We have published a research paper at ISWC'22
and won the "Best Paper Award". TinyHAR is designed specifically for human activity recognition employing different saliency of multi modalities, multimodal collaboration, and temporal information extraction. Initial experimental results show that TinyHAR is several times smaller and often meets or even surpasses the performance of DeepConvLSTM, a state-of-the-art human activity recognition model.
TinyHAR: A Lightweight Deep Learning Model Designed for Human Activity Recognition
Best Paper Award
Zhou, Yexu; Zhao, Haibin; Huang, Yiran; Hefenbrock,
Michael; Riedel, Till; Beigl, Michael