Range-Doppler radar sensor fusion for fall detection

Erol, Barış; Amin, Moeness G.; Boashash, Boualem

doi:10.1109/radar.2017.7944316

Cited by 63 publications

(40 citation statements)

References 17 publications

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“…In essence, the envelopes of the signal power concentration in the timefrequency domain may uniquely characterize the different hand motions. The envelopes of the MD signature can be determined by an energy-based thresholding algorithm [31]. First, the effective bandwidth of each gesture frequency spectrum is computed.…”

Section: B Extraction Of the MD Signature Envelopesmentioning

confidence: 99%

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin

Zeng

Shan

2019

2019 IEEE Radar Conference (RadarConf)

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We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler (MD) signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies that are generated during each gesture act. We detect the positive and negative frequency envelopes of MD separately, and form a feature vector of their augmentation. We use the k-nearest neighbor (kNN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates.

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Section: B Extraction Of the MD Signature Envelopesmentioning

confidence: 99%

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin

Zeng

Shan

2019

2019 IEEE Radar Conference (RadarConf)

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“…Finding the onset and offset times of arm motion becomes necessary to determine the individual motion boundaries and time span. These times can be obtained from the PBC [44,45], which measures the signal energy in the spectrogram within specific frequency bands. In particular, we compute…”

Section: Power Burst Curve (Pbc)mentioning

confidence: 99%

“…In this respect, the envelopes can accurately characterize different arm motions. An energy-based thresholding algorithm discussed in [17,44] can be applied to extract the envelopes. First, the maximum positive and negative Doppler frequencies are determined by computing the effective bandwidth of each motion from the spectrogram.…”

Section: Extraction Of the Maximum Instantaneous Doppler Frequency Simentioning

confidence: 99%

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

2020

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Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

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“…In order to separate the two consecutive in-place motions, where range information is no longer a factor, we deal with the radar scattering as a deterministic signal, rather that a random process, 31 and measure the rise and fall of the signal energy in S(n, k) over slow-time which is known as the power burst curve (PBC). 32,33 The Doppler frequencies at and around the zero Doppler axis are not of interest, since their strong power gain biases the PBC. In this work, the chosen frequency bands for power computation are between K P 1 = 20Hz and Figure 11: Walking towards the radar followed by sitting and then standing.…”

Section: Power Burst Curve (Pbc)mentioning

confidence: 99%

RF sensing for continuous monitoring of human activities for home consumer applications

Amin

Ravisankar

Guendel

2019

Big Data: Learning, Analytics, and Applications

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Radar for indoor monitoring is an emerging area of research and development, covering and supporting different health and wellbeing applications of smart homes, assisted living, and medical diagnosis. We report on a successful RF sensing system for home monitoring applications. The system recognizes Activities of Daily Living(ADL) and detects unique motion characteristics, using data processing and training algorithms. We also examine the challenges of continuously monitoring various human activities which can be categorized into translation motions (active mode) and in-place motions (resting mode). We use the range-map, offered by a range-Doppler radar, to obtain the transition time between these two categories, characterized by changing and constant range values, respectively. This is achieved using the Radon transform that identifies straight lines of different slopes in the range-map image. Over the in-place motion time intervals, where activities have insignificant or negligible range swath, power threshold of the radar return micro-Doppler signatures,which is employed to define the time-spans of individual activities with insignificant or negligible range swath. Finding both the transition times and the time-spans of the different motions leads to improved classifications, as it avoids decisions rendered over time windows covering mixed activities.

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Range-Doppler radar sensor fusion for fall detection

Cited by 63 publications

References 17 publications

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

RF sensing for continuous monitoring of human activities for home consumer applications

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