Article: Study on high jump athlete error action recognition based on dual stream CNN BiLSTM Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.10 pp.147 - 164 Abstract: A high jump athlete error recognition method based on dual stream CNN BiLSTM is proposed to address the issues of rapid changes in high jump movements and strong temporal dependencies in multiple stages. Firstly, collect wrist, ankle, and waist movement data, and use an adaptive filtering algorithm driven by spectral kurtosis extremum for pre-processing. Secondly, a dual stream heterogeneous input architecture that integrates time-domain and frequency-domain features is constructed to process sensor timing signals and time-frequency spectra separately. Finally, CNN is used to extract spatial local features, and a bidirectional long short-term memory network (BiLSTM) is introduced to model the entire process of running, jumping, pole crossing, and landing in a long-range time series, achieving accurate recognition of erroneous actions. The experimental results show that this method outperforms the comparative methods in three indicators: top-1 accuracy (> 96.8%), Cohen's Kappa coefficient (> 0.941), and average inference time (≤ 8.3 ms). Inderscience Publishers - linking academia, business and industry through research

Title: Study on high jump athlete error action recognition based on dual stream CNN BiLSTM

Authors: Huifen Jia

Addresses: Xi'an Technology and Business College, Xi'an, 710200, China

Abstract: A high jump athlete error recognition method based on dual stream CNN BiLSTM is proposed to address the issues of rapid changes in high jump movements and strong temporal dependencies in multiple stages. Firstly, collect wrist, ankle, and waist movement data, and use an adaptive filtering algorithm driven by spectral kurtosis extremum for pre-processing. Secondly, a dual stream heterogeneous input architecture that integrates time-domain and frequency-domain features is constructed to process sensor timing signals and time-frequency spectra separately. Finally, CNN is used to extract spatial local features, and a bidirectional long short-term memory network (BiLSTM) is introduced to model the entire process of running, jumping, pole crossing, and landing in a long-range time series, achieving accurate recognition of erroneous actions. The experimental results show that this method outperforms the comparative methods in three indicators: top-1 accuracy (> 96.8%), Cohen's Kappa coefficient (> 0.941), and average inference time (≤ 8.3 ms).

Keywords: CNN BiLSTM model; high jumper; wrong action; image recognition.

DOI: 10.1504/IJBIDM.2026.156186

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.10, pp.147 - 164

Received: 29 Jan 2026
Accepted: 16 Jun 2026
Published online: 07 Sep 2026 *

Title: Study on high jump athlete error action recognition based on dual stream CNN BiLSTM

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