A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation

Abstract

Background Markerless motion capture (MMC) technology has been developed to avoid the need for body marker placement during motion tracking and analysis of human movement. Although researchers have long proposed the use of MMC technology in clinical measurement—identification and measurement of movement kinematics in a clinical population, its actual application is still in its preliminary stages. The benefits of MMC technology are also inconclusive with regard to its use in assessing patients’ conditions. In this review we put a minor focus on the method’s engineering components and sought primarily to determine the current application of MMC as a clinical measurement tool in rehabilitation. Methods A systematic computerized literature search was conducted in PubMed, Medline, CINAHL, CENTRAL, EMBASE, and IEEE. The search keywords used in each database were “Markerless Motion Capture OR Motion Capture OR Motion Capture Technology OR Markerless Motion Capture Technology OR Computer Vision OR Video-based OR Pose Estimation AND Assessment OR Clinical Assessment OR Clinical Measurement OR Assess.” Only peer-reviewed articles that applied MMC technology for clinical measurement were included. The last search took place on March 6, 2023. Details regarding the application of MMC technology for different types of patients and body parts, as well as the assessment results, were summarized. Results A total of 65 studies were included. The MMC systems used for measurement were most frequently used to identify symptoms or to detect differences in movement patterns between disease populations and their healthy counterparts. Patients with Parkinson’s disease (PD) who demonstrated obvious and well-defined physical signs were the largest patient group to which MMC assessment had been applied. Microsoft Kinect was the most frequently used MMC system, although there was a recent trend of motion analysis using video captured with a smartphone camera. Conclusions This review explored the current uses of MMC technology for clinical measurement. MMC technology has the potential to be used as an assessment tool as well as to assist in the detection and identification of symptoms, which might further contribute to the use of an artificial intelligence method for early screening for diseases. Further studies are warranted to develop and integrate MMC system in a platform that can be user-friendly and accurately analyzed by clinicians to extend the use of MMC technology in the disease populations.

Using a Markerless Motion Capture System to Identify Preinjury Differences in Functional Assessments

“…41 However, these are currently not routinely used in sports medicine applications but have been utilized in sports performance-based studies and rehabilitation applications. [42][43][44][45] Comprehensive understanding of the preinjury or presurgical functional performance and movements are critical to monitor a safe return to sport following injury or return to work following surgery. Documenting these parameters with the use of a safe, efficient, and high-throughput markerless motion capture system to record and characterize these parameters and measurements may allow for the recognition of correctable deficits (i.e., ligament dominance) to address deficiencies to prevent injury, as well as guide recovery and safe return to work, sport, and recreation.…”

Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes

“…Human pose estimation refers to a deep learning computer vision process that captures a set of coordinates for key joints from an image that can describe a person’s pose, resulting in a skeleton-like structure when graphically formatted. [ 35 ] An exemplary image is presented in the first step of Figure 2 . Studies have commonly performed this stage with existing marker-less open-source 2D- or 3D-human pose estimation libraries, such as OpenPose and MediaPipe, that are frequently utilised in the medical context.…”

AI in Rehabilitation Medicine: Opportunities and Challenges

“…Pose estimation is becoming more commonplace in applications like gait analysis, since it reduces dependency on costly optoelectronic motion capture equipment. Video-based gait metrics have been computed in healthy populations [54], people with Parkinson's disease [55] and stroke [56], as well as for general functional assessment [57].…”