Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Abdelgawwad, Ahmed; Borhani, Alireza; Pätzold, Matthias

doi:10.3390/s20041049

Cited by 10 publications

(11 citation statements)

References 45 publications

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“…In the phase sanitization method, a linear transformation is applied to determine the true phases from the measured distorted phases of 30 OFDM subcarriers. Although the transformed phases show some patterns compared to the measured phases, another study [48] reported that the transformed phases still do not accurately disclose the Doppler characteristics of the measured CSI data.…”

Section: ) Phase Correctionmentioning

confidence: 90%

“…There are several methods that can be employed to eliminate phase distortions. For example, the phase sanitization method [25], [47], the phase calibration method [46], [48], and the CSI ratio method [49]. In the phase sanitization method, a linear transformation is applied to determine the true phases from the measured distorted phases of 30 OFDM subcarriers.…”

Section: ) Phase Correctionmentioning

confidence: 99%

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WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

et al. 2020

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Robust and accurate human activity recognition (HAR) systems are essential to many humancentric services within active assisted living and healthcare facilities. Traditional HAR systems mostly leverage a single sensing modality (e.g., either wearable, vision, or radio frequency sensing) combined with machine learning techniques to recognize human activities. Such unimodal HAR systems do not cope well with real-time changes in the environment. To overcome this limitation, new HAR systems that incorporate multiple sensing modalities are needed. Multiple diverse sensors can provide more accurate and complete information resulting in better recognition of the performed activities. This article presents WiWeHAR-a multimodal HAR system that uses combined Wi-Fi and wearable sensing modalities to simultaneously sense the performed activities. WiWeHAR makes use of standard Wi-Fi network interface cards to collect the channel state information (CSI) and a wearable inertial measurement unit (IMU) consisting of accelerometer, gyroscope, magnetometer sensors to collect the user's local body movements. We compute the time-variant mean Doppler shift (MDS) from the processed CSI data and magnitude from the inertial data for each sensor of the IMU. Thereafter, we separately extract various time-and frequencydomain features from the magnitude data and the MDS. We apply feature-level fusion to combine the extracted features, and finally supervised learning techniques are used to recognize the performed activities. We evaluate the performance of WiWeHAR by using a multimodal human activity data set, which was obtained from 9 participants. Each participant carried out four activities, such as walking, falling, sitting, and picking up an object from the floor. Our results indicate that the proposed multimodal WiWeHAR system outperforms the unimodal CSI, accelerometer, gyroscope, and magnetometer HAR systems and achieves an overall recognition accuracy of 99.6%-100%.

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Section: ) Phase Correctionmentioning

confidence: 90%

Section: ) Phase Correctionmentioning

confidence: 99%

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

et al. 2020

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“…First, Wi-Sense processes the CSI data to remove the impact of noise and fixed (or nonmoving) objects present in the environment. The processed CSI data is then used to compute the spectrogram 1 of the CSI data corresponding to different human activities. These spectrograms represent the radio channel Doppler characteristics caused by fixed and moving objects present in the environment.…”

Section: Contributions and Organizationmentioning

confidence: 99%

“…In the literature, there exist three different methods that can be used to eliminate the phase distortions, namely the phase sanitization method [6], the phase calibration method with back-to-back channel configuration [18], and the CSI ratio method [42]. The phase sanitization method applies a linear transformation to the measured (distorted) phases to determine the true phase However, it has been reported in [1] that the true phases obtained after applying the phase sanitization method do not unfold the Doppler features of the measured CSI data. The back-to-back channel configuration method [18] splits the transmitted signal into two similar signals using a two-way splitter, which is connected to the T x .…”

Section: Phase Correctionmentioning

confidence: 99%

Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

et al. 2021

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A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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“…Content may change prior to final publication. behaviour [54], [55]. Then, the CTFĤ (t, f q ) was summed over the subcarriers to obtain the complex channel gainμ(t)…”

Section: A Measurement Scenariomentioning

confidence: 99%

A Trajectory-Driven 3D Channel Model for Human Activity Recognition

2021

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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 radiofrequency-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.INDEX TERMS Human activity recognition, non-stationary fading channels, channel state information, channel transfer function, spectrogram, time-variant Doppler power characteristics, micro-Doppler signature, channel measurements, inertial measurement units, Internet of things, wireless sensing.

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Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Cited by 10 publications

References 45 publications

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

A Trajectory-Driven 3D Channel Model for Human Activity Recognition

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