A webcam-based machine learning approach for three-dimensional range of
  motion evaluation
Xiaoye Michael Wang Derek T. Smith Qin Zhu
Background. Joint range of motion (ROM) is an important quantitative measure
for physical therapy. Commonly relying on a goniometer, accurate and reliable
ROM measurement requires extensive training and practice. This, in turn,
imposes a significant barrier for those who have limited in-person access to
healthcare.
  Objective. The current study presents and evaluates an alternative machine
learning-based ROM evaluation method that could be remotely accessed via a
webcam.
  Methods. To evaluate its reliability, the ROM measurements for a diverse set
of joints (neck, spine, and upper and lower extremities) derived using this
method were compared to those obtained from a marker-based optical motion
capture system.
  Results. Data collected from 25 healthy adults demonstrated that the webcam
solution exhibited high test-retest reliability, with substantial to almost
perfect intraclass correlation coefficients for most joints. Compared with the
marker-based system, the webcam-based system demonstrated substantial to almost
perfect inter-rater reliability for some joints, and lower inter-rater
reliability for other joints (e.g., shoulder flexion and elbow flexion), which
could be attributed to the reduced sensitivity to joint locations at the apex
of the movement.
  Conclusions. The proposed webcam-based method exhibited high test-retest and
inter-rater reliability, making it a versatile alternative for existing ROM
evaluation methods in clinical practice and the tele-implementation of physical
therapy and rehabilitation.