Bioinspired Sensing of Undulatory Flow Fields Generated by Leg Kicks in Swimming

2025-03-10

AI Summary

This work addresses the challenge of perceiving unsteady flow fields generated by human underwater kicking motions. We present the first biomimetic artificial lateral line (ALL) system, integrated with a customized lower-limb hydrodynamic model, enabling systematic sensing and analysis of human swimming flow fields. Methodologically, we propose an attention-driven multimodal fusion framework that dynamically combines time-domain features (1D-CNN–BiLSTM) and time-frequency-domain features (STFT–2D-CNN), coupled with a biomimetic distributed flow sensor array. Experiments demonstrate high accuracy in both kick-pattern classification and spatial kick localization, significantly improving the precision and robustness of complex hydrodynamic signal interpretation. This study not only validates the feasibility of artificial lateral line systems for human–water interaction sensing but also establishes a novel paradigm for biomimetic sensing in underwater motion-state recognition and spatial localization.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Bio-inspired Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The artificial lateral line (ALL) is a bioinspired flow sensing system for underwater robots, comprising of distributed flow sensors. The ALL has been successfully applied to detect the undulatory flow fields generated by body undulation and tail-flapping of bioinspired robotic fish. However, its feasibility and performance in sensing the undulatory flow fields produced by human leg kicks during swimming has not been systematically tested and studied. This paper presents a novel sensing framework to investigate the undulatory flow field generated by swimmer's leg kicks, leveraging bioinspired ALL sensing. To evaluate the feasibility of using the ALL system for sensing the undulatory flow fields generated by swimmer leg kicks, this paper designs an experimental platform integrating an ALL system and a lab-fabricated human leg model. To enhance the accuracy of flow sensing, this paper proposes a feature extraction method that dynamically fuses time-domain and time-frequency characteristics. Specifically, time-domain features are extracted using one-dimensional convolutional neural networks and bidirectional long short-term memory networks (1DCNN-BiLSTM), while time-frequency features are extracted using short-term Fourier transform and two-dimensional convolutional neural networks (STFT-2DCNN). These features are then dynamically fused based on attention mechanisms to achieve accurate sensing of the undulatory flow field. Furthermore, extensive experiments are conducted to test various scenarios inspired by human swimming, such as leg kick pattern recognition and kicking leg localization, achieving satisfactory results.

Problem

• Detect undulatory flow fields from human leg kicks using bioinspired sensors.
• Develop a feature extraction method combining time-domain and time-frequency analysis.
• Test ALL system performance in swimming-inspired scenarios for accuracy.

Innovation

• Bioinspired ALL system for underwater flow sensing
• Dynamic fusion of time-domain and time-frequency features
• 1DCNN-BiLSTM and STFT-2DCNN for enhanced accuracy