Time–Frequency Feature Fusion Approach for Hemiplegic Gait Recognition

• Published in: Computers (Basel) Vol. 14; no. 8; p. 334
• Main Authors: Mao, Linglong, Mei, Zhanyong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Algorithms; Arrays; Artificial intelligence; Asymmetry; Biometry; Center of pressure; center of pressure trajectory; Data augmentation; Datasets; Deep learning; Employee motivation; Fast Fourier transformations; Fourier transform; Frequency domain analysis; Gait; Gait recognition; hemiplegic gait; Machine learning; Middle age; Motion capture; Neural networks; Patients; Performance measurement; Plantar pressure; Pressure distribution; Pressure sensors; Quality of life; Random noise; Sensor arrays; Sensors; Stroke; TFDI-Net; Time-frequency analysis; time-frequency domain features; Taiwan
• ISSN: 2073-431X; 2073-431X
• DOI: 10.3390/computers14080334

Accurately distinguishing hemiplegic gait from healthy gait is significant for alleviating clinicians’ diagnostic workloads and enhancing rehabilitation efficiency. The center of pressure (CoP) trajectory extracted from pressure sensor arrays can be utilized for hemiplegic gait recognition. Existing research studies on hemiplegic gait recognition based on plantar pressure have paid limited attention to the differences in recognition performance offered by CoP trajectories along different directions. To address this, this paper proposes a neural network model based on time–frequency domain feature interaction—the temporal–frequency domain interaction network (TFDI-Net)—to achieve efficient hemiplegic gait recognition. The work encompasses: (1) collecting CoP trajectory data using a pressure sensor array from 19 hemiplegic patients and 29 healthy subjects; (2) designing and implementing the TFDI-Net architecture, which extracts frequency domain features of the CoP trajectory via fast Fourier transform (FFT) and interacts or fuses them with time domain features to construct a discriminative joint representation; (3) conducting five-fold cross-validation comparisons with traditional machine learning methods and deep learning methods. Intra-fold data augmentation was performed by adding Gaussian noise to each training fold during partitioning. Box plots were employed to visualize and analyze the performance metrics of different models across test folds, revealing their stability and advantages. The results demonstrate that the proposed TFDI-Net outperforms traditional machine learning models, achieving improvements of 2.89% in recognition rate, 4.6% in F1-score, and 8.25% in recall.