EarCough: Enabling Continuous Subject Cough Event Detection on Hearables

Zhang, Xiyuxing; Wang, Yuntao; Zhang, Jingru; Yang, Yaqing; Patel, Shwetak; Shi, Yuanchun

doi:10.1145/3544549.3585903

Cited by 5 publications

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Mmtsa

Gao,

wang,

Chen

et al. 2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

Multimodal sensors provide complementary information to develop accurate machine-learning methods for human activity recognition (HAR), but introduce significantly higher computational load, which reduces efficiency. This paper proposes an efficient multimodal neural architecture for HAR using an RGB camera and inertial measurement units (IMUs) called Multimodal Temporal Segment Attention Network (MMTSA). MMTSA first transforms IMU sensor data into a temporal and structure-preserving gray-scale image using the Gramian Angular Field (GAF), representing the inherent properties of human activities. MMTSA then applies a multimodal sparse sampling method to reduce data redundancy. Lastly, MMTSA adopts an inter-segment attention module for efficient multimodal fusion. Using three well-established public datasets, we evaluated MMTSA's effectiveness and efficiency in HAR. Results show that our method achieves superior performance improvements (11.13% of cross-subject F1-score on the MMAct dataset) than the previous state-of-the-art (SOTA) methods. The ablation study and analysis suggest that MMTSA's effectiveness in fusing multimodal data for accurate HAR. The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.

show abstract