In　this　study,　we　designed　and　developed　an　intelligent　exercise　guidance　system　based　on　smart　clothing.　The　system　comprised　smart　clothing　for　electrocardiogram　(ECG)　signal　acquisition　and　heart　rate　(HR)　monitoring,　an　exercise　control　application　program,　and　a　cloud　server.　Music　beats　were　used　to　guide　the　exercise　routine.　The　use　of　an　empirical　mode　decomposition　(EMD)-based　ECG　signal　denoising　algorithm　and　a　quadratic　polynomial　regression　model　(QPRM)　of　HR　and　running　cadence　(running　steps　per　minute　guided　by　music　beats)　were　proposed　for　the　system.　Five　types　of　experiments　were　conducted:　Experiments　I　and　II,　R-peak　detection;　Experiment　III,　preset　QPRMs;　Experiment　IV,　degree　of　completion　of　exercises;　and　Experiment　V,　comparison　of　preset　and　trained　QPRMs.　The　average　accuracy　and　sensitivity　of　the　EMD-based　R-peak　detection　method　were　respectively　99.8%　and　94.87%　for　ECG　data　from　the　MIT-BIH　Arrhythmia　Database　and　96.46%　and　98.75%　for　ECG　data　collected　from　university　students　during　the　walking　exercise.　The　coefficient　of　determination　and　the　mean　absolute　percentage　error　(MAPE)　of　the　QPRMs　were　respectively　97.21%　and　3.12%　for　increasing　HR　and　98.09%　and　2.06%　for　decreasing　HR.　The　average　degrees　of　completion　for　warmup,　training,　and　cooldown　exercise　stages　were　97.05%,　91.91%,　and　98.32%,　respectively.　The　MAPEs　of　the　preset　and　trained　QPRMs　were　respectively　6.37%　and　3.84%　for　increasing　HR　and　5.25%　and　3.57%　for　decreasing　HR.　The　experimental　results　demonstrated　the　effectiveness　of　the　proposed　system　in　exercise　guidance.