Abstract
Recognition of human movements with radar for ambient activity monitoring is a developed area of research that yet presents outstanding challenges to address. In real environments, activities and movements are performed with seamless motion, with continuous transitions between activities of different duration and a large range of dynamic motions, compared with discrete activities of fixed-time lengths which are typically analysed in the literature. This paper proposes a novel approach based on recurrent LSTM and Bi-LSTM network architectures for continuous activity monitoring and classification. This approach uses radar data in the form of a continuous temporal sequence of micro-Doppler or range-time information, differently from from other conventional approaches based on convolutional networks that interpret the radar data as images. Experimental radar data involving 15 participants and different sequences of 6 actions are used to validate the proposed approach. It is demonstrated that using the Doppler-domain data together with the Bi-LSTM network and an optimal learning rate can achieve over 90% mean accuracy, whereas range-domain data only achieved approximately 76%. The details of the network architectures, insights in their behaviour as a function of key hyper-parameters such as the learning rate, and a discussion on their performance across are provided in the paper.
Highlights
R ADAR sensors in the context of short-range human monitoring are becoming increasingly popular, in applications such as activities classification in smart homes within the ambient assisted living framework, recognition of gestures for human-computer interaction, and contactless vital sign monitoring [1], [2]
Compared to the above state of the art methods, we investigate in this paper recurrent neural networks that interpret radar data as a temporal series and characterize the time-varying nature of a sequence of human activities and movements
We propose stacked Bidirectional Long Short Term Memory (LSTM) networks as a novel deep learning tool alternative to Deep Convolutional Neural Networks (DCNNs), to perform radar-based classification of these continuous sequences of human activities
Summary
R ADAR sensors in the context of short-range human monitoring are becoming increasingly popular, in applications such as activities classification in smart homes within the ambient assisted living framework, recognition of gestures for human-computer interaction, and contactless vital sign monitoring [1], [2]. Two categories of sensors can be used in all these applications, namely wearable and non-wearable sensors [3] The former are are usually attached to the body parts of the monitored subject with clasps or Velcro-straps, or are worn and carried in pockets. These sensors take fine resolution data from the specific movements of the human torso and limbs, characterized through their acceleration and angular velocity or displacements, or through. Date of publication July 1, 2020; date of current version October 16, 2020. The associate editor coordinating the review of this article and approving it for publication was Prof.
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