TY - JOUR
T1 - Wearable intelligence for driving safety
T2 - From multimodal wearable sensing to driver disengagement recognition
AU - Sun, Dongxian
AU - Qi, Changxin
AU - Tan, Haiqiu
AU - Shi, Jian
AU - Zhang, Haodong
AU - Luo, Bingxin
AU - Wang, Wuhong
AU - Guo, Baicang
AU - Qin, Yong
AU - Guo, Hongwei
N1 - Publisher Copyright:
© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/9
Y1 - 2026/9
N2 - Driver disengagement, including operational and attentional disengagement, significantly increases driving risk during human-vehicle co-driving, necessitating effective monitoring of disengagement behaviors. Unlike existing studies that rely on third-person perspective (TPP) visual information for indirect inference, this study proposes a wearable sensing-based driver disengagement recognition system, termed HV4DDR, which enables fine-grained recognition through the fusion of hand-motion sensing and first-person perspective (FPP) visual information. The system comprises a flexible smart glove, smart glasses, and a deep learning-based recognition algorithm. Specifically, a self-powered piezoelectric nanogenerator-based sensing unit (P-SU) is developed and integrated into a flexible smart glove (P-FSG) to directly capture temporal behavioral features of the driver’s hands, a primary site of disengagement, while smart glasses synchronously record first-person visual attention. To effectively exploit the multimodal data, a knowledge distillation network (KITE) is constructed to learn the intrinsic mapping between sensing signals and disengagement behaviors, enabling lightweight yet high-performance inference suitable for vehicular edge computing scenarios. Experimental results on a wearable multimodal dataset for driver disengagement recognition (WM-DDR) demonstrate an accuracy of 99.92% with only 3.29 million parameters and an inference latency of 10.41 ms. Furthermore, real-vehicle and simulation-based experiments validate that the developed DriCare system based on HV4DDR significantly enhances driving safety under both manual and automated driving modes. This work establishes a scalable and practical framework for real-time, fine-grained driver disengagement recognition, highlighting the potential of self-powered piezoelectric nanogenerator-enabled wearable sensing systems for intelligent transportation safety.
AB - Driver disengagement, including operational and attentional disengagement, significantly increases driving risk during human-vehicle co-driving, necessitating effective monitoring of disengagement behaviors. Unlike existing studies that rely on third-person perspective (TPP) visual information for indirect inference, this study proposes a wearable sensing-based driver disengagement recognition system, termed HV4DDR, which enables fine-grained recognition through the fusion of hand-motion sensing and first-person perspective (FPP) visual information. The system comprises a flexible smart glove, smart glasses, and a deep learning-based recognition algorithm. Specifically, a self-powered piezoelectric nanogenerator-based sensing unit (P-SU) is developed and integrated into a flexible smart glove (P-FSG) to directly capture temporal behavioral features of the driver’s hands, a primary site of disengagement, while smart glasses synchronously record first-person visual attention. To effectively exploit the multimodal data, a knowledge distillation network (KITE) is constructed to learn the intrinsic mapping between sensing signals and disengagement behaviors, enabling lightweight yet high-performance inference suitable for vehicular edge computing scenarios. Experimental results on a wearable multimodal dataset for driver disengagement recognition (WM-DDR) demonstrate an accuracy of 99.92% with only 3.29 million parameters and an inference latency of 10.41 ms. Furthermore, real-vehicle and simulation-based experiments validate that the developed DriCare system based on HV4DDR significantly enhances driving safety under both manual and automated driving modes. This work establishes a scalable and practical framework for real-time, fine-grained driver disengagement recognition, highlighting the potential of self-powered piezoelectric nanogenerator-enabled wearable sensing systems for intelligent transportation safety.
KW - Driver disengagement recognition
KW - Human-vehicle interaction
KW - Piezoelectric nanogenerator
KW - Wearable intelligence
UR - https://www.scopus.com/pages/publications/105043582226
U2 - 10.1016/j.nanoen.2026.112171
DO - 10.1016/j.nanoen.2026.112171
M3 - Article
AN - SCOPUS:105043582226
SN - 2211-2855
VL - 156
JO - Nano Energy
JF - Nano Energy
M1 - 112171
ER -