A Novel Lightweight Attention Network for Fall Detection in Internet of Medical Things

Internet of Medical Things (IoMT) is increasingly gaining attentions in fall detection because of its ability to sense, monitor and analyze, which can provide proper assistance to the elderly with fragile health conditions. As fall events are infrequent, its important to timely detect its occurrence in order to alleviate the harmless. This paper presents a Lightweight Attention Network Fall Detection (LA-FD) framework, which detects falls by analyzing gait acceleration signal (GAS). Then, we can take appropriate measures to mitigate the impact. LA-FD introduces depth-separated convolution to the Lightweight Attention Network module to reduce computational costs and model parameters. Additionally, relative positional offsets are incorporated into each self-attention module to enhance attention mechanisms' expressiveness. The result shows that LA-FD significantly reduces model size by 99.3% compared to the Transformer and 95.1% compared to CNN-LSTM, while only a 5% accuracy drop compared to the Transformer and 2% compared to CNN-LSTM.