Validation of video-based event detection against laboratory-standard biomechanical measures: a synchronized multimodal study

Introduction Biomechanical event detection is fundamental to human movement analysis in clinical, rehabilitation, and sports settings, yet conventional laboratory systems remain costly and inaccessible in many contexts. This study validated a markerless, video-primary event detection system against laboratory-reference biomechanical measurements using synchronized multimodal data. Methods Eight healthy adults performed standardized upper-limb dumbbell lifting tasks in a controlled laboratory while video, Vicon motion-capture, and surface electromyography (EMG) data were recorded (76 valid trials; 304 event labels). A hybrid pipeline combining threshold-based peak detection with Long Short-Term Memory (LSTM) networks identified four events: action start, peak of rising, end of holding, and start of returning. Kinematic features were derived from video; two EMG envelope channels were additionally used in the LSTM refinement stage. Streams were synchronized using hardware triggers (Vicon and EMG), software-started video recording, and cross-correlation of a shared kinematic signal prior to independent event detection in each system. Video-based event timestamps were compared with Vicon reference timestamps without further reference-informed adjustment, using intraclass correlation coefficients (ICC), Bland–Altman analysis, mean absolute error (MAE), root mean square error (RMSE), and classification metrics. Results The video-based system showed excellent agreement with the Vicon reference (ICC: 0.90–0.94), low temporal error (MAE: 38–51 ms; RMSE: 52–70 ms), and minimal systematic bias; errors corresponded to approximately 1.1–1.5 frames at the 30 fps acquisition rate. Classification performance was strong across all events (F1 score: 93.4%; sensitivity: 93.4%; specificity: 97.8%). EMG activation peaks correlated with detected events (r = 0.86–0.91); because EMG envelopes also served as LSTM inputs, this is an internal consistency check rather than independent physiological validation. Discussion These findings indicate that a hybrid video-primary approach can achieve accuracy comparable to laboratory-reference measurements for upper-limb analysis under controlled conditions, offering a scalable complement to traditional motion-capture systems. An ablation of the EMG input channels to establish video-only performance, and validation in larger, more diverse populations and real-world settings, are required.