Load carriage changes tibiofemoral arthrokinematics during ambulatory tasks in recruit-aged women.

The introduction of women into U.S. military ground close combat roles requires research into sex-specific effects of military training and operational activities. Knee osteoarthritis is prevalent among military service members; its progression has been linked to occupational tasks such as load carriage. Analyzing tibiofemoral arthrokinematics during load carriage is important to understand potentially injurious motion and osteoarthritis progression. The study purpose was to identify effects of load carriage on knee arthrokinematics during walking and running in recruit-aged women. Twelve healthy recruit-aged women walked and ran while unloaded (bodyweight [BW]) and carrying additional + 25%BW and + 45%BW. Using dynamic biplane radiography and subject-specific bone models, tibiofemoral arthrokinematics, subchondral joint space and center of closest contact location between subchondral bone surfaces were analyzed over 0-30% stance (separate one-way repeated measures analysis of variance, load by locomotion). While walking, medial compartment contact location was 5% (~ 1.6 mm) more medial for BW than + 45%BW at foot strike (p = 0.03). While running, medial compartment contact location was 4% (~ 1.3 mm) more lateral during BW than + 25%BW at 30% stance (p = 0.04). Internal rotation was greater at + 45%BW compared to + 25%BW (p < 0.01) at 30% stance. Carried load affects tibiofemoral arthrokinematics in recruit-aged women. Prolonged load carriage could increase the risk of degenerative joint injury in physically active women.

Recruit-aged adults may preferentially weight task goals over deleterious cost functions during short duration loaded and imposed gait tasks.

Optimal motor control that is stable and adaptable to perturbation is reflected in the temporal arrangement and regulation of gait variability. Load carriage and forced-marching are common military relevant perturbations to gait that have been implicated in the high incidence of musculoskeletal injuries in military populations. We investigated the interactive effects of load magnitude and locomotion pattern on motor variability, stride regulation and spatiotemporal complexity during gait in recruit-aged adults. We further investigated the influences of sex and task duration. Healthy adults executed trials of running and forced-marching with and without loads at 10% above their gait transition velocity. Spatiotemporal parameters were analyzed using a goal equivalent manifold approach. With load and forced-marching, individuals used a greater array of motor solutions to execute the task goal (maintain velocity). Stride-to-stride regulation became stricter as the task progressed. Participants exhibited optimal spatiotemporal complexity with significant but not meaningful differences between sexes. With the introduction of load carriage and forced-marching, individuals relied on a strategy that maximizes and regulates motor solutions that achieve the task goal of velocity specifically but compete with other task functions. The appended cost penalties may have deleterious effects during prolonged execution, potentially increasing the risk of musculoskeletal injuries.

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

Increases in Load Carriage Magnitude and Forced Marching Change Lower-Extremity Coordination in Physically Active, Recruit-Aged Women.

The objective was to examine the interactive effects of load magnitude and locomotion pattern on lower-extremity joint angles and intralimb coordination in recruit-aged women. Twelve women walked, ran, and forced marched at body weight and with loads of +25%, and +45% of body weight on an instrumented treadmill with infrared cameras. Joint angles were assessed in the sagittal plane. Intralimb coordination of the thigh-shank and shank-foot couple was assessed with continuous relative phase. Mean absolute relative phase (entire stride) and deviation phase (stance phase) were calculated from continuous relative phase. At heel strike, forced marching exhibited greater (P < .001) hip flexion, knee extension, and ankle plantar flexion compared with running. At mid-stance, knee flexion (P = .007) and ankle dorsiflexion (P = .04) increased with increased load magnitude for all locomotion patterns. Forced marching (P = .009) demonstrated a "stiff-legged" locomotion pattern compared with running, evidenced by the more in-phase mean absolute relative phase values. Running (P = .03) and walking (P = .003) had greater deviation phase than forced marching. Deviation phase increased for running (P = .03) and walking (P < .001) with increased load magnitude but not for forced marching. With loads of >25% of body weight, forced marching may increase risk of injury due to inhibited energy attenuation up the kinetic chain and lack of variability to disperse force across different supportive structures.

Loaded forced-marching shifts mechanical contributions proximally and disrupts stride-to-stride joint work modulation in recruit aged women.

BACKGROUND: Military personnel in combat roles often perform gait tasks with additional load, which can affect the contributions of joint mechanical work (positive and negative). Furthermore, different locomotion patterns can also affect joint specific work contributions. While mean behavior of joint work is important to understanding gait, changes in joint kinetic modulation, or the regulation/control of stride-to-stride joint work variability is necessary to elucidate locomotor system function. Suboptimal modulation exhibited as a stochastic time-series (large fluctuation followed by an opposite smaller fluctuation) could potentially affect locomotion efficiency and portend injury risk. It remains unclear how the locomotor system responds to a combination of load perturbations and varying locomotion patterns. RESEARCH QUESTION: What are the interactive effects of load magnitude and locomotion pattern on joint positive/negative work and joint work modulation in healthy, active, recruit-aged women? METHODS: Eleven healthy, active, recruit-aged (18-33 years) women ran and forced-marched (walking at a velocity an individual would typically jog) in bodyweight (BW), an additional 25 % of BW (+25 %BW) and an additional 45 % of BW (+45 %BW) conditions at a velocity above their gait transition velocity. Joint work was calculated as the time integral of joint power. Joint work modulation was assessed with detrended fluctuation analysis (DFA) on consecutive joint work time-series. RESULTS: Joint work contributions shifted proximally for forced-marching demonstrated by lesser (p < .001) positive/negative ankle work but greater (p = .001) positive hip work contributions compared to running. Running exhibited optimal positive ankle work modulation compared to forced-marching (p = .040). Knee and ankle negative joint work modulation was adversely impacted compared to the hip during forced-marching (p < .001). SIGNIFICANCE: Employing forced-marching gait while under loads of 25 and 45 % of BW reduces the ability of the plantar-flexors and knee extensors to optimally contribute to energy absorption and propulsion in recruit-aged women, potentially reducing metabolic efficiency and increasing injury risk.

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification.

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

Load Magnitude and Locomotion Pattern Alter Locomotor System Function in Healthy Young Adult Women.

INTRODUCTION: During cyclical steady state ambulation, such as walking, variability in stride intervals can indicate the state of the system. In order to define locomotor system function, observed variability in motor patterns, stride regulation and gait complexity must be assessed in the presence of a perturbation. Common perturbations, especially for military populations, are load carriage and an imposed locomotion pattern known as forced marching (FM). We examined the interactive effects of load magnitude and locomotion pattern on motor variability, stride regulation and gait complexity during bipedal ambulation in recruit-aged females. METHODS: Eleven healthy physically active females (18-30 years) completed 1-min trials of running and FM at three load conditions: no additional weight/bodyweight (BW), an additional 25% of BW (BW + 25%), and an additional 45% of BW (BW + 45%). A goal equivalent manifold (GEM) approach was used to assess motor variability yielding relative variability (RV; ratio of "good" to "bad" variability) and detrended fluctuation analysis (DFA) to determine gait complexity on stride length (SL) and stride time (ST) parameters. DFA was also used on GEM outcomes to calculate stride regulation. RESULTS: There was a main effect of load (p = 0.01) on RV; as load increased, RV decreased. There was a main effect of locomotion (p = 0.01), with FM exhibiting greater RV than running. Strides were regulated more tightly and corrected quicker at BW + 45% compared (p < 0.05) to BW. Stride regulation was greater for FM compared to running. There was a main effect of load for gait complexity (p = 0.002); as load increased gait complexity decreased, likewise FM had less (p = 0.02) gait complexity than running. DISCUSSION: This study is the first to employ a GEM approach and a complexity analysis to gait tasks under load carriage. Reduction in "good" variability as load increases potentially exposes anatomical structures to repetitive site-specific loading. Furthermore, load carriage magnitudes of BW + 45% potentially destabilize the system making individuals less adaptable to additional perturbations. This is further evidenced by the decrease in gait complexity, which all participants demonstrated values similarly observed in neurologically impaired populations during the BW + 45% load condition.

Load carriage magnitude and locomotion strategy alter knee total joint moment during bipedal ambulatory tasks in recruit-aged women.

Knee osteoarthritis (OA) is prevalent among female soldiers, resulting in limited duty and long term adverse ambulatory effects. A proposed mechanism to the development of knee OA is the assiduous execution of load carriage tasks. Soldiers are often required to maintain a walking gait with load at velocities beyond their gait transition velocity (GTV) known as forced marching. The primary aim of this investigation is to determine the interactive effects of load magnitude and locomotion pattern on relative knee total joint moment (KTJM) in healthy recruit-aged women. The secondary aims are to determine knee total joint moment limb differences and to determine the interactive effect of load magnitude and locomotion pattern on the percent contributions of each plane of motion moment. Individuals were tasked with running and forced marching at 10% above their GTV at body weight (BW) and with an additional 25% and 45% of their BW. KTJM was analyzed at two specific gait events of heel-strike and mid-stance. At heel-strike, forced marching exhibited greater KTJM compared to run for all load conditions but running had greater KTJM than forced marching at mid-stance. The forced marching pattern exhibited larger KTJM for the dominant limb at both gait events compared to running. Lastly, at mid-stance the knee adduction moment percent (KAM%) contribution was greater for forced marching compared to running. The forced marching pattern demonstrates joint kinetics that may be more deleterious with prolonged exposure. Likewise, forced marching induced KAM% similar to those already suffering from knee OA.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a 'typical' or 'stable' gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2-4 runs were needed to define a stable running pattern for univariate, and 4-5 days were necessary for multivariate analysis across all inclination conditions. Pearson's correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63-0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual's stable gait pattern.

New considerations for collecting biomechanical data using wearable sensors: Number of level runs to define a stable running pattern with a single IMU.

Wearable technology can be used to quantify running biomechanical patterns in a runner's natural environment, however, changes in external factors during outdoor running may influence a runner's typical gait pattern. Therefore, the purpose of this study was to determine how many runs are needed to define a stable or typical running pattern. Six biomechanical variables were recorded using a single wearable sensor placed on the lower back during ten outdoor runs for twelve runners. Univariate and multivariate distributions were created and based on the probability density function, the percent of similar data points (within 95%) from each unique run for the same runner were determined. Stability was defined when the addition of data from a new run resulted in less than a 5% change in the probability density function. To cross-validate, the percent of similar data points at stability was compared between the same and different runners using a repeated-measures MANOVA (Bonferroni-corrected α = 0.007). The maximum number of runs needed to reach stability for univariate and multivariate analyses was four and five, respectively. There was a significant overall effect on similar data points between the same and different runners (p = 0.001), with a greater percent of similar data points for the same runner compared to other runners (p < 0.007). Based on biomechanical data collected using a single wearable sensor placed on the lower back, this is the first study to show that four (univariate) to five (multivariate) runs are needed to establish a stable running pattern in real-world settings.

Design and development of an automated, portable and handheld tablet personal computer-based data acquisition system for monitoring electromyography signals during rehabilitation.

This article describes the design of a robust, inexpensive, easy-to-use, small, and portable online electromyography acquisition system for monitoring electromyography signals during rehabilitation. This single-channel (one-muscle) system was connected via the universal serial bus port to a programmable Windows operating system handheld tablet personal computer for storage and analysis of the data by the end user. The raw electromyography signals were amplified in order to convert them to an observable scale. The inherent noise of 50 Hz (Malaysia) from power lines electromagnetic interference was then eliminated using a single-hybrid IC notch filter. These signals were sampled by a signal processing module and converted into 24-bit digital data. An algorithm was developed and programmed to transmit the digital data to the computer, where it was reassembled and displayed in the computer using software. Finally, the following device was furnished with the graphical user interface to display the online muscle strength streaming signal in a handheld tablet personal computer. This battery-operated system was tested on the biceps brachii muscles of 20 healthy subjects, and the results were compared to those obtained with a commercial single-channel (one-muscle) electromyography acquisition system. The results obtained using the developed device when compared to those obtained from a commercially available physiological signal monitoring system for activities involving muscle contractions were found to be comparable (the comparison of various statistical parameters) between male and female subjects. In addition, the key advantage of this developed system over the conventional desktop personal computer-based acquisition systems is its portability due to the use of a tablet personal computer in which the results are accessible graphically as well as stored in text (comma-separated value) form.