FMCW mmWave Radar for Detection of Pulse, Breathing and Fall within Home Care

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

Author: Axel Trange; [2021]

Keywords: ;

Abstract: Countless of elderly people fall and get hurt within their homes, worldwide, every year, and as they can not always reach out for help themselves, they end up helplessly waiting for someone to notice what has occurred. Throughout this work, it is investigated if remote sensing of the mmWave FMCW radar IWR6843AOPEVM can be adopted to detect the incident of falls, and also detect the vital signs of the human subject. The goal is to prove that this is possible in a home care environment. By locating the sub-range resolution oscillatory motions, caused by breathing and heartbeats, and unwrapping consecutive phase measurements of multiple range bins and multiple virtual antennas, human heart rate for seated position is estimated with a RMS-Error of 3.49 beats/min (at 1.0 m range with 120_ FoV) for a 130 second time sequence. Analogously, breathing rate is estimated for a sitting person with a RMS-Error of 0.29 breaths/min (at 4.0 m range with 120_ FoV) for a 100 second time sequence. Different estimation methods are evaluated, such as Fourier transform (FFT), Chirp Z-transform (CZT) and autocorrelation peak-finding, where the CZT approach is deemed to provide the best estimations. Methods are presented to minimize spectral leakage, improve spectral resolution and reduce breathing harmonics. The measurements were performed in a home care environment and the heart rate ones were compared to measurements of the FDA approved pulse oximeter CMS50D+. 729 own recordings of range-doppler-time data was collected for the fall detection, which was fed into a convolutional neural network to extract image features. These features were then used as training and softmax classified by a LSTM recurrent neural network for multi-label classification. Promising results on separate test data showed a balanced accuracy for fall detection as 92% with a direct 2% false positive rate and 15% false negative rate. The area under the ROC curve for the falls was close to 1, namely 0.99, illustrating that the false negative rate may be chosen as lower at the cost of slightly more false alarms. A sweet spot in the ROC curve suggested that fall detection was possible with a 3.5% false positive rate and 6% false negative rate. 

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