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

Dariush Salami, Ramin Hasibi, Sameera Palipana, Petar Popovski, Tom Michoel, Stephan Sigg

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
76 Downloads (Pure)


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.

Original languageEnglish
JournalIEEE Transactions on Mobile Computing
Early online date2022
Publication statusE-pub ahead of print - 2022
MoE publication typeA1 Journal article-refereed


  • Gesture-recognition
  • Graph-convolution
  • Machine-learning
  • mmwave radar
  • Sensing


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