UWB Sensor Assisted Self-Quarantined Person Health Status Monitoring using LSTM

The severe acute respiratory syndrome virus (SARS-CoV-2), known as COVID-19, has brought untold hardship and deaths all over the world. Individuals affected by COVID-19 often experience respiratory difficulties along with fever, cough, and other symptoms. Social distancing and self-quarantine are strongly suggested by researchers to avoid the exponential spread of the disease. The ultra-wideband (UWB) sensor has recently offered remote monitoring and capturing respiratory signs by ensuring privacy. In this work, a UWB sensor is employed to observe the movement and respiration of a home-quarantined person for fourteen days. After collecting the information in realtime, a deep learning (DL) approach, the long-term short memory (LSTM) framework is further applied to detect the breathing and movement patterns. The experimental result depicts that the framework accomplished 99.93% accuracy with 2 misclassification costs. The proposed application shows promising possibilities into the Internet of medical things (IoMT), smart home health care support system (ShHeS), and practical use in COVID-19 pandemic emergency.