In　recent　years,　particular　attention　has　been　devoted　to　continuous　electrocardiogram　(ECG)　waveform　monitoring　due　to　its　numerous　applications.　However,　existing　solutions　require　users　to　be　confined　to　particular　locations,　rely　on　dedicated　and　expensive　hardware,　or　require　active　user　participation.　The　constrained　recording　conditions　prevent　them　from　being　deployed　in　many　practical　application　scenarios.　In　view　of　this,　we　present　VibCardiogram,　a　continuous　and　reliable　design　for　estimating　ECG　waveform　shape　via　ubiquitous　wrist-worn　wearables;　it　renders　a　personal　ECG　waveform　shape　estimating　system　with　prolonged　recording　time　accessible　to　a　larger　sector　of　the　population.　Instead　of　adding　auxiliary　sensors　to　wearables,　VibCardiogram　leverages　the　widely　integrated　motion　sensor　to　characterize　cardiac　activities,　and　interpret　them　to　generate　an　alternative　signal　that　has　the　same　waveform　shape　as　the　ECG　signal.　Specifically,　VibCardiogram　extracts　the　cardiogenic　body　vibrations　from　noisy　sensor　data.　As　the　waveform　variability　and　inconstant　period　hinder　the　segmentation　of　cardiac　cycles,　VibCardiogram　extracts　features　and　realizes　accurate　segmentation　via　machine　learning.　To　parse　cardiac　activities　from　vibration　signals,　we　build　a　deep-learning　pipeline　associating　the　encoder-decoder　framework　and　Generative　Adversarial　Networks.　With　dedicated　construction　and　training,　it　can　estimate　the　ECG　waveform　accurately.　Experiments　with　20　participants　show　that　VibCardiogram　can　achieve　an　average　estimation　error　of　5.989%　for　waveform　amplitude　estimation,　which　is　within　the　10%　margins　regulated　by　the　American　National　Standards　Institute.　Moreover,　the　promising　results　further　confirm　that　VibCardiogram　effectively　extracts　Heart　Rate　Variability　features　and　supports　downstream　applications.　©　2022　ACM.