arXiv subjects

Brenton Pennicooke

Publications and source records attributed to Brenton Pennicooke.

2 recordsLinked to original sources

arXiv2025

Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone

Movement directly reflects neurological and musculoskeletal health, yet objective biomechanical assessment is rarely available in routine care. We introduce Portable Biomechanics Laboratory (PBL), a secure platform for fitting biomechanical models to video collected with a handheld, moving, smartphone. We validate this approach on over 15 hours of data synchronized to ground truth motion capture, finding mean joint-angle errors < 3$°$ and pelvis-translation errors of a few centimeters across patients with neurological-injury, lower-limb prosthesis users, pediatric in-patients, and controls. In > 5 hours of prospective deployments to neurosurgery and sports-medicine clinics, PBL was easy to setup, yielded highly reliable gait metrics (ICC > 0.9), and detected clinically relevant differences. For cervical-myelopathy patients, its measurement of gait quality correlated with modified Japanese Orthopedic Association (mJOA) scores and were responsive to clinical intervention. Handheld smartphone video can therefore deliver accurate, scalable, and low-burden biomechanical measurement, enabling greatly increased monitoring of movement impairments. We release the first clinically-validated method for measuring whole-body kinematics from handheld smartphone video at https://IntelligentSensingAndRehabilitation.github.io/MonocularBiomechanics/.

cs.CV↗

arXiv2019

More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions

Inverse probability of treatment weighting (IPTW), which has been used to estimate sample average treatment effects (SATE) using observational data, tenuously relies on the positivity assumption and the correct specification of the treatment assignment model, both of which are problematic assumptions in many observational studies. Various methods have been proposed to overcome these challenges, including truncation, covariate-balancing propensity scores, and stable balancing weights. Motivated by an observational study in spine surgery, in which positivity is violated and the true treatment assignment model is unknown, we present the use of optimal balancing by Kernel Optimal Matching (KOM) to estimate SATE. By uniformly controlling the conditional mean squared error of a weighted estimator over a class of models, KOM simultaneously mitigates issues of possible misspecification of the treatment assignment model and is able to handle practical violations of the positivity assumption, as shown in our simulation study. Using data from a clinical registry, we apply KOM to compare two spine surgical interventions and demonstrate how the result matches the conclusions of clinical trials that IPTW estimates spuriously refute.

stat.ME↗