The Research Progress on Sensing Technologies in Lower Limb Exoskeleton Robot Motion Intent Perception Systems

Authors

  • Haoxiang Zhang

DOI:

https://doi.org/10.61173/rzqecf80

Keywords:

Sensing technologies, lower limb exoskele-ton robot, motion intent perception systems

Abstract

Lower limb exoskeleton robots have demonstrated great potential in the field of medical rehabilitation, especially in assisting patients with limited mobility to recover lower limb motor functions. The accuracy of the movement intention recognition system is a critical factor influencing the control effectiveness of exoskeletons. Sensor technology plays a crucial role in this system, as it accurately collects information such as gait phase, joint angles, and muscle activity, ensuring that the exoskeleton can adjust its movement strategy in real-time. This study analyzes the application and development of inertial measurement units (IMUs), plantar pressure sensors, and surface electromyography (sEMG) sensors in movement intention recognition systems. However, issues such as data synchronization, signal noise, and individual differences remain, requiring further optimization of sensor configuration and data processing strategies. Future research will focus on the introduction of intelligent compensation algorithms, multi-sensor fusion, and collaborative sensing, and the optimization of sensor performance, thereby enhancing the system's accuracy, stability, real-time capability, and adaptability, promoting the widespread application of exoskeleton robots in rehabilitation training.

References

[1] Bartlett H L, Goldfarb M. A phase variable approach for IMU-based locomotion activity recognition. IEEE Transactions on Biomedical Engineering, 2017, 65(6): 1330–1338.

[2] Liu L, Wang H, Li H, et al. Ambulatory human gait phase detection using wearable inertial sensors and a hidden Markov model. Sensors, 2021, 21(4): 1347.

[3] Choi W, Yang W, Na J, et al. Feature optimization for gait phase estimation with a genetic algorithm and Bayesian optimization. Applied Sciences, 2021, 11(19): 8940.

[4] Fullerton E, Heller B, Munoz-Organero M. Recognizing human activity in free-living using multiple body-worn accelerometers. IEEE Sensors Journal, 2017, 17(16): 5290– 5297.

[5] Su B, Gutierrez-Farewik E M. Gait trajectory and gait phase prediction based on an LSTM network. Sensors, 2020, 20(24): 7127.

[6] Sarshar M, Polturi S, Schega L. Gait phase estimation by using LSTM in IMU-based gait analysis—Proof of concept. Sensors, 2021, 21(17): 5749.

[7] Karakish M, Fouz M A, ELsawaf A. Gait trajectory prediction on an embedded microcontroller using deep learning. Sensors, 2022, 22(21): 8441.

[8] Heng W, Pang G, Xu F, et al. Flexible insole sensors with stably connected electrodes for gait phase detection. Sensors, 2019, 19(23): 5197.

[9] Xia Y, Li J, Yang D, et al. Gait phase classification of lower limb exoskeleton based on a compound network model. Symmetry, 2023, 15(1): 163.

[10] Cheng X, Zhang D. Lower limb motion intention recognition based on multi-source information. Journal of Mechanical Design and Research, 2020, 36(6): 54–58.

[11] Guo Z, Song C, Wang C. Real-time gait classification for exoskeleton based on long short-term memory model. Journal of Guangxi University (Natural Science Edition), 2020, 45(5): 1171–1179.

[12] Yuan Y, Guo Z, Wang C, et al. Gait phase classification based on sEMG signals using long short-term memory for lower limb exoskeleton robot. IOP Conference Series: Materials Science and Engineering, 2020: 012041.

[13] Cai S, Chen D, Fan B, et al. Gait phases recognition based on lower limb sEMG signals using LDA-PSO-LSTM algorithm. Biomedical Signal Processing and Control, 2023, 80: 104272.

[14] Marcos Mazon D, Groefsema M, Schomaker L R B, et al. IMU-based classification of locomotion modes, transitions, and gait phases with convolutional recurrent neural networks. Sensors, 2022, 22(22): 8871.

[15] Su B, Smith C, Gutierrez Farewik E. Gait phase recognition using deep convolutional neural network with inertial measurement units. Biosensors, 2020, 10(9): 109.

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Published

2025-08-26