We present a simple, yet effective and flexible method for action recognition
supporting multiple sensor modalities. Multivariate signal sequences are
encoded in an image and are then classified using a recently proposed
EfficientNet CNN architecture. Our focus was to find an approach that
generalizes well across different sensor modalities without specific adaptions
while still achieving good results. We apply our method to 4 action recognition
datasets containing skeleton sequences, inertial and motion capturing
measurements as well as \wifi fingerprints that range up to 120 action classes.
Our method defines the current best CNN-based approach on the NTU RGB+D 120
dataset, lifts the state of the art on the ARIL Wi-Fi dataset by +6.78%,
improves the UTD-MHAD inertial baseline by +14.4%, the UTD-MHAD skeleton
baseline by 1.13% and achieves 96.11% on the Simitate motion capturing data
(80/20 split). We further demonstrate experiments on both, modality fusion on a
signal level and signal reduction to prevent the representation from
overloading.

该研究提出了一种支持多种传感器模态的简单、有效和灵活的动作识别方法，将多元信号序列编码成图像，并使用最近提出的 EfficientNet CNN 架构进行分类，实验结果表明该方法在多个数据集上的表现均优于目前的激光雷达数据集识别方法。