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

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 have 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. Note to Practitioners—This paper tackles the challenge of sensing the flow fields created by human leg kicks during swimming using a bioinspired artificial lateral line (ALL) system. Unlike the undulating movement of a fish’s body or tail, human leg kicks vary in frequency and phase, and very limited research has been done on using ALL systems to sense these movements. To address this, we propose a new method for detecting complex flow patterns with the ALL system. We integrate a lab-fabricated human leg model with the ALL system to show how it captures the features of leg kicks. This method improves flow sensing accuracy by combining time-domain and time-frequency features using advanced neural networks. Key findings include the successful recognition of leg kick patterns and precise localization of the kicking leg, demonstrating the ALL system’s potential for sensing the undulatory flow fields. This study aims to provide theoretical and technical insights for human-robot interaction and formation using ALL systems, enhancing the capabilities of underwater robots and swimmers in complex environments. Additionally, this technology could help develop assistive robots for swimmers, boosting human abilities underwater and ensuring safety during aquatic activities.