Artificial Intelligence-Based Classification of Human Activities from mmWave Radar Point Clouds
18th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2026, Bucharest, Romanya, 2 - 03 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/ecai69016.2026.11613841
- Basıldığı Şehir: Bucharest
- Basıldığı Ülke: Romanya
- Anahtar Kelimeler: deep learning, human activity recognition, mmWave radar, point cloud, PointNet, real-time classification, ROS 2
- Kocaeli Üniversitesi Adresli: Evet
Özet
This paper presents a real time human activity classification system using three dimensional point clouds produced by millimeter wave radar and a PointNet based deep learning model. A Texas Instruments IWR6843ISK radar operating between 60 and 64 GHz was used to collect data from 10 subjects, including 5 female and 5 male participants, performing five distinct gestures: waist bending, arm flapping, arm swinging, forward bending, and lateral bending. Data were recorded through Robot Operating System 2, converted into sliding window segments of 500 points with a stride of 250 points, quality filtered, and normalized before being fed to a shared multilayer perceptron and global max pooling classifier. The final curated dataset contains 4,155 segments split into 3,324 training samples and 831 test samples. The model achieves 98 percent test accuracy and a macro F1 score of zero point ninety seven. In addition to a contextual comparison with literature baselines, an in house RadHAR reproduction on the same dataset reached 93 percent accuracy, confirming the advantage of direct raw point processing for this dataset. Real time inference runs at approximately 35 frames per second on Jetson Nano and approximately 25 frames per second on Raspberry Pi 4, demonstrating suitability for embedded and contactless monitoring in smart living, rehabilitation, and human machine interaction scenarios.