Human activity classification with radar signal processing and machine learning

Abstract

As the number of older adults increases worldwide, new paradigms for indoor activity monitoring are required to keep people living at home independently longer. Radar-based human activity recognition has been identified as a sensing modality of choice because it is privacy-preserving and does not require end-users compliance or manipulation. In this paper, we explore the robustness of machine learning algorithms for human activity recognition using six different activities from the University of Glasgow dataset recorded with an FMCW radar. The raw radar data is pre-processed and represented using four different domains, namely, range-time, range-Doppler amplitude and phase diagrams, and Cadence Velocity Diagram. From those, salient features can be extracted and classified using Support Vector Machine, Stacked AutoEncoder, and Convolutional Neural Networks. The fusion of handcrafted features and features from CNN is applied to get the best scheme of classification with over 96% accuracy.

Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures

“…In this work, spectrograms are used to capture the various velocity patterns of different body parts in human movements. However, recent works [17], [82], [83] have shown that combining multidomain information for HAR can also be advantageous, e.g., combining the RD, DT, CVD, and RT information.…”

Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

“…Up to now, the most common methods of human activity detection are visionbased detection like using cameras and sensor-based detection such as using wearable sensors, radar and smartphone sensors [3,4]. Among all the methods, radar technology outperforms the other for the following aspects [5][6][7][8]. Environmental insensitivity: radar detection is not influenced by harsh light; Contactless Sensing: users do not need to wear or connect with any devices, which provides a high capability of comfort and convenience; Privacy Protection: radar technology collects human activity data without showing their actual images, ensuring the privacy of individuals.…”

“…As machine learning developed, many researchers began to directly consider the grayscale of RGB images of spectrograms as features. Then, convolutional neural networks derived from vision-based classification were applied to those images for classification [3][4][5][6][7]10,22]. Compared with the conventional hand-crafted feature extraction approaches, the use of deep learning technology can increase the accuracy in classification.…”

Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging Radar and Deep Learning Classifiers

“…To the first category belong, studies where handcrafted features are extracted from the pre-processed radar data and then used together with supervised Machine Learning algorithms. For instance, studies such as [11]- [13] used handcrafted features and machine learning algorithms (e.g. Support Vector Machine, SVM) to classify different human activities, intended as individual motions performed by human subjects (e.g., walking, sitting still, boxing, and so on).…”