Irina Djuraskovic

Seth Donahue, Irina Djuraskovic, Kunal Shah et al.

Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerless motion capture (MMMC) require that, in addition to being accurate, these systems produce reliable confidence intervals to indicate how accurate they are for any individual. Building on our prior work utilizing variational inference to estimate joint angle posterior distributions, this study evaluates the calibration and reliability of a probabilistic MMMC method. We analyzed data from 68 participants across two institutions, validating the model against an instrumented walkway and standard marker-based motion capture. We measured the calibration of the confidence intervals using the Expected Calibration Error (ECE). The model demonstrated reliable calibration, yielding ECE values generally < 0.1 for both step and stride length and bias-corrected gait kinematics. We observed a median step and stride length error of ~16 mm and ~12 mm respectively, with median bias-corrected kinematic errors ranging from 1.5 to 3.8 degrees across lower extremity joints. Consistent with the calibrated ECE, the magnitude of the model's predicted uncertainty correlated strongly with observed error measures. These findings indicate that, as designed, the probabilistic model reconstruction quantifies epistemic uncertainty, allowing it to identify unreliable outputs without the need for concurrent ground-truth instrumentation.

J. D. Peiffer, Kunal Shah, Irina Djuraskovic et al.

The way a person moves is a direct reflection of their neurological and musculoskeletal health, yet it remains one of the most underutilized vital signs in clinical practice. Although clinicians visually observe movement impairments, they lack accessible and validated methods to objectively measure movement in routine care. This gap prevents wider use of biomechanical measurements in practice, which could enable more sensitive outcome measures or earlier identification of impairment. We present our Portable Biomechanics Laboratory (PBL), which includes a secure, cloud-enabled smartphone app for data collection and a novel algorithm for fitting biomechanical models to this data. We extensively validated PBL's biomechanical measures using a large, clinically representative dataset. Next, we tested the usability and utility of our system in neurosurgery and sports medicine clinics. We found joint angle errors within 3 degrees across participants with neurological injury, lower-limb prosthesis users, pediatric inpatients, and controls. In addition to being easy to use, gait metrics computed from the PBL showed high reliability and were sensitive to clinical differences. For example, in individuals undergoing decompression surgery for cervical myelopathy, the mJOA score is a common patient-reported outcome measure; we found that PBL gait metrics correlated with mJOA scores and demonstrated greater responsiveness to surgical intervention than the patient-reported outcomes. These findings support the use of handheld smartphone video as a scalable, low-burden tool for capturing clinically meaningful biomechanical data, offering a promising path toward accessible monitoring of mobility impairments. We release the first clinically validated method for measuring whole-body kinematics from handheld smartphone video at https://intelligentsensingandrehabilitation.github.io/MonocularBiomechanics/ .