计算机科学
公制(单位)
雷达
实时计算
机器人
远程医疗
计算机视觉
人工智能
医疗保健
电信
工程类
运营管理
经济增长
经济
作者
Peijun Zhao,Chris Xiaoxuan Lu,Qingqing Wang,Changhao Chen,Linhai Xie,Mengyu Wang,Niki Trigoni,Andrew Markham
标识
DOI:10.1109/icra40945.2020.9197437
摘要
Heart rate monitoring at home is a useful metric for assessing health e.g. of the elderly or patients in post-operative recovery. Although non-contact heart rate monitoring has been widely explored, typically using a static, wall-mounted device, measurements are limited to a single room and sensitive to user orientation and position. In this work, we propose mBeats, a robot mounted millimeter wave (mmWave) radar system that provide periodic heart rate measurements under different user poses, without interfering in a users daily activities. mBeats contains a mmWave servoing module that adaptively adjusts the sensor angle to the best reflection pro le. Furthermore, mBeats features a deep neural network predictor, which can estimate heart rate from the lower leg and additionally provides estimation uncertainty. Through extensive experiments, we demonstrate accurate and robust operation of mBeats in a range of scenarios. We believe by integrating mobility and adaptability, mBeats can empower many down-stream healthcare applications at home, such as palliative care, post-operative rehabilitation and telemedicine.
科研通智能强力驱动
Strongly Powered by AbleSci AI