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AimsTo 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.DesignThis 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.ResultsOverall, 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.ConclusionThere is no conclusive evidence that the STM system offers any benefits in terms of plaque control over conventional TBI in this study.

Ergonomic risk assessment is crucial for preventing work-related musculoskeletal disorders (WMSDs), which often arise from repetitive tasks, prolonged sitting, and load handling, leading to absenteeism and increased healthcare costs. Biomechanical risk assessment, such as RULA/REBA, is increasingly being enhanced by camera-based motion capture systems, either marker-based (MBSs) or markerless systems (MCBSs). This systematic review compared MBSs and MCBSs regarding accuracy, validity, and reliability for industrial ergonomic risk analysis. A comprehensive search of PubMed, WoS, ScienceDirect, IEEE Xplore, and PEDro (31 May 2025) identified 898 records; after screening with PICO-based eligibility criteria, 20 quantitative studies were included. Methodological quality was assessed with the COSMIN Risk of Bias tool, synthesized using PRISMA 2020, and graded with EBRO criteria. MBSs showed the highest precision (0.5–1.5 mm error) and reliability (ICC > 0.90) but were limited by cost and laboratory constraints. MCBSs demonstrated moderate-to-high accuracy (5–20 mm error; mean joint-angle error: 2.31° ± 4.00°) and good reliability (ICC > 0.80), with greater practicality in field settings. Several studies reported strong validity for RULA/REBA prediction (accuracy up to 89%, κ = 0.71). In conclusion, MCBSs provide a feasible, scalable alternative to traditional ergonomic assessment, combining reliability with usability and supporting integration into occupational risk prevention.

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. Graphical Abstract

Abstract 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.

Purpose To evaluate the dosimetric impact on spatially fractionated radiation therapy (SFRT) plan quality due to intrafraction patient motion via multi‐field MLC‐based method for treating large and bulky (≥8 cm) unresectable tumors. Methods For large tumors, a cone beam CT‐guided 3D conformal MLC‐based SFRT method was utilized with 15 Gy prescription. An MLC GTV‐fitting algorithm provided 1 cm diameter apertures with a 2 cm center‐to‐center distance at the isocenter. This generated a highly heterogeneous sieve‐like dose distribution within an hour, enabling same‐day SFRT treatment. Fifteen previously treated SFRT patients were analyzed (5 head & neck [H&N], 5 chest and lungs, and 5 abdominal and pelvis masses). For each plan, intrafraction motion errors were simulated by incrementally shifting original isocenters of each field in different x‐, y‐, and z‐directions from 1 to 5 mm. The dosimetric metrics analyzed were: peak‐to‐valley‐dose‐ratio (PVDR), percentage of GTV receiving 7.5 Gy, GTV mean dose, and maximum dose to organs‐at‐risk (OARs). Results For ±1, ±2, ±3, ±4, and ±5 mm isocenter shifts: PVDR dropped by 3.9%, 3.8%, 4.0%, 4.1%, and 5.5% on average respectively. The GTV(V7.5) remained within 0.2%, and the GTV mean dose remained within 3.3% on average, compared to the original plans. The average PVDR drop for 5 mm shifts was 4.2% for H&N cases, 10% for chest and lung, and 2.2% for abdominal and pelvis cases. OAR doses also increased. The maximum dose to the spinal cord increased by up to 17 cGy in H&N plans, mean lung dose (MLD) changed was small for chest/lung, but the bowel dose varied up to 100 cGy for abdominal and pelvis cases. Conclusion Due to tumor size, location, and characteristics of MLC‐based SFRT, isocenter shifts of up to ±5 mm in different directions had moderate effects on PVDR for H&N and pelvic tumors and a larger effect on chest tumors. The dosimetric impact on OAR doses depended on the treatment site. Site‐specific patient masks, Vac‐Lok bags, and proper immobilization devices similar to SBRT/SRT setups should be used to minimize these effects.

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. 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. 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. . 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. 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.

An accurate and effective neurological evaluation is indispensable in the treatment and rehabilitation of traumatic brain injury. However, most of the existing evaluation methods in basic research and clinical practice are not objective or intuitive for assessing the neurological function of big animals, and are also difficult to use to qualify the extent of damage and recovery. In the present study, we established a big animal model of traumatic brain injury by impacting the cortical motor region of beagles. At 2 weeks after successful modeling, we detected neurological deficiencies in the animal model using a series of techniques, including three-dimensional motion capture, electromyogram and ground reaction force. These novel technologies may play an increasingly important role in the field of traumatic brain injury diagnosis and rehabilitation in the future. The experimental protocol was approved by the Animal Care and Use Committee of Logistics University of People's Armed Police Force (approval No. 2017-0006.2).

(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.

While markerless motion capture system has recently gained interest, little is known on the accuracy of Theia 3D for measuring shoulder kinematics. This study aimed to compare shoulder kinematics across the full range of arm motion between Theia 3D and a gold standard marker-based motion capture system. Shoulder kinematics of 20 healthy subjects were measured during three repetitions of bilateral full arm scaption, abduction, flexion, internal rotation, external rotation, and internal and external rotation at 90° abduction. Data were simultaneously collected with Theia 3D (v2024.1.24) markerless and marker-based motion capture. Markerless and marker-based angular trajectories had similar patterns, with larger differences towards the inflection point of the movements and with the markerless trajectories having mostly greater values than marker-based trajectories. Root mean square differences were smallest for abduction angles (<6°) and largest for the external rotation angles (15.3°–22.3°). Coefficients of multiple correlations (CMCs) were mostly good to excellent (>0.75), but CMC was moderate (0.65–0.75) for flexion angles during rotation at 90° and weak (<0.65) for flexion angles during external rotation. For almost all tasks, the ranges of motion differed significantly between the two measurement systems in all three planes. However, mean differences in the coronal plane and in the sagittal plane were within the minimal clinically important differences. We found moderate to very strong correlations between the range of motion parameters of the two measurement methods. The assessment of shoulder kinematics with Theia 3D is promising but further improvements are needed for clinical routine application.

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s −1 . Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.