Real-Time Multi-Gesture Recognition using 77 GHz FMCW MIMO Single Chip Radar

Goswami, Piyali; Rao, Sandeep; Bharadwaj, Sachin; Nguyen, Amanda

doi:10.1109/icce.2019.8662006

Cited by 28 publications

(15 citation statements)

References 8 publications

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“…However, device-free gesture recognition leverages signals of the commercial hardware devices for the gesture recognition task. These systems are categorized as: radar-based [32], RSSI [33,34] and CSI-based [15,35].…”

Section: Related Workmentioning

confidence: 99%

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

Ahmed

Ahmad

Phang

et al. 2019

Sensors

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Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.

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Section: Related Workmentioning

confidence: 99%

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

Ahmed

Ahmad

Phang

et al. 2019

Sensors

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show abstract

“…Future research may consider improvements in the bandwidth, the data rate, the Doppler resolution, and the applications of other classification algorithms, to identify smaller, faster actions as finger movements. Since gesture recognition based on the Artificial Neural Network (ANN) and temporal parameter sequences has been proven to be effective [44], we also expect the spectral parameter sequences to be used in neural-network-based gesture recognition, and attempts to compare the classification performances of different network structures. It is likely that this kind of multi-dimensional, high-resolution micro-radar will become the mainstream system for future gesture recognition, as its precise positioning and measurement capability provide enough information for identification without the need for complex micro-motion analysis.…”

Section: E Discussionmentioning

confidence: 99%

“…The signal processing algorithm on the radar also adopted the processing scheme disclosed by TI. For more detailed signal processing methods and principles, please refer to the relevant introduction on the TI website [43] or related literatures [44], [45].…”

Section: A Radar Sensor System and Placementmentioning

confidence: 99%

“…Using the radar parameter data collected by the same type of radar chip, a deep-learning based gesture recognition was proposed by TI [44]. Another difference is that the temporal features were extracted in [44] instead of the spectrum features in our scheme. The tested classification rates of seven gestures, range from 95% to 99%, are similar to those of our method.…”

Section: B Comparative Analysis Of Alogrithm Performancementioning

confidence: 99%

“…G8 G9 G10 If other processing functions need to be implemented on the same chip, the classification time of this magnitude might be an obstacle to the overall real-time performance. Moreover, the classification time of the deep-learning-based scheme has been tested in [44] with the radar chip AWR1642 and the result is about 1 ms. Considering that the processing ability of AWR1642 is stronger than that of TC397, our classification algorithm obviously has better real-time performance.…”

Section: G7mentioning

confidence: 99%

See 2 more Smart Citations

Spectrum-Based Hand Gesture Recognition Using Millimeter-Wave Radar Parameter Measurements

Liu

et al. 2019

IEEE Access

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Radar sensors offer several advantages over optical sensors in the gesture recognition for remote control of electronic devices. In this paper, we investigate the feasibility of human gesture recognition using the spectra of radar measurement parameters. With the combination of radar theory and classification methods, we found that the frequencies of different gestures' parameters could be utilized as features for gesture recognition. Six kinds of periodic dynamic gestures are designed to avoid the complexity of defining and extracting the start and end of the dynamic gesture. In addition to the frequency ratio, we also extracted some features related to motion range and detection coherence to eliminate the interferences brought by the unintended gestures. The decision tree classifier designed on the basis of experimental phenomena can guarantee effective classification between different gestures, and in general, the correct recognition rate of each gesture is higher than 90%. Finally, we collected the position and the Doppler velocity information of hand for classification by a W-band millimeter wave radar in the experiment and verified the usability of the proposed method.

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Word-level arabic sign language recognition using millimeter wave radar and convolutional neural networks

Alobaidlei,

Alsuwaidi,

Alfalasi

et al. 2024

AIP Conference Proceedings

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Real-Time Multi-Gesture Recognition using 77 GHz FMCW MIMO Single Chip Radar

Cited by 28 publications

References 8 publications

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

Spectrum-Based Hand Gesture Recognition Using Millimeter-Wave Radar Parameter Measurements

Word-level arabic sign language recognition using millimeter wave radar and convolutional neural networks

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