A low-cost prototyping framework for human-robot desk interaction

Odoemelem, Henry; Laerhoven, Kristof Van

doi:10.1145/3410530.3414323

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Given the widespread usage of Raspberry PI in the Internet of Things (IoT) from human-robot interaction [23], [24] to smart-home applications [25], [26], we integrate Tesla in a system called Tesla-Rapture (Tesla for RAdar generated Point cloud gesTURE) which can be executed on a Raspberry PI 4. The architecture is depicted in Fig.…”

Section: Introductionmentioning

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.

show abstract

Section: Introductionmentioning

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.

View full text Add to dashboard Cite

show abstract

“…Given the widespread usage of Raspberry PI in Internet of Things (IoT) world from human-robot interaction [25], [26] to smart-home applications [27], [28], we integrate the proposed model in a system called Tesla-Rapture (Tesla for RAdar generated Point cloud gesTURE) on Raspberry PI 4, the architecture of which is depicted in Fig. 1.…”

Section: Introductionmentioning

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 low-cost prototyping framework for human-robot desk interaction

Cited by 2 publications

References 4 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

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

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