Coach-BP: Computer Vision-Based Assessment and Correction of Blood Pressure Measurement Technique

Background: Accurate blood pressure (BP) measurement, essential for hypertension care, requires proper technique. Posture errors can raise systolic BP by more than 20 mm Hg, yet technique is rarely verified objectively. We aimed to develop and evaluate Coach-BP, a computer vision system that detects technique errors from video to support real-time coaching. Methods: We enrolled adults attending an academic cardiovascular prevention center between February and April 2026 and filmed BP measurements with front and side cameras. We analyzed 408 encounters from 225 participants. Coach-BP uses machine learning models trained on clinician labels to assess five American Heart Association criteria using pose, visual context, and audio-visual features. We evaluated its accuracy against expert clinician ratings by leave-one-participant-out cross-validation. We then examined robustness to simulated camera artifacts and interpreted the features important for each criterion. Findings: At baseline, 138 (62%) of 223 participants had at least one technique error and 54 (24%) two or more. Coach-BP achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.93 (95% CI 0.90-0.94), with 0.97 (0.94-0.99) for legs uncrossed, 0.97 (0.93-1.00) for no talking, 0.92 (0.85-0.98) for back supported, 0.91 (0.86-0.95) for feet flat, and 0.86 (0.81-0.91) for arm supported at heart level. Mean balanced accuracy was 0.88 (0.85-0.90). Performance was robust to simulated camera shift, zoom, and rotation. Its models relied on features clinicians would assess, such as cuffed-hand height and foot position. One model classified back support similarly from either camera. Interpretation: Coach-BP accurately detected errors in the five guideline-recommended criteria of BP measurement technique from clinic video, enabling objective assessment of each criterion.