In　this　paper,　we　propose　a　compact,　wearable　biosensor　for　the　noninvasive　measurement　of　human　radial　artery　pulse　waveform　curve　(PWC)　and　blood　pressure　(BP).　In　this　system,　self-mixing　interferometry　(SMI)　technology　is　employed　to　measure　the　weak　arterial　vascular　deformation,　enabling　accurate　PWC　retrieval.　Based　on　the　reconstructed　PWC　features,　BP　values　are　precisely　estimated　by　means　of　deep　learning　method.　Here　continuous　wavelet　transform　(CWT),　enabling　visualization　of　the　relationship　between　the　SMI　signal　temporal　frequency　components　and　the　PWC　characteristics,　is　highlighted　for　PWC　flipping　points　seeking　and　convolutional　neural　network　(CNN)　input　parameter　acquisition.　For　the　first　time,　a　novel　deep　learning　network　preprocessing　method　is　proposed　that　allows　direct　feature　extraction　from　the　CWT　scalogram　of　SMI　signal　without　the　complicated　PWC　reconstruction　algorithm.　The　robustness　and　accuracy　of　our　device　are　validated　by　a　series　of　clinical　measurements,　mean　absolute　error　(MAE)　and　standard　deviation　(STD)　values　are　calculated　and　compared　with　the　existing　models.　We　approach　minimal　BP　estimation　results　(MAE　+/-　STD)　of　1.41　+/-　1.89　mmHg　for　systolic　blood　pressure　(SBP)　and　1.78　+/-　2.01　mmHg　for　diastolic　blood　pressure　(DBP),　respectively.　The　luxuriant　novelties　and　remarkable　performance　clearly　demonstrate　our　wearable　sensor＇s　great　potential　in　BP　monitoring,　and　other　clinical　applications.　(c)　2024　Optica　Publishing　Group　under　the　terms　of　the　Optica　Open　Access　Publishing　Agreement