Methods of measurement
The CV-aided system approach (Good Boost CV system, Good Boost Wellness, UK, 2021) in this study involved a modified version of OpenPose, a computer vision algorithm trained to detect key landmarks on the human body within camera images. For a given frame of image/video data, OpenPose returns predicted x,y coordinates for each body part and each human detected in the image. X,y coordinates were used to compute metrics such as joint angles and distances (in pixels) between two body parts for the index of movements. To translate distance values into real-world distances, at the start of each movement, the participant or investigator held up a calibration checkerboard parallel to the camera and at the same distance at which the movement was performed; Python’s OpenCV package was used to automatically detect the corners of the checkerboard to scale all distance values from pixels to centimetres. The videos taken in the movement laboratory were captured by a Logitech C920 pro HD webcam (©2021 Logitech, UK) with 1080p resolution and 30 frames per second sampling rate. The videos taken in the home setting were captured by the participant’s smartphone camera, tablet camera or webcam. Spinal curvature was measured in the laboratory only using a portable surface topography method employing the Microsoft Kinect sensor V2 (Microsoft Corporation, Seattle, Washington, U.S.A) and using an established method to measure thoracic kyphosis. The reference tests were a series of standard clinical assessments measured by an experienced physiotherapist who was blinded to the remote technology systems analyses and results.