The Component Timed-Up-and-Go is a reliable and valid clinical tool for detailed assessment of prosthetic mobility in people with non-vascular lower limb amputation. The iPad application provided a means to easily record data, contributing to clinical utility.
The use of inertial measurement units (IMUs) for gait analysis has emerged as a tool for clinical applications. Shank gyroscope signals have been utilized to identify heel-strike and toe-off, which serve as the foundation for calculating temporal parameters of gait such as single and double limb support time. Recent publications have shown that toe-off occurs later than predicted by the dual minima method (DMM), which has been adopted as an IMU-based gait event detection algorithm.In this study, a real-time algorithm, Noise-Zero Crossing (NZC), was developed to accurately compute temporal gait parameters. Our objective was to determine the concurrent validity of temporal gait parameters derived from the NZC algorithm against parameters measured by an instrumented walkway. The accuracy and precision of temporal gait parameters derived using NZC were compared to those derived using the DMM. The results from Bland-Altman Analysis showed that the NZC algorithm had excellent agreement with the instrumented walkway for identifying the temporal gait parameters of Gait Cycle Time (GCT), Single Limb Support (SLS) time, and Double Limb Support (DLS) time. By utilizing the moment of zero shank angular velocity to identify toe-off, the NZC algorithm performed better than the DMM algorithm in measuring SLS and DLS times. Utilizing the NZC algorithm's gait event detection preserves DLS time, which has significant clinical implications for pathologic gait assessment.
This study provides evidence that noise during surgery can increase feelings of stress, as measured by perceived task load and fatigue levels, in anesthesiologists and adds to the growing literature pointing to an overall adverse impact of clinical noise on caregivers and patient safety. The psychometric model proposed in this study for assessing perceived stress is plausible based on factor analysis and will be useful for characterizing the impact of the clinical environment on subject stress levels in future investigations.
Introduction
A common criterion in decision making regarding return to sport (RTS) after knee ligament injury is that athletes should achieve symmetrical bilateral movement between the injured limb and the noninjured limb. Body-worn wireless inertial measurement units (IMU) can provide clinicians with valuable information about lower-limb kinematics and athletic performance.
Methods
The IMU-based novel kinematic metrics were developed. The Transitional Angular Displacement of Segment (TADS) and Symmetry Index (SI) measures that quantify lower-limb motions and interlimb symmetry during the 4-m side step test (FmSST) were developed. Test–retest reliability was measured in 20 healthy adults. Experimental application of the metrics was also determined in 15 National Collegiate Athletic Association Division I collegiate athletes who completed rehabilitation after a knee ligament injury.
Results
The intraclass correlation coefficient for test–retest reliability for FmSST, TADS right lower limb, TADS left lower limb, and TADS SI was 0.90 (95% confidence interval, [0.61–0.95]); 0.87 [0.63–0.96]; 0.89 [0.64–0.96], and 0.81 [0.58–0.92], respectively. The differences between TADS SI at baseline (preinjury) and RTS were also compared with those between the total times for performing the FmSST at baseline and RTS. There was no significant difference in the FmSST times between baseline and RTS (P = 0.32); however, TADS SI at the time of RTS was significantly lower than at baseline (P = 0.046). A large effect size (d = −1.04) was observed for the change in TADS SI from baseline to RTS.
Conclusions
Using IMU sensor technology can provide quantitative and discrete analysis to detect kinematic differences during agility after a knee ligament injury in the field or nonlaboratory setting. This approach has the potential to help clinicians improve decisions about rehabilitation at a time when an athlete is reintegrating back into sport.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.