  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.
