Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters

A Novel Mathematical Approach to Gait Analysis: The Reliability and Validity of the ZAY Angle for Step Length Estimation in Healthy Adults

(1) Background: The application of a mathematical formula to human gait at certain phases is a considerable method to avoid the issues associated with complicated procedures of gait assessment. The purpose of this study was to identify the validity and reliability of an angle (the ZAY angle) in estimating and predicting the step length in healthy subjects. (2) Methods: Thirty-three college-aged students participated in this study. For an assessment of each participant's gait, a 4.5 m walkway was covered with a weight paper roll to mark the participant's footprints, providing the step lengths of six consecutive steps for two trials. At the same time, a video recording was captured and analyzed by the Coach's Eye application to determine the step angle (ß). The arc length formula was utilized to calculate the ZAY angle (θ). Spearman's rho correlation coefficient and the interclass correlation coefficient were used to test the validity and reliability of the ZAY angle in determining individualized step lengths in healthy subjects. Simple linear regression was used to test if the calculated angle could significantly predict the step length. (3) Results: The Spearman rho correlation between the analyzed and calculated angles was significant for all three step lengths (p < 0.05). It was found that the calculated angle could significantly predict the step length (β = 0.91, p < 0.05). The ICC was very high (p < 0.05). (4) Conclusions: The ZAY angle is a valid and reliable angle that can be used to estimate individualized step lengths. Clinicians could include this angle in their gait analysis profiles to achieve individualized assessment and rehabilitation goals.

Extracted step parameters during the timed up and go test discriminate between groups with different levels of cognitive ability-a cross-sectional study

BACKGROUND

Identifying cognitive impairment at an early stage is important to enable preventive treatment and lifestyle changes. As gait deviations precede cognitive impairment, the aim of this study was to investigate if step parameters during different Timed Up and Go (TUG) conditions could discriminate between people with different cognitive ability.

METHODS

Participants (N = 304) were divided into the following groups: (1) controls, n = 50, mean age:73, 44% women; (2) Subjective cognitive Impairment (SCI), n = 71, mean age:67, 45% women; (3) Mild Cognitive Impairment (MCI), n = 126, mean age: 73, 42% women; and (4) dementia disorders, n = 57, mean age: 78, 51% women. Participants conducted TUG and two motor-cognitive TUG-conditions: TUG while naming animals (TUGdt-NA) and reciting months in reverse order (TUGdt-MB). Tests were video recorded for data extraction of valid spatiotemporal parameters: step length, step width, step duration, single step duration and double step duration. Step length was investigated with the step length/body height ratio (step length divided by body height). Logistic regression models (adjusted for age, sex and education) investigated associations between step parameters and dichotomous variables of groups adjacent in cognitive ability: dementia disorders vs. MCI, MCI vs. SCI, and SCI vs. controls. Results were presented as standardized odds ratios (sORs), with 95% confidence intervals (CI95) and p-values (significance level: p < 0.05). The areas under the Receiver Operating Characteristic curves were presented for the step parameters/conditions with the highest sORs and, where relevant, optimal cutoff values were calculated.

RESULTS

Step length showed greatest overall ability to significantly discriminate between adjacent groups (sOR ≤ . 67, CI95: .45-.99, p = ≤ . 047) during all group comparisons/conditions except three. The highest sOR for step-length was obtained when discriminating between SCI vs controls during TUGdt-MB (sOR = .51, CI95:.29- .87, p = .014), whereby the area under the curve was calculated (c-statistics = .700). The optimal cut-off indicated a step length of less than 32.9% (CI95 = 22.1-43.0) of body height to identify SCI compared with controls.

CONCLUSIONS

The results indicate that step length may be important to assess during TUG, for discrimination between groups with different cognitive ability; and that the presented cut-off has potential to aid early detection of cognitive impairment.

TRIAL REGISTRATION NUMBER

NCT05893524 (retrospectively registered 08/06/23).

Dual-Task Interference Effects on Lower-Extremity Muscle Activities during Gait Initiation and Steady-State Gait among Healthy Young Individuals, Measured Using Wireless Electromyography Sensors

To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual tasks on lower-limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. The participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and coactivation in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that, during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle coactivation between the swing and stance limbs compared with the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared with the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, the knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.

Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength

The need for developing a simple and effective assessment tool for muscle mass has been increasing in a rapidly aging society. This study aimed to evaluate the feasibility of the surface electromyography (sEMG) parameters for estimating muscle mass. Overall, 212 healthy volunteers participated in this study. Maximal voluntary contraction (MVC) strength and root mean square (RMS) values of motor unit potentials from surface electrodes on each muscle (biceps brachii, triceps brachii, biceps femoris, rectus femoris) during isometric exercises of elbow flexion (EF), elbow extension (EE), knee flexion (KF), knee extension (KE) were acquired. New variables (MeanRMS, MaxRMS, and RatioRMS) were calculated from RMS values according to each exercise. Bioimpedance analysis (BIA) was performed to determine the segmental lean mass (SLM), segmental fat mass (SFM), and appendicular skeletal muscle mass (ASM). Muscle thicknesses were measured using ultrasonography (US). sEMG parameters showed positive correlations with MVC strength, SLM, ASM, and muscle thickness measured by US, but showed negative correlations with SFM. An equation was developed for ASM: ASM = -26.04 + 20.345 × Height + 0.178 × weight - 2.065 × (1, if female; 0, if male) + 0.327 × RatioRMS(KF) + 0.965 × MeanRMS(EE) (SEE = 1.167, adjusted R² = 0.934). sEMG parameters in controlled conditions may represent overall muscle strength and muscle mass in healthy individuals.

Gait speed and individual characteristics are related to specific gait metrics in neurotypical adults

Gait biofeedback is a well-studied strategy to reduce gait impairments such as propulsion deficits or asymmetric step lengths. With biofeedback, participants alter their walking to reach the desired magnitude of a specific parameter (the biofeedback target) with each step. Biofeedback of anterior ground reaction force and step length is commonly used in post-stroke gait training as these variables are associated with self-selected gait speed, fall risk, and the energy cost of walking. However, biofeedback targets are often set as a function of an individual's baseline walking pattern, which may not reflect the ideal magnitude of that gait parameter. Here we developed prediction models based on speed, leg length, mass, sex, and age to predict anterior ground reaction force and step length of neurotypical adults as a possible method for personalized biofeedback. Prediction of these values on an independent dataset demonstrated strong agreement with actual values, indicating that neurotypical anterior ground reaction forces can be estimated from an individual's leg length, mass, and gait speed, and step lengths can be estimated from individual's leg length, mass, age, sex, and gait speed. Unlike approaches that rely on an individual's baseline gait, this approach provides a standardized method to personalize gait biofeedback targets based on the walking patterns exhibited by neurotypical individuals with similar characteristics walking at similar speeds without the risk of over- or underestimating the ideal values that could limit feedback-mediated reductions in gait impairments.

Acute Fluid Intake Impacts Assessment of Body Composition via Bioelectrical Impedance Analysis. A Randomized, Controlled Crossover Pilot Trial

Bioelectrical impedance analysis (BIA) has proven to be particularly useful due to its inexpensive and rapid assessment of total body water and body density. However, recent fluid intake may confound BIA results since equilibration of fluid between intra- and extracellular spaces may take several hours and furthermore, ingested fluids may not be fully absorbed. Therefore, we aimed to evaluate the impact of different fluid compositions on the BIA. A total of eighteen healthy individuals (10 females, mean ± SD age of 23.1 ± 1.8 years) performed a baseline measurement of body composition before they consumed isotonic 0.9% sodium-chloride (ISO), 5% glucose (GLU) or Ringer (RIN) solutions. During the visit of the control arm (CON), no fluid was consumed. Further impedance analyses were conducted every 10 min after the fluid consumption for 120 min. We found statistically significant interactions between the effects of solution ingestion and time for intra- (ICW, p < 0.01) and extracellular water (ECW, p < 0.0001), skeletal muscle mass (SMM, p < 0.001) and body fat mass (FM, p < 0.01), respectively. Simple main effects analysis showed that time had a statistically significant effect on changes in ICW (p < 0.01), ECW (p < 0.01), SMM (p < 0.01) and FM (p < 0.01), while fluid intake did not have a significant effect. Our results highlight the importance of a standardized pre-measurement nutrition, with particular attention to hydration status when using a BIA for the evaluation of body composition.