A Model-Based RF Hand Motion Detection System for Shadowing Scenarios

Dong, Zaili; Li, Fangmin; Ying, Julang; Pahlavan, Kaveh

doi:10.1109/access.2020.3004513

Cited by 7 publications

(5 citation statements)

References 44 publications

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“…Recent years have witnessed the emergence of mmWave radar point cloud human sensing due to the availability of commercial hardware that is miniaturized and low cost (e.g. hand tracking [49], gesture recognition [16], [50] activity recognition [43], gait recognition [46], or positioning [45]).…”

Section: Static Point Cloudsmentioning

confidence: 99%

Tesla-Rapture: A Lightweight Gesture Recognition System From mmWave Radar Sparse Point Clouds

Salami¹,

Hasibi²,

Palipana³

et al. 2023

IEEE Trans. on Mobile Comput.

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We present Tesla-Rapture, a gesture recognition system for sparse point clouds generated by mmWave Radars. State of the art gesture recognition models are either too resource consuming or not sufficiently accurate for the integration into real-life scenarios using wearable or constrained equipment such as IoT devices (e.g. Raspberry PI), XR hardware (e.g. HoloLens), or smart-phones. To tackle this issue, we have developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds. The model outperforms the state of the art on three datasets in terms of accuracy while reducing the computational complexity and, hence, the execution time. In particular, the approach, is able to predict a gesture almost 8 times faster than the most accurate competitor. Our performance evaluation in different scenarios (environments, angles, distances) shows that Tesla generalizes well and improves the accuracy up to 20% in challenging scenarios, such as a through-wall setting and sensing at extreme angles. Utilizing Tesla, we develop Tesla-Rapture, a real-time implementation using a mmWave Radar on a Raspberry PI 4 and evaluate its accuracy and time-complexity. We also publish the source code, the trained models, and the implementation of the model for embedded devices.

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Section: Static Point Cloudsmentioning

confidence: 99%

Tesla-Rapture: A Lightweight Gesture Recognition System From mmWave Radar Sparse Point Clouds

Salami¹,

Hasibi²,

Palipana³

et al. 2023

IEEE Trans. on Mobile Comput.

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show abstract

“…Content may change prior to final publication. [26] and motion detection [18,19]. Parameters associated with the Doppler spectrum can also be used for range estimation using ML algorithms.…”

Section: ) Shape Of Fading Distributionmentioning

confidence: 99%

“…Traditionally, RSSI of RF signals have been used in Doppler radars and for GPS signals to measure the speed to correct the estimated range for moving objects. In recent years, using RSSI signal in time and in frequency with intelligent algorithms have attracted attention in new emerging fields for big data such as gesture [26] and motion detection [18] , [19] . Parameters associated with the Doppler spectrum can also be used for range estimation using ML algorithms.…”

Section: Features Of Rssi Short Range Fadingmentioning

confidence: 99%

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

Pahlavan

Agu

2021

IEEE Access

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The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%∼19.98% better precision, getting closer to achievable bounds for estimation.

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“…Recent years have witnessed the emergence of mmWave radar point cloud human sensing due to the availability of commercial hardware that is miniature and low cost (e.g. hand tracking [50], gesture recognition [18], [51] activity recognition [45], gait recognition [48], or positioning [47]).…”

Section: Static Point Cloudsmentioning

confidence: 99%

Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds

Salami,

Hasibi,

Palipana

et al. 2021

Preprint

View full text Add to dashboard Cite

We present Tesla-Rapture, a gesture recognition interface for point clouds generated by mmWave Radars. State of the art gesture recognition models are either too resource consuming or not sufficiently accurate for integration into real-life scenarios using wearable or constrained equipment such as IoT devices (e.g. Raspberry PI), XR hardware (e.g. HoloLens), or smart-phones. To tackle this issue, we developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds. The model outperforms the state of the art on two datasets in terms of accuracy while reducing the computational complexity and, hence, the execution time. In particular, the approach, is able to predict a gesture almost 8 times faster than the most accurate competitor. Our performance evaluation in different scenarios (environments, angles, distances) shows that Tesla generalizes well and improves the accuracy up to 20% in challenging scenarios like a through-wall setting and sensing at extreme angles. Utilizing Tesla, we develop Tesla-Rapture, a real-time implementation using a mmWave Radar on a Raspberry PI 4 and evaluate its accuracy and time-complexity. We also publish the source code, the trained models, and the implementation of the model for embedded devices.

show abstract

A Model-Based RF Hand Motion Detection System for Shadowing Scenarios

Cited by 7 publications

References 44 publications

Tesla-Rapture: A Lightweight Gesture Recognition System From mmWave Radar Sparse Point Clouds

Tesla-Rapture: A Lightweight Gesture Recognition System From mmWave Radar Sparse Point Clouds

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal

Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds

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