In this work, we present a cloud-based system for noncontact, real-time recognition, and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition, and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a data set of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network [gated recurrent unit (GRU)] model selected for real-time deployment due to its balance of speed and accuracy compared to 2-D convolutional neural network long short-term memory (2D-CNNLSTM) and long short-term memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

中文翻译：

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基于毫米波 FMCW 雷达和云计算的人工智能非接触式家用步态监测和活动识别系统


在这项工作中，我们提出了一个基于云的系统，用于非接触式、实时识别和监控家庭环境中的身体活动和步行时间。该系统采用独立的基于物联网 (IoT) 的毫米波雷达设备和深度学习模型，以实现自主、自由生活的活动识别和步态分析。为了训练深度学习模型，我们利用从现实家庭活动数据集生成的距离多普勒图。根据准确性和预测时间评估多种深度学习模型的性能，选择门控循环网络[门控循环单元（GRU）]模型进行实时部署，因为与二维卷积相比，其速度和准确性的平衡神经网络长短期记忆（2D-CNNLSTM）和长短期记忆（LSTM）模型。 GRU 模型对受过训练的受试者的家庭身体活动进行分类的总体准确度为 93%，对于新受试者的准确度为 86%。除了识别和区分各种活动和行走时段外，系统还记录受试者随时间的活动水平、洗手间使用频率、睡眠/久坐/活动/外出持续时间、当前状态和步态参数。重要的是，该系统不需要受试者佩戴或携带任何额外的设备来维护隐私。