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

arXiv: https://arxiv.org/abs/2507.08268 · HTML: https://mlsanity.com/paper/2507.08268/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 82 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Equinox: neural networks in JAX via callable PyTrees and filtered transformations](https://mlsanity.com/paper/2111.00254/) (2021)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [MoVi: A Large Multipurpose Motion and Video Dataset](https://mlsanity.com/paper/2003.01888/) (2020)
- [Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats](https://mlsanity.com/paper/2212.14474/) (2022)
- [LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion](https://mlsanity.com/paper/2311.02496/) (2023)
- [Reconstructing Humans with a Biomechanically Accurate Skeleton](https://mlsanity.com/paper/2503.21751/) (2025)
- [Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture](https://mlsanity.com/paper/2402.17192/) (2024)
- [BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular Videos](https://mlsanity.com/paper/2501.07800/) (2025)
- [PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research](https://mlsanity.com/paper/2203.08792/) (2022)
- [Markerless Motion Capture and Biomechanical Analysis Pipeline](https://mlsanity.com/paper/2303.10654/) (2023)
- [Transforming Gait: Video-Based Spatiotemporal Gait Analysis](https://mlsanity.com/paper/2203.09371/) (2022)
- [Differentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture](https://mlsanity.com/paper/2411.14992/) (2024)
- [Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics](https://mlsanity.com/paper/2405.17368/) (2024)
- [KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture](https://mlsanity.com/paper/2505.13436/) (2025)
- [Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation](https://mlsanity.com/paper/2403.06164/) (2024)
- [Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture](https://mlsanity.com/paper/2502.06486/) (2025)
- [A Causal Framework for Precision Rehabilitation](https://mlsanity.com/paper/2411.03919/) (2024)

## Built on by (most cited first, 8 of 8)

- [Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Models](https://mlsanity.com/paper/2605.09258/) (2026)
- [HumanScore: Benchmarking Human Motions in Generated Videos](https://mlsanity.com/paper/2604.20157/) (2026)
- [Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace](https://mlsanity.com/paper/2602.13176/) (2026)
- [Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants](https://mlsanity.com/paper/2605.17120/) (2026)
- [BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agents](https://mlsanity.com/paper/2602.06975/) (2026)
- [AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing](https://mlsanity.com/paper/2605.21421/) (2026)
- [Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices](https://mlsanity.com/paper/2607.17639/) (2026)
- [SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation](https://mlsanity.com/paper/2609.08108/) (2026)

## Abstract (arXiv)

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 < 3deg 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/.