Abstract
This paper concerns the design, analysis, and simulation of a 3D non-stationary channel model fed with inertial measurement unit (IMU) data. The work in this paper provides a framework for simulating the micro-Doppler signatures of indoor channels for human activity recognition by using radio-frequency-based sensing technologies. The major human body segments, such as wrists, ankles, torso, and head, are modelled as a cluster of moving point scatterers. We provide expressions for the time variant (TV) speed and TV angles of motion based on 3D trajectories of the moving person. Moreover, we present mathematical expressions for the TV Doppler shifts and TV path gains associated with each moving point scatterer. Furthermore, a model of the non-stationary time variant channel transfer function (TV-CTF) is provided, which takes into account the effects caused by a moving person as well as fixed objects, such as furniture, walls, and ceiling. The micro-Doppler signatures of the moving person is extracted from the TV-CTF by employing the concept of the spectrogram, whose expression is also provided in closed form. Our model is confirmed by channel state information (CSI) measurements taken during walking, falling, and sitting activities. The proposed channel model is fed with IMU data that has been collected. We evaluate the micro-Doppler signature of the model and CSI measurements. The results show a good agreement between the spectrograms and the TV mean Doppler shifts of our IMU-driven channel model and the measured CSI. The proposed model enables a paradigm shift from traditional experimental-based approaches to future simulation-based approaches for the design of human activity recognition systems.
Highlights
The unsupervised monitoring of human mobility parameters during the activities of daily living is generating a high interest in the medical community, especially after the explicit recommendation of the US Food and Drug Administration [1] and the European Medicines Agency [2] that it is desirable to include information from portable or context-aware systems in clinical trials
The results show a good agreement between the spectrograms and the time variant (TV) mean Doppler shifts of our inertial measurement unit (IMU)-driven channel model and the measured channel state information (CSI)
In this paper, we demonstrated the possibility of designing an IMU-driven non-stationary channel model for human activity recognition
Summary
The unsupervised monitoring of human mobility parameters during the activities of daily living is generating a high interest in the medical community, especially after the explicit recommendation of the US Food and Drug Administration [1] and the European Medicines Agency [2] that it is desirable to include information from portable or context-aware systems in clinical trials. To the best of our knowledge, inertial measurement units (IMUs) have not been used to simulate the micro-Doppler signatures of CSI channel models under the influence of human activities. We present an IMU-driven non-stationary channel model that enables to simulate the multipath components associated with different body segments. Such a model allows for in-depth understanding of the parameters that have influence on the Doppler shifts caused by the moving body segments.
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