Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit.

Journal: Journal of shoulder and elbow surgery
PMID: 29525490

Abstract

BACKGROUND: Mobile technology offers the prospect of delivering high-value care with increased patient access and reduced costs. Advances in mobile health (mHealth) and telemedicine have been inhibited by the lack of interconnectivity between devices and software and inability to process consumer sensor data. The objective of this study was to preliminarily validate a motion-based machine learning software development kit (SDK) for the shoulder compared with a goniometer for 4 arcs of motion: (1) abduction, (2) forward flexion, (3) internal rotation, and (4) external rotation.

Authors

  • Prem N Ramkumar
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Heather S Haeberle
    Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX.
  • Sergio M Navarro
    Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, Said Business School, University of Oxford, Oxford, United Kingdom.
  • Assem A Sultan
    Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA.
  • Michael A Mont
    Department of Orthopaedic Surgery, Lenox Hill Hospital of Northwell Health, New York, NY.
  • Eric T Ricchetti
    Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA.
  • Mark S Schickendantz
    Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA.
  • Joseph P Iannotti
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.

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