可穿戴计算机
鉴定(生物学)
计算机科学
步态
人工神经网络
人机交互
人工智能
物理医学与康复
医学
嵌入式系统
生物
植物
作者
Sijia Yi,Zhanyong Mei,Kamen Ivanov,Zijie Mei,Tong He,Hui Zeng
标识
DOI:10.1016/j.sna.2024.115478
摘要
Advances in sensor technology have sparked a wave of innovation and progress in the domain of wearable devices. However, ensuring the security of these devices remains a critical concern for their users. This study proposes a novel identification framework that employs custom multimodal sensing insoles and deep learning techniques to contribute to the data security of wearable devices in a user-transparent manner. The insole incorporates an inertial sensing unit and ten force sensors to capture kinematic and kinetic information. For this implementation, we propose a lightweight multiclass classification deep model based on depthwise separable convolution and an enhanced attention mechanism, which operates over multiple signal channels. In designing the recognition model, we focused on two pivotal factors: (1) achieving an optimal balance between computational complexity and recognition accuracy, which is essential for models that operate on devices with limited computational power, and (2) introducing an enhanced attention mechanism that ensures high recognition accuracy without the stringent requirement for precise temporal alignment between sensor channels, which can be technically challenging to attain. We conducted extensive experiments to validate the proposed approach, utilizing a walking dataset collected from 82 young subjects. The results indicate that when using data from the multimodal sensing insole, a high recognition accuracy of 97.96% for person identification is achieved, with a notably low model parameter count of 22,462. The results support the feasibility and effectiveness of sensing footwear-based gait identification. The proposed approach holds significant potential for enhancing user authentication methods across a broad range of scenarios while ensuring transparency and user-friendliness.
科研通智能强力驱动
Strongly Powered by AbleSci AI