The proliferation of deep learning has significantly advanced various fields,
yet Human Activity Recognition (HAR) has not fully capitalized on these
developments, primarily due to the scarcity of labeled datasets. Despite the
integration of advanced Inertial Measurement Units (IMUs) in ubiquitous
wearable devices like smartwatches and fitness trackers, which offer
self-labeled activity data from users, the volume of labeled data remains
insufficient compared to domains where deep learning has achieved remarkable
success. Addressing this gap, in this paper, we propose a novel approach to
improve wearable sensor-based HAR by introducing a pose-to-sensor network model
that generates sensor data directly from 3D skeleton pose sequences. our method
simultaneously trains the pose-to-sensor network and a human activity
classifier, optimizing both data reconstruction and activity recognition. Our
contributions include the integration of simultaneous training, direct
pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit
dataset. Experimental results demonstrate the superiority of our framework with
significant performance improvements over baseline methods.

为了改进基于可穿戴传感器的人体活动识别，本文提出了一种从 3D 骨架姿势序列中直接生成传感器数据的姿势到传感器网络模型，并同时训练姿势到传感器网络和人体活动分类器，通过优化数据重构和活动识别来改进性能。实验结果表明该框架优于基线方法，并在 MM-Fit 数据集上取得了显著的性能提升。