Wearable devices enable continuous monitoring of physiological signals in realâworld settings, yet a standardized approach for synchronizing signals across devices remains lacking. We present a generalizable, userâfriendly pipeline that enables synchronization of any two devices capturing the same physiological signals, without requiring coding expertise. The pipeline performs resampling, dynamic time warping alignment, amplitude correction via wavelet transforms, and signal standardization, followed by agreement analyses at both waveform and feature levels. To demonstrate its validity, we applied the pipeline to a case study comparing two researchâgrade devices, the Empatica E4 and the EmbracePlus, using up to 48 h of concurrent recordings from 31 participants. We compared signalâlevel agreement across waveform similarity, amplitude distribution, spectral content, and extracted features between the two researchâgrade devices to determine their interchangeability for longitudinal and multiâsite studies. Specifically, we aimed to determine how well these devices agree at the signal level and to identify which physiological signals are most robust to deviceâspecific variability. Four signals were examined (blood volume pulse, electrodermal activity, accelerometry, and temperature) using NeuroKit2 and FLIRT. We computed Pearson and concordance correlation coefficients, BlandâAltman bias and limits of agreement, root mean squared error (RMSE), KL divergence, spectral coherence, mutual information, and featureâlevel correlations using NeuroKit2 and FLIRT. Results showed nearâperfect agreement for BVP (concordance correlation coefficient (CCC) â 1.0; coherence †0.98) and phasic EDA features (CCC 0.85â0.99), whereas tonic EDA, temperature, and accelerometry exhibited systematic amplitude biases (EmbracePlus lower) and axisâdependent variability ( Z âaxis CCC = 0.85; Y âaxis = 0.19). Relative signal dynamics were preserved across devices despite differences in absolute levels. These findings support integration of BVP, EDA, and TEMP data across E4 and EmbracePlus with proper preprocessing, while highlighting calibration needs for movement signals.