Commercially available motion capture systems are expensive, can have rigid, non-breathable materials with moderate to strong compression, and are not aesthetically appealing to be worn in everyday life. This study aimed to investigate the feasibility of using fabric-based radio frequency identification (RFID) tags for received signal strength indicator (RSSI) human position-sensing classification using artificial neural networks. Subsequent to this, characterization of each antenna type was investigated. Models were tested using k-fold cross validation schemes using the classification metrics of overall accuracy, individual position accuracies, and cohen’s kappa statistic. In the supervised models for each participant, the tags with commercial antennas (CA), and the tags with the conductive fabric antennas (CFA) yielded overall classification rates of 95%, while the tags with embroidered antennas (EA) was 91%. The CFA, similar in thickness to the CA, showed a superior water vapor transmission rate and bending rigidity than the CA and EA. Misclassifications were generally due to either similar positions, or positions with similar signal strength signatures, resulting in ambiguity when training the artificial neural network. Concealing fabric-based antennas into wearable garments presents the opportunity for human-sensing applications to work in tandem with outpatient healthcare and virtual reality entertainment systems.