14.3.2 AI versus MOCAP: scope, limitations, and user validation

Motion-capture (MOCAP) systems, including marker-based systems and certain laboratory markerless systems, are a reference when highly accurate and repeatable kinematic measurement is required. These systems operate with calibrated cameras and controlled geometric models of the environment, enabling posture reconstruction with very low uncertainty and explicit control of scale, relative sensor positions, and temporal synchronization. In contrast, artificial-intelligence posture detection from general-purpose images or video relies on algorithms that must “infer” body structure from uncalibrated 2D signals, often under suboptimal lighting, occlusion, and perspective conditions. For this reason, although AI provides a fast, accessible, and potentially useful solution for numerous ergonomic analyses when the result is reviewed, its accuracy does not reach that of a properly configured MOCAP system.

The main sources of discrepancy between the two approaches arise from the nature of the problem that AI must solve. First, there is inherent 2D-to-3D ambiguity: different three-dimensional body configurations can project very similar images when the camera is not calibrated or the scene contains no metric references. Second, scale and viewpoint introduce systematic biases if the subject is not appropriately framed or the camera is sharply tilted; AI compensates using anthropometric assumptions and statistical priors that do not always match individual morphology. Third, partial occlusions caused by clothing, tools, furniture, or overlapping segments reduce the visibility of certain joints and require the model to estimate unseen positions. This can result in displaced joints, implausible extreme angles, or local instability. Finally, in video sequences captured in real-world environments, the effective frame rate, motion blur, and variations in lighting increase temporal noise and may require subsequent smoothing, which can attenuate very brief peaks.

With these considerations in mind, Ergoniza Intelligent incorporates automatic posture detection as a rapid capture and preliminary-analysis mechanism fully integrated with the software's assessment methods. However, it follows a clear operating principle: when a posture originates from AI, the user must review and validate the result before incorporating it into the analysis. The interface facilitates this verification by overlaying the estimate on the image or video and displaying the resulting joint angles. If an inconsistency is identified because of occlusion, poor interpretation of a segment, or inappropriate framing, the evaluator can correct the required elements and confirm the result again. This validation is not a bureaucratic formality; it verifies whether the data used in the assessment adequately represent the observed posture and whether capture limitations have been taken into account.

In terms of recommended use, posture AI may be suitable for assessing common static or dynamic tasks, conducting rapid screenings, comparing scenarios before and after an intervention, and documenting representative situations without instrumented testing. Conversely, when the study requires reference-level accuracy—for example, biomechanical research validation, detailed monitoring of micromovements, synchronization with force or EMG data, or certification where the allowable error is very low—the appropriate resource is a MOCAP system with a corresponding calibration and quality-control protocol.

Good practice in Ergoniza Intelligent therefore consists of capturing with careful attention to framing, lighting, and viewpoint; reviewing the overlay of anatomical landmarks and segments; confirming that key angles are plausible for the task being analyzed; and making the necessary adjustments whenever there is any indication of occlusion or questionable interpretation.