Discriminative Mobility Characteristics between Neurotypical Young, Middle-Aged, and Older Adults Using Wireless Inertial Sensors

Exploring the potential of electrical bioimpedance technique for analyzing physical activity

Introduction

Exercise physiology investigates the complex and multifaceted human body responses to physical activity (PA). The integration of electrical bioimpedance (EBI) has emerged as a valuable tool for deepening our understanding of muscle activity during exercise.

Method

In this study, we investigate the potential of using the EBI technique for human motion recognition. We analyze EBI signals from the quadriceps muscle and extensor digitorum longus muscle acquired when healthy participants in the range 20-30 years of age performed four lower body PAs, namely squats, lunges, balance walk, and short jumps.

Results

The characteristics of EBI signals are promising for analyzing PAs. Each evaluated PA exhibited unique EBI signal characteristics.

Discussion

The variability in how PAs are executed leads to variations in the EBI signal characteristics, which, in turn, can provide insights into individual differences in how a person executes a specific PA.

Temporal Variability in Stride Kinematics during the Application of TENS: A Machine Learning Analysis

INTRODUCTION

The purpose of our report was to use a Random Forest classification approach to predict the association between transcutaneous electrical nerve stimulation (TENS) and walking kinematics at the stride level when middle-aged and older adults performed the 6-min test of walking endurance.

METHODS

Data from 41 participants (aged 64.6 ± 9.7 yr) acquired in two previously published studies were analyzed with a Random Forest algorithm that focused on upper and lower limb, lumbar, and trunk kinematics. The four most predictive kinematic features were identified and utilized in separate models to distinguish between three walking conditions: burst TENS, continuous TENS, and control. SHAP analysis and linear mixed models were used to characterize the differences among these conditions.

RESULTS

Modulation of four key kinematic features-toe-out angle, toe-off angle, and lumbar range of motion (ROM) in coronal and sagittal planes-accurately predicted walking conditions for the burst (82% accuracy) and continuous (77% accuracy) TENS conditions compared with control. Linear mixed models detected a significant difference in lumbar sagittal ROM between the TENS conditions. SHAP analysis revealed that burst TENS was positively associated with greater lumbar coronal ROM, smaller toe-off angle, and less lumbar sagittal ROM. Conversely, continuous TENS was associated with less lumbar coronal ROM and greater lumbar sagittal ROM.

CONCLUSIONS

Our approach identified four kinematic features at the stride level that could distinguish between the three walking conditions. These distinctions were not evident in average values across strides.

Turning speed as a more responsive metric of age-related decline in mobility: A comparative study with gait speed

BACKGROUND

Navigating your environment requires both straight-line gait as well as turning. Gait speed normative values are well established and utilized in determining a person's functional status, however, it has limitations. This study sought to examine whether turning speed declines with age and how it compared to gait speed age-related decline.

METHODS

A secondary analysis was performed on 275 community dwelling adults between the ages of 18-88 that performed a timed walking test with an inertial measurement unit on their lumbar spine. Turning speed and walking speed were extracted for each participant. A series of mixed models were compared, and Akaike's Information Criterion was used to determine the best fit model between age and turning speed and age and gait speed.

FINDINGS

Turning speed and gait speed normative values were reported for each age decade. A linear model with a random intercept of "Condition" was used to assess the relationship between age and turning speed. The results indicated a significant negative relationship between age and turning speed (B = -0.66, p < 0.001). A spline-fit model determined a significant negative relationship between age and gait speed after the age of 65 (B = -0.0097, p = 0.002). The effect of age on gait speed before age 65 was not significant.

INTERPRETATION

Turning speed significantly declines with age in a linear fashion while gait speed begins to decline after age 65. Turning speed may be more responsive to age than gait speed. More research is needed to determine if the decline in turning speed with age is associated with a decline in function.

Beyond timing and step counting in 360° turning-in-place assessment: a scoping review

BACKGROUND

Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed.

METHODS

Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place. From the selected articles, the study population, types of instruments used, turn detection method, and how the turning-in-place characteristics were calculated.

RESULTS

Twenty-one papers met the inclusion criteria. The subject groups involved in the reviewed studies included young, middle-aged, and older adults, stroke, multiple sclerosis and Parkinson's disease patients. Inertial measurement units (16 studies) and motion camera systems (5 studies) were employed for gathering measurement data, force platforms were rarely used (2 studies). Two studies used commercial software for turn detection, six studies referenced previously published algorithms, two studies developed a custom detector, and eight studies did not provide any details about the turn detection method. The most frequently used parameters were mean angular velocity (14 cases, 7 studies), turn duration (13 cases, 13 studies), peak angular velocity (8 cases, 8 studies), jerkiness (6 cases, 5 studies) and freezing-of-gait ratios (5 cases, 5 studies). Angular velocities were derived from sensors placed on the lower back (7 cases, 4 studies), trunk (4 cases, 2 studies), and shank (2 cases, 1 study). The rest (9 cases, 8 studies) did not report sensor placement. Calculation of the freezing-of-gait ratio was based on the acceleration of the lower limbs in all cases. Jerkiness computation employed acceleration in the medio-lateral (4 cases) and antero-posterior (1 case) direction. One study did not reported any details about jerkiness computation.

CONCLUSION

This review identified the capabilities of turning-in-place assessment in identifying movement differences between the various subject groups. The results, based on data acquired by inertial measurement units across studies, are comparable. A more in-depth analysis of tests developed for gait, which has been adopted in turning-in-place, is needed to examine their validity and accuracy.