A normative study of the gait features measured by a wearable inertia sensor in a healthy old population.

BACKGROUND: Gait function impairments are associated with the risk of various medical conditions in older adults. As gait function declines with advancing age, normative data are required for proper interpretation of gait function in older adults. RESEARCH PURPOSE: This study aimed to construct age-stratified normative data of non-dimensionally normalized temporal and spatial gait features in healthy older adults. METHODS: We recruited 320 community-dwelling healthy adults aged 65 years or older from two prospective cohort studies. We stratified them into four age groups (65-69, 70-74, 75-79, and 80-84 years). Each age group comprised 40 men and 40 women. We obtained six gait features (cadence, step time, step time variability, step time asymmetry, gait speed, and step length) using a wearable inertia measurement unit attached on the skin overlying L3-L4 on the back. To mitigate the influence of body shape, we non-dimensionally normalized the gait features into unitless values using height and gravity. RESULT: The effect of age group was significant in all raw gait features (p < 0.001 for step time variability, speed and step length; p < 0.05 for cadence, step time and step time asymmetry), and that of sex was significant in the five raw gait features, except for step time asymmetry(p < 0.001 for cadence, step time, speed, and step length; p < 0.05 for step time asymmetry). When gait features were normalized, the effect of age group remained (p < 0.001 for all gait features), whereas that of sex disappeared (p > 0.05 for all gait features). SIGNIFICANCE: Our dimensionless normative data on gait features may be useful in comparative studies of gait function between sexes or ethnicities with different body shapes.

Exploring shared neural substrates underlying cognition and gait variability in adults without dementia.

BACKGROUND: High gait variability is associated with neurodegeneration and cognitive impairments and is predictive of cognitive impairment and dementia. The objective of this study was to identify cortical or subcortical structures of the brain shared by gait variability measured using a body-worn tri-axial accelerometer (TAA) and cognitive function. METHODS: This study is a part of a larger population-based cohort study on cognitive aging and dementia. The study included 207 participants without dementia, with a mean age of 72.6, and 45.4% of them are females. We conducted standardized diagnostic interview including a detailed medical history, physical and neurological examinations, and laboratory tests for cognitive impairment. We obtained gait variability during walking using a body-worn TAA along and measured cortical thickness and subcortical volume from brain magnetic resonance (MR) images. We cross-sectionally investigated the cortical and subcortical neural structures associated with gait variability and the shared neural substrates of gait variability and cognitive function. RESULTS: Higher gait variability was associated with the lower cognitive function and thinner cortical gray matter but not smaller subcortical structures. Among the clusters exhibiting correlations with gait variability, one that included the inferior temporal, entorhinal, parahippocampal, fusiform, and lingual regions in the left hemisphere was also associated with global cognitive and verbal memory function. Mediation analysis results revealed that the cluster's cortical thickness played a mediating role in the association between gait variability and cognitive function. CONCLUSION: Gait variability and cognitive function may share neural substrates, specifically in regions related to memory and visuospatial navigation.

Development of a Gait Feature-Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor.

BACKGROUND AND OBJECTIVES: Gait changes are potential markers of cognitive disorders (CDs). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertial sensor and compared its diagnostic performance for CD with that of the model using the Mini-Mental State Examination (MMSE). METHODS: We enrolled community-dwelling older adults with normal gait from the Korean Longitudinal Study on Cognitive Aging and Dementia and measured their gait features using a wearable inertial sensor placed at the center of body mass while they walked on a 14-m long walkway thrice at comfortable paces. We randomly split our entire dataset into the development (80%) and validation (20%) datasets. We developed a model for classifying CD using logistic regression analysis from the development dataset and validated it in the validation dataset. In both datasets, we compared the diagnostic performance of the model with that using the MMSE. We estimated optimal cutoff score of our model using receiver operator characteristic analysis. RESULTS: In total, 595 participants were enrolled, of which 101 of them experienced CD. Our model included both gait speed and temporal gait variability and exhibited good diagnostic performance for classifying CD from normal cognition in both the development (area under the receiver operator characteristic curve [AUC] = 0.788, 95% CI 0.748-0.823, p < 0.001) and validation datasets (AUC = 0.811, 95% CI 0.729-0.877, p < 0.001). Our model showed comparable diagnostic performance for CD with that of the model using the MMSE in both the development (difference in AUC = 0.026, standard error [SE] = 0.043, z statistic = 0.610, p = 0.542) and validation datasets (difference in AUC = 0.070, SE = 0.073, z statistic = 0.956, p = 0.330). The optimal cutoff score of the gait-based model was >-1.56. DISCUSSION: Our gait-based model using a wearable inertial sensor may be a promising diagnostic marker of CD in older adults. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that gait analysis can accurately distinguish older adults with CDs from healthy controls.

Development of a gait speed estimation model for healthy older adults using a single inertial measurement unit.

Although gait speed changes are associated with various geriatric conditions, standard gait analysis systems, such as laboratory-based motion capture systems or instrumented walkways, are too expensive, spatially limited, and difficult to access. A wearable inertia sensor is cheap and easy to access; however, its accuracy in estimating gait speed is limited. In this study, we developed a model for accurately estimating the gait speed of healthy older adults using the data captured by an inertia sensor placed at their center of body mass (CoM). We enrolled 759 healthy older adults from two population-based cohort studies and asked them to walk on a 14 m long walkway thrice at comfortable paces with an inertia sensor attached to their CoM. In the middle of the walkway, we placed GAITRite™ to obtain the gold standard of gait speed. We then divided the participants into three subgroups using the normalized step length and developed a linear regression model for estimating the gold standard gait speed using age, foot length, and the features obtained from an inertia sensor, including cadence, vertical height displacement, yaw angle, and role angle of CoM. Our model exhibited excellent accuracy in estimating the gold standard gait speed (mean absolute error = 3.74%; root mean square error = 5.30 cm/s; intraclass correlation coefficient = 0.954). Our model may contribute to the early detection and monitoring of gait disorders and other geriatric conditions by making gait assessment easier, cheaper, and more ambulatory while remaining as accurate as other standard gait analysis systems.

Walking-speed estimation using a single inertial measurement unit for the older adults.

BACKGROUND: Although walking speed is associated with important clinical outcomes and designated as the sixth vital sign of the elderly, few walking-speed estimation algorithms using an inertial measurement unit (IMU) have been derived and tested in the older adults, especially in the elderly with slow speed. We aimed to develop a walking-speed estimation algorithm for older adults based on an IMU. METHODS: We used data from 659 of 785 elderly enrolled from the cohort study. We measured gait using an IMU attached on the lower back while participants walked around a 28 m long round walkway thrice at comfortable paces. Best-fit linear regression models were developed using selected demographic, anthropometric, and IMU features to estimate the walking speed. The accuracy of the algorithm was verified using mean absolute error (MAE) and root mean square error (RMSE) in an independent validation set. Additionally, we verified concurrent validity with GAITRite using intraclass correlation coefficients (ICCs). RESULTS: The proposed algorithm incorporates the age, sex, foot length, vertical displacement, cadence, and step-time variability obtained from an IMU sensor. It exhibited high estimation accuracy for the walking speed of the elderly and remarkable concurrent validity compared to the GAITRite (MAE = 4.70%, RMSE = 6.81 𝑐𝑚/𝑠, concurrent validity (ICC (3,1)) = 0.937). Moreover, it achieved high estimation accuracy even for slow walking by applying a slow-speed-specific regression model sequentially after estimation by a general regression model. The accuracy was higher than those obtained with models based on the human gait model with or without calibration to fit the population. CONCLUSIONS: The developed inertial-sensor-based walking-speed estimation algorithm can accurately estimate the walking speed of older adults.