In　this　study,　we　proposed　an　intelligent　health　monitoring　system　based　on　smart　clothing.　The　system　consisted　of　smart　clothing　and　sensing　component,　care　institution　control　platform,　and　mobile　device.　The　smart　clothing　is　a　wearable　device　for　electrocardiography　signal　collection　and　heart　rate　monitoring.　The　system　integrated　our　proposed　fast　empirical　mode　decomposition　algorithm　for　electrocardiography　denoising　and　hidden　Markov　model-based　algorithm　for　fall　detection.　Eight　kinds　of　services　were　provided　by　the　system,　including　surveillance　of　signs　of　life,　tracking　of　physiological　functions,　monitoring　of　the　activity　field,　anti-lost,　fall　detection,　emergency　call　for　help,　device　wearing　detection,　and　device　low　battery　warning.　The　performance　of　fast　empirical　mode　decomposition　and　hidden　Markov　model　were　evaluated　by　experiment　I　(fast　empirical　mode　decomposition　evaluation)　and　experiment　II　(fall　detection),　respectively.　The　accuracy　and　sensitivity　of　R-peak　detection　using　fast　empirical　mode　decomposition　were　96.46%　and　98.75%,　respectively.　The　accuracy,　sensitivity,　and　specificity　of　fall　detection　using　hidden　Markov　model　were　97.92%,　90.00%,　and　99.50%,　respectively.　The　system　was　evaluated　in　an　elderly　long-term　care　institution　in　Taiwan.　The　results　of　the　satisfaction　survey　showed　that　both　the　caregivers　and　the　elders　are　willing　to　use　the　proposed　intelligent　health　monitoring　system.　The　proposed　system　may　be　used　for　long-term　health　monitoring.