Eight features (VAR, MAD, CoV, AAC, WA, LCoV, LDASDV, WL) are extracted from thermal images using a microcontroller and sent wirelessly to a remote PC for data collection. The data collection includes both "human" and "no human" classes. The temperature during data collection is kept between 24°C and 26.5°C. To collect human class data features, a person sits in front of the wireless backpack at distances ranging from 25 cm to 1 meter. The collected feature datasets (12,062 x 8) are split into 70% for training and 30% for testing. The RF classifier is implemented with ten trees to reduce memory usage, resulting in a compact size of 1.65 KB. This embedded Random Forest (RF) classifier can efficiently operate on the proposed backpack with its 32KB SRAM and 256KB flash memory. The average processing time from feature extraction to the random forest algorithm for onboard human detection is 7.5 ms.