Deep learning-based fishermen's behaviour recognition with wearable devices to enhance maritime safety

February 1, 2026

From the perspective of fishermen surveillance, this study investigates fishermen’s behaviour and operational patterns to enhance workplace safety during fishing activities. This paper introduces an early-fusion, multimodal approach to monitoring individual fishermen’s behaviour using wrist-worn inertial sensors. To characterise behaviour during fishing operations, accelerometer and gyroscope signals were collected onboard from 17 subjects across four behaviour classes. The proposed model performs channel-level early fusion to jointly process the heterogeneous six-channel time series, enhancing the extraction of complementary and discriminative representations from multi-sensor inputs. An experimental analysis was conducted, including a hyperparameter study, to assess the effects of class imbalance on model performance, compare the proposed approach with baseline and state-of-the-art models, and examine how different sensor configurations influence attention distribution within convolutional layers. Class imbalance affects model performance, with balanced training yielding 2.25 % higher accuracy than imbalanced training. A single accelerometer or gyroscope produces 8.43 % and 28.57 % lower accuracy than the fused accelerometer-gyroscope setup. The proposed model achieved the highest accuracy of 90.11 % with an inference time of 0.14 ms per sample compared with baseline and state-of-the-art models. Integration of wearable devices with human activity recognition algorithms is highly applicable to maritime scenarios and has strong potential to accurately distinguish fishermen’s behaviours, thereby providing a practical tool for monitoring human-related risk factors in maritime transportation.

Authors in the community:

• 

  C

  Carlos Guedes Soares

  ist11869

  Affiliations and sources

  IST > CENTEC

  Centro de Engenharia e Tecnologia Naval e Oceânica

VoR - Version of Record

Elsevier

https://www.sciencedirect.com/science/article/pii/S0924424725011574

Sensors and Actuators A: Physical

117351

398

0924-4247

other-engineering-and-technologies - Other engineering and technologies

Keywords

• Fishermen's behaviour recognition
• Wearable devices
• Fishermen's occupational safety

eng - English