An Optimization System for Intent Recognition Based on an Improved KNN Algorithm with Minimal Feature Set for Powered Knee Prosthesis

Zhang, Yao; Wang, Xu; Xiu, Haohua; Liu, Ren; Han, Yang; Ma, Yongxin; Chen, Wei; Wei, Guowu

doi:10.1007/s42235-023-00419-w

Cited by 2 publications

(1 citation statement)

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“… Chung et al (2019) developed an LSTM network to train multimodal physical sensor data with a recognition rate of 92.18%. Naturally, many other methods have been employed for locomotion mode recognition of exoskeleton robots, just as SVM ( Zheng et al, 2022b ), hidden Markov model (HMM) ( Liu et al, 2017 ), dynamic time warping (DTW) ( Zheng et al, 2022a ), KNN ( Zhang et al, 2023 ) and linear discriminant analysis (LDA) ( Young & Hargrove, 2015 ). Table 8 shows the results of the comparison with other methods.…”

Section: Resultsmentioning

confidence: 99%

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton

Tang,

Zhao,

et al. 2024

PeerJ Computer Science

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Locomotion mode recognition in humans is fundamental for flexible control in wearable-powered exoskeleton robots. This article proposes a hybrid model that combines a dense convolutional network (DenseNet) and long short-term memory (LSTM) with a channel attention mechanism (SENet) for locomotion mode recognition. DenseNet can automatically extract deep-level features from data, while LSTM effectively captures long-dependent information in time series. To evaluate the validity of the hybrid model, inertial measurement units (IMUs) and pressure sensors were used to obtain motion data from 15 subjects. Five locomotion modes were tested for the hybrid model, such as level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending. Furthermore, the data features of the ramp were inconspicuous, leading to large recognition errors. To address this challenge, the SENet module was incorporated, which improved recognition rates to some extent. The proposed model automatically extracted the features and achieved an average recognition rate of 97.93%. Compared with known algorithms, the proposed model has substantial recognition results and robustness. This work holds promising potential for applications such as limb support and weight bearing.

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