enhancing the ability of people to perform heavy physical labor (e.g., soldiers transporting military supplies in high altitude areas and couriers carrying express goods in the logistics industry). A key problem we care about is how to make the exoskeletons provide efficient aids for people in complex environment (e.g., different terrains, different speeds, and different payloads), and the exoskeletons controller system needs the gait phase information reflecting people’s walking state for creating aids for people. There are two main methods of gait phase detection, which are model-based and distance-based methods. Model-based approaches describe data distribution by employing discriminative (e.g., support vector machines (SVMs), recurrent neural networks (RNNs) and convolutional neural networks (CNNs)) and generative (e.g., hidden Markov models (HMMs) and Gaussian models) models. Distance-based approaches match locomotion pattern with reference sets by employing distance measures, such as dynamic time warping (DTW). The distance-based method during training is better when the available data is limited, because the model-based method will face the problem of over-fitting. This study proposes an online gait detection method based on distance and multi-sensors information fusion to solve the gait phase detection problem in complex environment. In addition, considering the force sensitive resistors (FSRs) limited lifespan, we adopt lower extremity angles measured by the IMU sensors as the input signal of the method for improving the performance. The results show that the method can adaptively detect the gait phase in real time for different terrains and different payloads while walking at different speeds. Our proposed method can gain over 95% accuracy and time difference is 4.99 ± 15.05 ms.

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References

  1. Wang L, Ning H, Tan T, Hu W (2004) Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans Circuits Syst Video Technol 14(2):149–158. https://doi.org/10.1109/TCSVT.2003.821972

    Article  Google Scholar 

  2. Pappas IP, Popovic MR, Keller T, Dietz V, Morari M (2001) A reliable gait phase detection system. IEEE Trans Neural Syst Rehabil Eng. https://doi.org/10.1109/7333.928571

    Article  Google Scholar 

  3. Fong T, Kunz C, Hiatt LM, Bugajska M (2006) The human-robot interaction operating system. In: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction, HRI 2006, Salt Lake City, Utah, USA, PP. 41–48

  4. Kong K, Tomizuka M (2009) A gait monitoring system based on air pressure sensors embedded in a shoe. IEEE/ASME Trans Mechatron 14(3):358–370. https://doi.org/10.1109/TMECH.2008.2008803

    Article  Google Scholar 

  5. Mariani B, Rouhani H, Crevoisier X, Aminian K (2013) Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait Posture 37(2):229–234. https://doi.org/10.1016/j.gaitpost.2012.07.012

    Article  Google Scholar 

  6. Lopez-Meyer P, Fulk GD, Sazonov ES (2011) Automatic Detection of Temporal Gait Parameters in Poststroke Individuals. IEEE Trans Inf Technol Biomed 15(4):594–601. https://doi.org/10.1109/TITB.2011.2112773

    Article  Google Scholar 

  7. Blanc Y, Balmer C, Landis T, Vingerhoets F (1999) Temporal parameters and patterns of the foot roll over during walking: Normative data for healthy adults. Gait & Posture, pp. 97–108. https://doi.org/10.1016/S0966-6362(99)00019-3

  8. Jing T, Bin ZJ, Yang W, Lie Y (2018) Self-tuning threshold method for real-time gait phase detection based on ground contact forces using FSRs. Sensors 18(2):481–495

    Article  Google Scholar 

  9. Hu H, Zheng J, Zhan E, Yu L (2019) Curve similarity model for real-time gait phase detection based on ground contact forces. Sensors 19(14):3235–3255. https://doi.org/10.3390/s19143235

    Article  Google Scholar 

  10. Lie Yu, Zheng J, Wang Y, Song Z, Zhan E (2019) Adaptive method for real-time gait phase detection based on ground contract forces. Gait Posture 41(1):269–275. https://doi.org/10.1016/j.gaitpost.2014.10.019

    Article  Google Scholar 

  11. Fangli Y, Zheng J, Lie Y, Zhang R, He H, Zhu Z, Zhang Y (2018) Adjustable method for real-time gait pattern detection based on ground reaction forces using force sensitive resistors and statistical analysis of constant false alarm rate. Sensors. https://doi.org/10.3390/s18113764

    Article  Google Scholar 

  12. Martin Schepers H, van Asseldonk EHF, Baten CTM, Veltink PH (2010) Ambulatory estimation of foot placement during walking using inertial sensors. J Biomech 43(15):3138–3143

    Article  Google Scholar 

  13. Rueterbories J, Spaich EG, Andersen OK (2013) Characterization of gait pattern by 3D angular accelerations in hemiparetic and healthy gait. Gait Posture 37(2):183–189. https://doi.org/10.1016/j.gaitpost.2012.06.029

    Article  Google Scholar 

  14. Peruzzi A, Croce U, Cereatti A (2011) Estimation of stride length in level walking using an inertial measurement unit attached to the foot: A validation of the zero velocity assumption during stance. J Biomech 44(10):1991–1994. https://doi.org/10.1016/j.jbiomech.2011.04.035

    Article  Google Scholar 

  15. Rueterbories J, Spaich EG, Larsen B, Andersen OK (2010) Methods for gait event detection and analysis in ambulatory systems. Med Eng Phys 32(6):545–552. https://doi.org/10.1016/j.medengphy.2010.03.007

    Article  Google Scholar 

  16. Huang B, Chen W, Shi X, Xu Y (2007) Gait Event Detection with Intelligent Shoes. In: Proceedings of the International Conference on Information Acquisition, Seogwipo-si, South Korea, 9(11) 579–584.

  17. Takeda R, Tadano S, Todoh M, Morikawa M, Nakayasu M, Yoshinari S (2009) Gait analysis using gravitational acceleration measured by wearable sensors. J Biomech 42(3):223–233

    Article  Google Scholar 

  18. Schwesig R, Leuchte S, Fischer D, Ullmann R, Kluttig A (2011) Inertial sensor based reference gait data for healthy subjects. Gait Posture 33(4):673–678. https://doi.org/10.1016/j.gaitpost.2011.02.023

    Article  Google Scholar 

  19. Kiguchi K, Hayashi Y (2012) An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot. IEEE Trans Syst, Man, Cyber, Part B Cyber 42(4):1064–1071. https://doi.org/10.1109/TSMCB.2012.2185843

    Article  Google Scholar 

  20. Li Z, Wang B, Sun F, Yang C, Xie Q, Zhang W (2014) sEMG-based joint force control for an upper-limb power-assist exoskeleton robot. IEEE J Biomed Health Inform 18(3):1043–1050. https://doi.org/10.1109/JBHI.2013.2286455

    Article  Google Scholar 

  21. Wentink EC, Beijen SI, Hermens HJ, Rietman JS, Veltink PH (2013) Intention detection of gait initiation using EMG and kinematic data. Gait Posture 37(2):223–228. https://doi.org/10.1016/j.gaitpost.2012.07.013

    Article  Google Scholar 

  22. Kyeong S, Shin W, Yang M, Heo U, Feng J-R, Kim J (2019) Recognition of walking environments and gait period by surface electromyography. Front Inf Technol Electr Eng 20(3):40–50. https://doi.org/10.1631/FITEE.1800601

    Article  Google Scholar 

  23. H Lamine, S Bennour, M Laribi, L Romdhane, S Zaghloul (2017) Evaluation of Calibrated Kinect Gait Kinematics Using a Vicon Motion Capture System. In: Computer Methods in Biomechanics & Biomedical Engineering, pp.111–112

  24. Attal F, Amirat Y, Chibani A, Mohammed S (2018) Automatic recognition of gait phases using a multiple-regression hidden Markov model. IEEE/ASME Trans Mechatron 99:1597–1607

    Google Scholar 

  25. Kale A, Cuntoor N, Yegnanarayana B, Rajagopalan AN, Chellappa R (2003) Gait analysis for human identification. In: Proceedings of the 3rd international conference on audio and video based person authentication, Springer-Verlag 706–714

  26. Allseits E, Lučarević J, Gailey R, Agrawal V, Gaunaurd I, Bennett C (2017) The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units. J Biomech 55:27–33

    Article  Google Scholar 

  27. Martinez-Hernandez U, Rubio-Solis A, Dehghani-Sanij AA (2018) Recognition of walking activity and prediction of gait periods with a CNN and first-order MC strategy. In: 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 897–902

  28. Sahoo S, Saboo M, Pratihar DK, Mukhopadhyay S (2020) Real-time detection of actual and early gait events during level-ground and ramp walking. IEEE Sens J 20(14):8128–8136

    Article  Google Scholar 

  29. Huong Thi Thu Vu, Dong D, Cao H-L, Verstraten T, Lefeber D, Vanderborght B, Geeroms J (2020) A review of gait phase detection algorithms for lower limb prostheses. Sensors 20(14):3972–3990. https://doi.org/10.3390/s20143972

    Article  Google Scholar 

  30. Jain B, Schultz D (2020) Sufficient conditions for the existence of a sample mean of time series under dynamic time warping. Ann Math Artif Intell 88(4):313–346. https://doi.org/10.1007/s10472-019-09682-2

    Article  MathSciNet  MATH  Google Scholar 

  31. Morel M, Achard C, Kulpa R, Dubuisson S (2017) Time-series averaging using constrained dynamic time warping with tolerance. Patt Recogn, Elsevier 74:77–89. https://doi.org/10.1016/j.patcog.2017.08.015

    Article  Google Scholar 

  32. Brijnesh J, David S (2018) On the existence of a sample mean in dynamic time warping spaces. https://www.researchgate.net/publication/323655911

  33. Brill M, Fluschnik T, Froese V, Jain B, Niedermeier R, Schultz D (2019) Exact mean computation in dynamic time warping spaces. Data Min Knowl Disc 33:252–291

    Article  MathSciNet  Google Scholar 

  34. Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, Zakaria J, Keogh E (2012) Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.262–270

  35. Sánchez Manchola MD, Pinto Bernal MJ, Munera M, Cifuentes CA (2019) Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals. In: Sensors p.2988. https://doi.org/10.3390/s19132988

  36. Zakria M, Maqbool HF, Hussain T, Awad MI, Mehryar P, Iqbal N, Dehghani-Sanij AA (2017) Heuristic based gait event detection for human lower limb movement. In: Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA, pp. 337–340

  37. Maqbool HF, Husman MAB, Awad MI, Abouhossein A, Mehryar P, Iqbal A, Dehghani-Sanij AA (2016) Real-time gait event detection for lower limb amputees using a single wearable sensor. In: Proceedings of the 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 5067–5070

  38. Bejarano NC, Ambrosini E, Pedrocchi A, Ferrigno G, Monticone M, Ferrante S (2015) A novel adaptive, real-time algorithm to detect gait events from wearable sensors. IEEE Trans Neural Syst Rehabil Eng 23:413–422. https://doi.org/10.1109/TNSRE.2014.2337914

    Article  Google Scholar 

  39. Jung-Young WH, Yang H, Park H (2015) A Neural network-based gait phase classification method using sensors equipped on lower limb exoskeleton robots. Sensors 15:7738–7759. https://doi.org/10.3390/s151127738

    Article  Google Scholar 

  40. Mannini A, Genovese V, Sabatini AM (2014) Online decoding of hidden Markov models for gait event detection using foot-mounted gyroscopes. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2013.2293887

    Article  Google Scholar 

  41. Lempereur M, Rousseau F, Rémy-Néris O, Pons C, Houx L, Quellec G, Brochard S (2020) A new deep learning-based method for the detection of gait events in children with gait disorders: Proof-of-concept and concurrent validity. J Biomech 98:9490–9494. https://doi.org/10.1016/j.jbiomech.2019.109490

    Article  Google Scholar 

  42. Lee JK, Park EJ (2011) Quasi real-time gait event detection using shank-attached gyroscopes. Med Biol Eng Comput 49(6):707–712. https://doi.org/10.1007/s11517-011-0736-0

    Article  Google Scholar 

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Acknowledgements

We wish to express our gratitude to the participants who took part in this study.

Funding

This study was funded by the National Key R&D Program of China “The study on Load-bearing and Moving Support Exoskeleton Robot Key Technology and Typical Application” (grant number 2017YFB1300502).

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  1. School of Information Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei, China

    Liping Huang, Jianbin Zheng & Huacheng Hu

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  1. Liping Huang
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Correspondence to Liping Huang.

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Huang, L., Zheng, J. & Hu, H. Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units. Int J of Soc Robotics 14, 413–428 (2022). https://doi.org/10.1007/s12369-021-00794-6

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