J.D. Peiffer

Background 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. Methods In this work, 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 and heterogeneous dataset with synchronous ground truth. Next, we tested the usability and utility of our system in both a neurosurgery and sports medicine clinic. Results We found joint angle errors within 3 degrees and pelvis translation errors within several centimeters across participants with neurological injury, lower-limb prosthesis users, pediatric inpatients, and controls. In addition to being easy and quick to use, gait metrics computed from the PBL showed high reliability (ICCs \textgreater 0.9) and were sensitive to clinical differences. For example, in individuals undergoing decompression surgery for cervical myelopathy, the modified Japanese Orthopedic Association (mJOA) score is a common patient-reported outcome measure; we found that PBL gait metrics not only correlated with mJOA scores but also demonstrated greater responsiveness to surgical intervention than the patient-reported outcomes. Conclusions 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 remote, accessible monitoring of mobility impairments in clinical populations. To promote further research and clinical translation, we release the first method for measuring whole-body kinematics from handheld smartphone video validated in clinical populations: https://IntelligentSensingAndRehabilitation.

Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors, namely inertial measurement units (IMUs), provide high-resolution measurements of specific body segments. A robust method to fuse these modalities and obtain biomechanically accurate kinematics would have substantial utility for clinical assessment and monitoring. Although multiple video-sensor fusion methods exist, most assume that a time-intensive and often brittle sensor-body calibration process has already been completed. In this work, we employ an implicit function to combine handheld smartphone video and uncalibrated IMU data at their full temporal resolution. Our monocular, video-only, biomechanical reconstruction already performs well, with only 3.91 (1.55) degrees of mean adjusted angular error in knee kinematics across 60 recordings. Re-constructing from a fusion of video and IMU data reduces this error to 2.9 (1.27) degrees. We validate this method in a diverse group including individuals with no gait impairments, lower limb prosthesis users, and those with a history of stroke. We also show that IMU data allows accurate tracking through periods of visual occlusion, equivalent to video-only.