Feasibility and Reliability Assessment of Video-Based Motion Analysis and Surface Electromyography in Children with Fragile X during Gait.
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ABSTRACT:
SUBMITTER: Sawacha Z
PROVIDER: S-EPMC8309640 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Personality affects an individual's academic achievements, occupational tendencies, marriage quality and physical health, so more convenient and objective personality assessment methods are needed. Gait is a natural, stable, and easy-to-observe body movement that is closely related to personality. The purpose of this paper is to propose a personality assessment model based on gait video and evaluate the reliability and validity of the multidimensional model. This study recruited 152 participants and used cameras to record their gait videos. Each participant completed a 44-item Big Five Inventory (BFI-44) assessment. We constructed diverse static and dynamic time-frequency features based on gait skeleton coordinates, interframe differences, distances between joints, angles between joints, and wavelet decomposition coefficient arrays. We established multidimensional personality trait assessment models through machine learning algorithms and evaluated the criterion validity, split-half reliability, convergent validity, and discriminant validity of these models. The results showed that the reliability and validity of the Gaussian process regression (GPR) and linear regression (LR) models were best. The mean values of their criterion validity were 0.478 and 0.508, respectively, and the mean values of their split-half reliability were all greater than 0.8. In the formed multitrait-multimethod matrix, these methods also had higher convergent and discriminative validity. The proposed approach shows that gait video can be effectively used to evaluate personality traits, providing a new idea for the formation of convenient and non-invasive personality assessment methods.
| S-EPMC9380895 | biostudies-literature
Project description:INTRODUCTION:Intensive care unit (ICU) patients often develop weakness. Rehabilitation is initiated early to prevent physical deterioration, but knowledge of optimal training schedules is lacking. A reliable method to assess muscle activity during exercise is needed. In this study we explored the feasibility of electrical activity measurement by surface electromyography (sEMG) during bed cycling in ICU patients. METHODS:SEMG was performed in 9 ICU patients and 6 healthy controls. A standardized 1-minute incremental resistance bedside cycle ergometer protocol was used. RESULTS:The median cycle time was 5.3 minutes in patients and 12.0 minutes in controls. The maximum sEMG increased in both groups; the minimal sEMG activity remained the same in patients, whereas an increase in the control group was found. DISCUSSION:sEMG is feasible and can detect muscle activity during bed cycling in ICU patients. It may be a useful monitoring tool. Repeated measurements could possibly provide information on the effects of training. Muscle Nerve 58: 688-693, 2018.
| S-EPMC6282956 | biostudies-literature
Project description:BackgroundQuantitative train-of-four (TOF) monitoring remains essential in optimizing anesthetic outcomes by assessing the depth and recovery from neuromuscular blockade. Despite this, residual neuromuscular blockade, defined as a TOF ratio <0.90, remains a concern in both adult and pediatric patients. Quantitative TOF monitoring has seen limited use in infants and children primarily due to a lack of effective equipment. This study evaluates a new electromyography (EMG)-based TOF monitor in pediatric patients undergoing inpatient surgical procedures including laparoscopic (restricted arm access) surgery.MethodsPediatric patients undergoing inpatient surgery requiring the administration of neuromuscular blocking agents (NMBAs) were enrolled. The EMG electrodes were placed along the ulnar nerve on the volar aspect of the arm to provide neurostimulation. The muscle action potentials from the abductor digiti minimi muscle were recorded. Neuromuscular responses were recorded by the device throughout surgery at 20-s intervals until after tracheal extubation. Data recorded on the monitor's built-in memory card were later retrieved and analyzed.ResultsThe study cohort included 100 pediatric patients (62% male). The average age was 11 years (IQR: 8, 13) and the average weight was 39.6 kg (30, 48.7). Automatic detection of supramaximal stimulus was obtained in 95% of patients. The muscle action potential mean baseline amplitude (in mV) was 7.5 mV (6, 9.2). The baseline TOF ratio was 100% (100, 104). After administration of a neuromuscular blocking agent, monitoring of the TOF ratio was successful in 93% of the patients. After antagonism of neuromuscular blockade, monitoring was possible in 94% of patients when using an upgraded algorithm. The baseline amplitude recovered to 6.5 mV (5, 7.8), and the TOF ratio recovered to a mean of 90.1% (90,97) before tracheal extubation.ConclusionOur results indicate that neuromuscular monitoring can be performed intraoperatively in pediatric patients weighing between 20 and 60 kg using the new commercially available EMG-based monitor. Automatic detection of neuromuscular stimulating parameters (supramaximal current intensity level and baseline amplitude of the muscle action potential) by an adult neuromuscular monitor is feasible in pediatric patients receiving nondepolarizing neuromuscular blocking agents.
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Project description:The primary aim of the study was to assess intraday and interday reliability of surface electromyography (sEMG) reflex activity of the pelvic floor muscles during synchronous whole-body vibration (S-WBV) of two intensities (30Hz/2mm; 40Hz/4mm) using band-stop filter and high-pass filter signal processing. The secondary aim of the study was to assess intraday and interday (test-retest) reliability of sEMG obtained from maximal voluntary contraction (MVC) test. We evaluated the intraday reliability of sEMG recordings obtained during sessions 1 and 2 performed on the same day. The sessions consisting of maximal voluntary pelvic floor muscle contraction and synchronous vibration sets with 1-hour rest in-between sessions 1 and 2 in healthy nulliparous women. The next intraday reliability was evaluated between the results of sessions 3 and 4 performed on the same day but followed at an interval of 4 weeks. to include the entire menstrual cycle. The interday reliability was determined based on the results of sessions 1 and 3 using the intraclass correlation coefficient (ICC 3,3). The intraday ICCs for band-stop filtered mean and median sEMG frequency and mean normalized sEMGRMS amplitude of the 30Hz/2mm (ICC = 0.89-0.99) and 40Hz/4mm vibration (ICC = 0.95-0.99) indicated substantial reproducibility. The intraday reliability of high-pass filter at 100-450Hz for these parameters was also substantial (30Hz/2mm ICC of 0.92 to 0.98; 40Hz/4mm ICC of 0.88 to 0.98). The interday reliability (session 1 vs. session 3) of the mean normalized sEMGRMS amplitude for band-stop filtered means of 40 Hz/4mm and 30Hz/2mm vibration recordings was substantial (ICC = 0.82 and 0.93). However, ICCs of the mean and median frequency were indicative of fair reliability (ICC of 0.43 to 0.59). The interday reliability of mean normalized sEMGRMS amplitude for high-pass filter at 100-450Hz was substantial (30Hz/2mm ICC of 0.90; 40Hz/4mm ICC of 0.73) for the 30Hz/2mm S-WBV and moderate (ICC = 0.73) for the 40/4mm S-WBV. The ICCs for mean and median sEMG frequency ICCs indicated slight to fair reproducibility (ICC of 0.16 to 0.56). The intraday reliability of the strongest MVC contraction and average MVC turned out substantial (ICC = 0.91-0.98). The interday reliability coefficients of the strongest MVC contraction and average MVCs were 0.91 and 0.82, respectively. Concluded, the intraday reliability proved satisfactory for all variables; however, the interday comparison showed sufficient ICC levels only for the mean amplitude. We therefore recommend this parameter should be used when analyzing PFM sEMG recorded during vibration. ICCs of the mean and median frequency for both signal processing methods were indicative of insufficient reliability and did not reach the threshold for usefulness. Our study showed similar reliability of PFM sEMG during S-WBV in case of the two filtering methods used.
| S-EPMC8130969 | biostudies-literature
Project description:The classification of gait phases based on surface electromyography (sEMG) and electroencephalogram (EEG) can be used to the control systems of lower limb exoskeletons for the rehabilitation of patients with lower limb disorders. In this study, the slope sign change (SSC) and mean power frequency (MPF) features of EEG and sEMG were used to recognize the seven gait phases [loading response (LR), mid-stance (MST), terminal stance (TST), pre-swing (PSW), initial swing (ISW), mid-swing (MSW), and terminal swing (TSW)]. Previous researchers have found that the cortex is involved in the regulation of treadmill walking. However, corticomuscular interaction analysis in a high level of gait phase granularity remains lacking in the time-frequency domain, and the feasibility of gait phase recognition based on EEG combined with sEMG is unknown. Therefore, the time-frequency cross mutual information (TFCMI) method was applied to research the theoretical basis of gait control in seven gait phases using beta-band EEG and sEMG data. We firstly found that the feature set comprising SSC of EEG as well as SSC and MPF of sEMG was robust for the recognition of seven gait phases under three different walking speeds. Secondly, the distribution of TFCMI values in eight topographies (eight muscles) was different at PSW and TSW phases. Thirdly, the differences of corticomuscular interaction between LR and MST and between TST and PSW of eight muscles were not significant. These insights enrich previous findings of the authors who have carried out gait phase recognition and provide a theoretical basis for gait recognition based on EEG and sEMG.
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Project description:IntroductionThe Duchenne Video Assessment (DVA) assesses quality of movement as an indication of Duchenne muscular dystrophy (DMD) disease severity. Caregivers video record patients performing home-based movement tasks using a mobile application, and physical therapists (PTs) rate the videos using scorecards with prespecified compensatory movement criteria. Reliability and construct validity of the DVA were tested using video and Pediatric Outcomes Data Collection Instrument (PODCI) data from patients with DMD and healthy controls from a separate study.MethodsFifteen PTs were trained and certified as DVA raters. All raters scored videos of five subjects performing each movement task; nine raters rescored the same videos four weeks later. Three raters scored videos from an average of 25 subjects for each movement task. Aggregate scores were used to test construct validity. An expert DMD clinician assigned each video to a severity group for known-groups analyses. Differences between rater scores across severity groups were tested and correlations between DVA and PODCI scores were calculated.ResultsInter-rater reliability (intraclass correlation coefficient [ICC]) between all 15 raters ranged from 0.70 to 0.97 for all movement tasks. Mean intra-rater reliability ICC for nine raters ranged from 0.82 to 0.98 for all movement tasks. There were statistically significant differences between known severity groups for all movement tasks. The DVA correlated strongly with related PODCI constructs of physical function and weakly with unrelated constructs.DiscussionThe DVA was found to be a reliable and valid tool for measuring quality of movement as an indication of disease severity.
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Project description:Optical motion capturing systems are expensive and require substantial dedicated space to be set up. On the other hand, they provide unsurpassed accuracy and reliability. In many situations however flexibility is required and the motion capturing system can only temporarily be placed. The Microsoft Kinect v2 sensor is comparatively cheap and with respect to gait analysis promising results have been published. We here present a motion capturing system that is easy to set up, flexible with respect to the sensor locations and delivers high accuracy in gait parameters comparable to a gold standard motion capturing system (VICON). Further, we demonstrate that sensor setups which track the person only from one-side are less accurate and should be replaced by two-sided setups. With respect to commonly analyzed gait parameters, especially step width, our system shows higher agreement with the VICON system than previous reports.
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Project description:Measuring muscle fatigue involves assessing various components within the motor system. While subjective and sensor-based measures have been proposed, a comprehensive comparison of these assessment measures is currently lacking. This study aims to bridge this gap by utilizing three commonly used measures: participant self-reported perceived muscle fatigue scores, a sports physiotherapist's manual palpation-based muscle tightness scores, and surface electromyography sensors. Compensatory muscle fatigue occurs when one muscle group becomes fatigued, leading to the involvement and subsequent fatigue of other muscles as they compensate for the workload. The evaluation of compensatory muscle fatigue focuses on nine different upper body muscles selected by the sports physiotherapist. With a cohort of 30 male subjects, this study provides a valuable dataset for researchers and healthcare practitioners in sports science, rehabilitation, and human performance. It enables the exploration and comparison of diverse methods for evaluating different muscles in isometric contraction.
| S-EPMC10869346 | biostudies-literature
Project description:Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment. Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl-Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation. Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r > 0.9, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention (r > 0.9, P < 0.001). Significant correlations (P < 0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r = 0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention. Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02117089.
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2014-06-20 | E-MTAB-1166 | biostudies-arrayexpress