Light Weight Human Activity Recognition using Raspberry PI IoT Edge and Reduced Features from Smartphones | ||
| IJCI. International Journal of Computers and Information | ||
| Article 2, Volume 10, Issue 3, November 2023, Pages 1-8 PDF (628.22 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijci.2023.235233.1121 | ||
| Authors | ||
| Ayman Wazwaz* 1; Khalid Amin2; Noura Semary3; Tamer Ghanem4 | ||
| 1Computer Engineering Department, College of Information Technology and Computer Engineering, Palestine Polytechnic University, Hebron, Palestine | ||
| 2Information Technology dept., Faculty of Computers and Information, Menoufia University, Shebin El Kom, Egypt | ||
| 3Department of Information Technology, Faculty of Computers and Information Menoufia University, Shebin El Kom, Egypt | ||
| 4Department of Information Technology, Faculty of Computers and Information, Menofia University, Shebin El Kom, Egypt | ||
| Abstract | ||
| Abstract— Different applications used cloud computing, machine learning, and the Internet of Things (IoT). Transferring data from the local network to the cloud for processing causes huge traffic and delay. IoT services, like Human Activity Recognition (HAR), use IoT edge options to be near the place of telemetry data generation that decreases traffic and speeds up the results. This study used three smartphones with built-in accelerometers; three parameters from each accelerometer to predict human activities. While building the models at the Raspberry PI edge, the most important features were determined using Principal Component Analysis (PCA). Light GBM, Extra Trees, and Random Forest algorithms were employed to evaluate the best models. Significant performance improvements in training and real-time results were achieved using the top related features at the IoT edge. The Light GBM recognized four different activities with 99.6% accuracy when all nine features were used, and with more than 98% accuracy when less than half of the features were used. To process one prediction, Raspberry PI 3 took 6.1 milliseconds, Raspberry PI 4 took less than 3 milliseconds if all features are used, while Microsoft Azure cloud took 5.8 seconds, including prediction time and network latency. | ||
| Keywords | ||
| IoT Edge; Raspberry PI; Human Activity Recognition; Feature Selection; Machine Learning | ||
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