Multisource Information Fusion Method for Human Gait Perception

doi:10.16182/j.issn1004731x.joss.24-0389

Abstract

Abstract:

In response to the insufficient gait perception capability during lower limb exoskeleton assistance, a human lower limb gait phase optimization classification model was proposed. A wireless transmission gait information collection system was designed for collecting the required gait phase feature information. Human joint angles were accurately calculated by fusing acceleration and angular velocity information using extended Kalman filtering. Additionally, kernel principal component analysis was applied to reduce dimensionality in conjunction with plantar pressure data. The LSSVM algorithm was employed to classify gait data, and the PSO algorithm was utilized to find the optimal classification parameters. Experimental results demonstrate that after dimensionality reduction, the PSO-based LSSVM outperforms other methods in identifying gait phases and improves timeliness, achieving an accuracy rate of 97.4%. The results validate the feasibility of using a multisource information fusion classification method for human gait phase perception, providing support for the assistance strategy of lower limb exoskeleton robots.

Key words: multisource information fusion, gait perception, least squares support vector machine, plantar pressure, joint angle

CLC Number:

TP391.9

Chen Guiliang, Liu Guowei, Li Yongchao, Cai Chao, Li Zihao, Yang Dong. Multisource Information Fusion Method for Human Gait Perception[J]. Journal of System Simulation, 2025, 37(10): 2522-2532.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Table 1

Fig. 13

References 23

[1]	Nagata J M, Alsamman S, Smith N, et al. Social Epidemiology of Fitbit Daily Steps in Early Adolescence[J]. Pediatric Research, 2023, 94(5): 1838-1844.
[2]	Yan Tingfang, Cempini Marco, Calogero Maria Oddo, et al. Review of Assistive Strategies in Powered Lower-limb Orthoses and Exoskeletons[J]. Robotics and Autonomous Systems, 2015, 64: 120-136.
[3]	刘薛勤, 刘宁, 苏中, 等. 基于足底压力感知的智能步态识别方法研究[J]. 系统仿真学报, 2021, 33(11): 2572-2578.
	Liu Xueqin, Liu Ning, Su Zhong, et al. Research on Intelligent Gait Recognition Method Based on Plantar Pressure Perception[J]. Journal of System Simulation, 2021, 33(11): 2572-2578.
[4]	Gao Farong, Tian Taixing, Yao Ting, et al. Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms[J]. Computational Intelligence and Neuroscience, 2021, 2021: 6693206.
[5]	Zhang Zaifang, Wang Zhaoyang, Lei Han, et al. Gait Phase Recognition of Lower Limb Exoskeleton System Based on the Integrated Network Model[J]. Biomedical Signal Processing and Control, 2022, 76: 103693.
[6]	Zhen Tao, Yan Lei, Kong Jianlei. An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection[J]. International Journal of Environmental Research and Public Health, 2020, 17(16): 5633.
[7]	汪步云, 缪龙, 吴臣, 等. 肌电和足压信息融合的外骨骼步态识别[J]. 兵器装备工程学报, 2024, 45(1): 278-287.
	Wang Buyun, Miao Long, Wu Chen, et al. Research on Exoskeleton Gait Recognition Based on sEMG and Foot Pressure Information Introspection[J]. Journal of Ordnance Equipment Engineering, 2024, 45(1): 278-287.
[8]	Wang Yu, Song Quanjun, Ma Tingting, et al. Research on Human Gait Phase Recognition Algorithm Based on Multi-source Information Fusion[J]. Electronics, 2023, 12(1): 193.
[9]	Merletti R, G L Tutorial Cerone. Surface EMG Detection, Conditioning and Pre-processing: Best Practices[J]. Journal of Electromyography and Kinesiology, 2020, 54: 102440.
[10]	Farah J D, Baddour N, Lemaire E D. Design, Development, and Evaluation of a Local Sensor-based Gait Phase Recognition System Using a Logistic Model Decision Tree for Orthosis-control[J]. Journal of NeuroEngineering and Rehabilitation, 2019, 16(1): 22.
[11]	Cestari Manuel, Sanz-Merodio Daniel, Juan Carlos Arevalo, et al. An Adjustable Compliant Joint for Lower-limb Exoskeletons[J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(2): 889-898.
[12]	Wang Lefan, Jones D, Jones A, et al. A Portable Insole System to Simultaneously Measure Plantar Pressure and Shear Stress[J]. IEEE Sensors Journal, 2022, 22(9): 9104-9113.
[13]	Zhu Chenyao, Luo Lan, Jingeng Mai, et al. Recognizing Continuous Multiple Degrees of Freedom Foot Movements with Inertial Sensors[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 431-440.
[14]	Zhen Tao, Yan Lei, Yuan Peng. Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm[J]. Algorithms, 2019, 12(12): 253.
[15]	Taborri Juri, Palermo Eduardo, Rossi Stefano, et al. Gait Partitioning Methods: A Systematic Review[J]. Sensors, 2016, 16(1): 66.
[16]	Novak Domen, Riener Robert. A Survey of Sensor Fusion Methods in Wearable Robotics[J]. Robotics and Autonomous Systems, 2015, 73: 155-170.
[17]	Kreuzer David, Munz Michael. Deep Convolutional and LSTM Networks on Multi-channel Time Series Data for Gait Phase Recognition[J]. Sensors, 2021, 21(3): 789.
[18]	Le Nguyen V, Caverly R J. Cable-driven Parallel Robot Pose Estimation Using Extended Kalman Filtering with Inertial Payload Measurements[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 3615-3622.
[19]	Kadkhodazadeh Mojtaba, Farzin Saeed. A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters[J]. Water Resources Management, 2021, 35(12): 3939-3968.
[20]	邵诚, 孙宏远, 张林. 基于PSO-LSSVM的反舰导弹自控弹道仿真[J]. 系统仿真学报, 2021, 33(4): 918-926.
	Shao Cheng, Sun Hongyuan, Zhang Lin. Simulation of Anti-ship Missile Automatic Control Section Trajectory Based on PSO-LSSVM[J]. Journal of System Simulation, 2021, 33(4): 918-926.
[21]	Tharwat Alaa, Schenck Wolfram. A Conceptual and Practical Comparison of PSO-style Optimization Algorithms[J]. Expert Systems with Applications, 2021, 167: 114430.
[22]	Park Jieun, Kim Minho, Hong Insic, et al. Foot Plantar Pressure Measurement System Using Highly Sensitive Crack-based Sensor[J]. Sensors, 2019, 19(24): 5504.
[23]	徐继亚, 王艳, 严大虎, 等. 融合KPCA与信息粒化的滚动轴承性能退化SVM预测[J]. 系统仿真学报, 2018, 30(6): 2345-2354.
	Xu Jiya, Wang Yan, Yan Dahu, et al. SVM Prediction of Performance Degradation of Rolling Bearings with Fusion of KPCA and Information Granulation[J]. Journal of System Simulation, 2018, 30(6): 2345-2354.

分类方法	时间
KPCA-PSO-LSSVM	0.001 72
PSOLSSVM	0.008 58
BPNN	0.004 17
RF	0.006 05