| Proj No. | A3174-251 |
| Title | Machine Learning-Based Indoor Human Detection Using 433 MHz RF Signals |
| Summary | Detecting human presence inside buildings using wireless signals has gained significant interest due to its potential applications in security, surveillance, and smart environments. Traditional RF communication technologies such as WiFi and Ultra-Wideband (UWB) experience significant signal attenuation when passing through walls, limiting their effectiveness in such scenarios. Low-frequency RF signals, particularly in the 433 MHz band, offer better penetration capabilities and can be leveraged for human detection. This project proposes a novel approach that integrates machine learning (ML) techniques to enhance the accuracy of human detection using Received Signal Strength Indicator (RSSI) values obtained from multiple low-power 433 MHz transceivers. |
| Supervisor | A/P Tan Soon Yim (Loc:S1 > S1 B1B > S1 B1B 44, Ext: +65 67904505) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Communication Lab (Loc: S2-B4c-17) |
| Single/Group: | Single |
| Area: | Wireless and Communications Engineering |
| ISP/RI/SMP/SCP?: |