Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review.

BACKGROUND: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia. OBJECTIVE: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease. METHODS: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. . RESULTS: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models. CONCLUSIONS: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

Optical Marker-Based Motion Capture of the Human Spine: A Scoping Review of Study Design and Outcomes.

Biomechanical analysis of the human spine is crucial to understanding injury patterns. Motion capture technology has gained attention due to its non-invasive nature. Nevertheless, traditional motion capture studies consider the spine a single rigid segment, although its alignment changes during movement. Moreover, guidelines that indicate where markers should be placed for a specific exercise do not exist. This study aims to review the methods used to assess spine biomechanics using motion capture systems to determine the marker sets used, the protocols used, the resulting parameters, the analysed activities, and the characteristics of the studied populations. PRISMA guidelines were used to perform a Scoping Review using SCOPUS and Web of Science databases. Fifty-six journal and conference articles from 1997 to 2023 were considered for the analysis. This review showed that Plug-in-Gait is the most used marker set. The lumbar spine is the segment that generates the most interest because of its high mobility and function as a weight supporter. Furthermore, angular position and velocity are the most common outcomes when studying the spine. Walking, standing, and range of movement were the most studied activities compared to sports and work-related activities. Male and female participants were recruited similarly across all included articles. This review presents the motion capture techniques and measurement outcomes of biomechanical studies of the human spine, to help standardize the field. This work also discusses trends in marker sets, study outcomes, studied segments and segmentation approaches.

Mirror, mirror on the wall-is toothbrushing instruction via a three-dimensional motion-tracked system the fairest of them all?

AIMS: To assess the efficacy of a smart toothbrush and mirror (STM) system which provided computer-assisted toothbrushing instruction versus conventional verbal toothbrushing instruction (TBI) amongst a group of 6-12-year-old children. DESIGN: This randomised controlled trial comprised South Korean school children who were randomly allocated to one of two arms - the STM (n = 21) or the conventional TBI arm (n = 21). The STM system used identical brushes as the TBI group, but with the brush employing three-dimensional motion tracking systems alongside a mirror with an inbuilt computer to guide the user. Modified Quigley-Hein plaque indexes were taken at baseline, immediately after STM/TBI, and then at 1 week and 1 month. RESULTS: Overall, there was a statistically significant reduction in the average whole-mouth plaque scores for both groups, 40.50% and 40.57% for the STM group and TBI group respectively. There was no statistical significance that either arm was more effective in reducing plaque scores than the other. Both groups demonstrated time to be a statistically significant factor in reducing plaque indices. CONCLUSION: There is no conclusive evidence that the STM system offers any benefits in terms of plaque control over conventional TBI in this study.

Wearable Nanocomposite Sensor System for Motion Phenotyping Chronic Low Back Pain: A BACPAC Technology Research Site.

Chronic low back pain (cLBP) is a prevalent and multifactorial ailment. No single treatment has been shown to dramatically improve outcomes for all cLBP patients, and current techniques of linking a patient with their most effective treatment lack validation. It has long been recognized that spinal pathology alters motion. Therefore, one potential method to identify optimal treatments is to evaluate patient movement patterns (ie, motion-based phenotypes). Biomechanists, physical therapists, and surgeons each utilize a variety of tools and techniques to qualitatively assess movement as a critical element in their treatment paradigms. However, objectively characterizing and communicating this information is challenging due to the lack of economical, objective, and accurate clinical tools. In response to that need, we have developed a wearable array of nanocomposite stretch sensors that accurately capture the lumbar spinal kinematics, the SPINE Sense System. Data collected from this device are used to identify movement-based phenotypes and analyze correlations between spinal kinematics and patient-reported outcomes. The purpose of this paper is twofold: first, to describe the design and validity of the SPINE Sense System; and second, to describe the protocol and data analysis toward the application of this equipment to enhance understanding of the relationship between spinal movement patterns and patient metrics, which will facilitate the identification of optimal treatment paradigms for cLBP.