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

2025-07-10

AI Summary

Clinical practice lacks accessible, objective tools for motor function assessment, hindering the adoption of biomechanical metrics in rehabilitation and neurology. To address this, we propose a portable, smartphone-based monocular video analysis framework: secure video acquisition via mobile devices is coupled with cloud-based computation, high-precision monocular pose estimation, subject-specific biomechanical modeling, and optimization algorithms—enabling clinical-grade full-body kinematic measurement (joint angle error <3°). Our method significantly outperforms patient-reported outcomes (e.g., mJOA), demonstrating high sensitivity and responsiveness to surgical intervention. It reliably quantifies gait parameters and sensitively detects pre- to postoperative functional changes in cervical spondylotic myelopathy. This work represents the first smartphone-video-driven, clinically validated objective assessment of motor function, establishing a standardized pathway for integrating mobile health technologies into evidence-based rehabilitation medicine.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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/ .

Problem

• Lack of accessible methods to objectively measure movement in clinical care
• Underutilization of biomechanical measurements for early impairment identification
• Need for scalable tools to capture clinically meaningful biomechanical data

Innovation

• Smartphone app for clinical movement data collection
• Novel algorithm for biomechanical model fitting
• Validated whole-body kinematics from smartphone video