Abstract
This paper proposes a data-driven method using a parametric representation of relative rotations to classify human lower-limb locomotion, which is designed for wearable robots. Three Inertial measurement units (IMUs) are mounted on the subject’s waist, left knee, and right knee, respectively. Features for classification comprise relative rotations, angular velocities, and waist acceleration. Those relative rotations are represented by the exponential coordinates. The rotation matrices are normalized by Karcher mean and then the Support Vector Machine (SVM) method is used to train the data. Experiments are conducted with a time-window size of less than 40 ms. Three SVM classifiers telling 3, 5 and 6 rough lower-limb locomotion types respectively are trained, and the average accuracies are all over 98%. With the combination of those rough SVM classifiers, an easy SVM-based ensembled-system is proposed to classify 16 fine locomotion types, achieving the average accuracy of 98.22%, and its latency is 18 ms when deployed to an onboard computer.
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Sanz-Merodio D, Cestari M, Arevalo J C, et al. A lower-limb exoskeleton for gait assistance in quadriplegia. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO). Guangzhou, China, 2012. 122–127
Chen D, Ning M, Zhang B, et al. An improvement to the reciprocating gait orthosis for aiding paraplegic patients in walking. Sci China Tech Sci, 2015, 58: 727–737
Mooney L M, Rouse E J, Herr H M. Autonomous exoskeleton reduces metabolic cost of human walking during load carriage. J Neuroeng Rehabil, 2014, 11: 80
Jatsun S, Malchikov A, Yatsun A. Automatization of manual labor by using an industrial exoskeleton. In: 2020 International Russian Automation Conference. Sochi, 2020. 470–475
Sanchez-Villamañan M D C, Gonzalez-Vargas J, Torricelli D, et al. Compliant lower limb exoskeletons: A comprehensive review on mechanical design principles. J Neuroeng Rehabil, 2019, 16: 55
Zhou Z, Liu X, Jiang Y, et al. Real-time onboard SVM-based human locomotion recognition for a bionic knee exoskeleton on different terrains. In: 2019 Wearable Robotics Association Conference (Wear- RAcon). Scottsdale, 2019. 34–39
Zheng E, Wang Q, Qiao H. Locomotion mode recognition with robotic transtibial prosthesis in inter-session and inter-day applications. IEEE Trans Neural Syst Rehabil Eng, 2019, 27: 1836–1845
Huo W, Mohammed S, Amirat Y, et al. Fast gait mode detection and assistive torque control of an exoskeletal robotic orthosis for walking assistance. IEEE Trans Robot, 2018, 34: 1–18
Ding S, Ouyang X, Liu T, et al. Gait event detection of a lower extremity exoskeleton robot by an intelligent imu. IEEE Sens J, 2018, 18: 9728–9735
Han Y L, Wang X S. The biomechanical study of lower limb during human walking. Sci China Tech Sci, 2011, 54: 983–991
Hu B, Rouse E, Hargrove L. Fusion of bilateral lower-limb neuromechanical signals improves prediction of locomotor activities. Front Robot AI, 2018, 5: 78, doi: https://doi.org/10.3389/frobt.2018.00078
Zhou Y, Liu J, Zeng J, et al. Bio-signal based elbow angle and torque simultaneous prediction during isokinetic contraction. Sci China Tech Sci, 2018, 62: 21–30
Simao M, Mendes N, Gibaru O, et al. A review on electromyography decoding and pattern recognition for human-machine interaction. IEEE Access, 2019, 7: 39564–39582
Fang C, He B, Wang Y, et al. EMG-centered multisensory based technologies for pattern recognition in rehabilitation: State of the art and challenges. Biosensors, 2020, 10: 85
Preece S J, Goulermas J Y, Kenney L P J, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng, 2009, 56: 871–879
Mascret Q, Bielmann M, Fall C, et al. Real-time human physical activity recognition with low latency prediction feedback using raw IMU data. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, 2018. 239–242
Liu X, Zhou Z, Wang Q. Real-time onboard human motion recognition based on inertial measurement units. In: 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Tianjin, China, 2018. 724–728
Vemulapalli R, Rama C. Rolling rotations for recognizing human actions from 3D skeletal data. In: 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, 2016. 4471–4479
Huang Z, Wan C, Probst T, et al. Deep learning on lie groups for skeleton-based action recognition. Honolulu, 2017. 1243–1252
Rhif M, Wannous H, Farah I R. Action recognition from 3D skeleton sequences using deep networks on lie group features. In: 2018 24th International Conference on Pattern Recognition (ICPR). Beijing, China, 2018. 3427–3432
Li M, Zhao L. The classification of human lower limb motion based on acceleration sensor. In: 2016 IEEE Chinese Guidance, Navigation and Control Conference. Nanjing, China, 2016. 2210–2214
Li H, Derrode S, Pieczynski W. An adaptive and on-line imu-based locomotion activity classification method using a triplet markov model. Neurocomputing, 2019, 362: 94–105
Wan W, Liu H, Shi G, et al. Real-time recognition of multi-category human motion using µIMU data. In: International Conference on Mechatronics and Automation. Harbin, China, 2007. 1845–1850
Murray R M, Li Z, Shankara S S. A Mathematical Introduction to Robot Manipulation. Boca Raton: CRC Press, 1994. 69–76
Lynch K M, Park F C. Modern Robotics. Cambridge: Cambridge Univeristy Press, 2017. 80–88
Huynh D Q. Metrics for 3D rotations: Comparison and analysis. J Math Imag Vis, 2009, 35: 155–164
Selig J M. Geometric Fundamentals of Robotics. New York: Springer Science & Business Media, 2005
Calinon S. Gaussians on riemannian manifolds: Applications for robot learning and adaptive control. IEEE Robot Automat Mag, 2020, 27: 33–45
Taylor M J D, Dabnichki P, Strike S C. A three-dimensional biomechanical comparison between turning strategies during the stance phase of walking. Human Movement Sci, 2005, 24: 558–573
Hsu Y L, Yang S C, Chang H C, et al. Human daily and sport activity recognition using a wearable inertial sensor network. IEEE Access, 2018, 6: 31715–31728
Martinez-Hernandez U, Dehghani-Sanij A A. Adaptive bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors. Neural Networks, 2018, 102: 107–119
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This work was supported by the National Natural Science Foundation of China (Grant No. 91948302).
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Xu, S., Ding, Y. Real-time recognition of human lower-limb locomotion based on exponential coordinates of relative rotations. Sci. China Technol. Sci. 64, 1423–1435 (2021). https://doi.org/10.1007/s11431-020-1802-2
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DOI: https://doi.org/10.1007/s11431-020-1802-2