Real　-time　emotion　recognition　via　wearable　devices　is　a　pivotal　component　of　health　monitoring　and　human-　computer　interaction.　To　realize　this　objective,　a　spiking　feed-forward　neural　networks　(SFNNs)　model　was　developed,　which　leverages　six　physiological　signals　from　the　psychophysiology　of　positive　and　negative　emotions　(POPANE)　dataset　to　construct　feature　vectors.　By　converting　well-trained　artificial　neural　networks　(ANNs)　to　spiking　neural　networks　(SNNs)　and　employing　weight　normalization　techniques,　the　SFNNs　with　data-based　normalization　achieved　a　maximum　classification　accuracy　of　88.17%　at　a　maximum　input　firing　rate　of　1000　Hz.　In　comparison　to　existing　models,　the　SFNNs　model　integrates　multimodal　physiological　signals　to　classify　six　discrete　emotions,　demonstrating　high　classification　performance　and　rapid　convergence　speed,　rendering　it　ideal　for　real　-time　emotion　recognition.　This　work　has　potential　applications　in　psychological　diagnosis　and　medical　rehabilitation　through　the　use　of　wearable　wristbands.