Differences Between Gait on Stairs and Flat Surfaces in Relation to Fall Risk and Future Falls

Abstract

We used body-worn inertial sensors to quantify differences in semi-free-living gait between stairs and on normal flat ground in older adults, and investigated the utility of assessing gait on these terrains for predicting the occurrence of multiple falls. Eighty-two community-dwelling older adults wore two inertial sensors, on the lower back and the right ankle, during several bouts of walking on flat surfaces and up and down stairs, in between rests and activities of daily living. Derived from the vertical acceleration at the lower back, step rate was calculated from the signal's fundamental frequency. Step rate variability was the width of this fundamental frequency peak from the signal's power spectral density. Movement vigor was calculated at both body locations from the signal variance. Partial Spearman correlations between gait parameters and physiological fall risk factors (components from the Physiological Profile Assessment) were calculated while controlling for age and gender. Overall, anteroposterior vigor at the lower back in stair descent was lower in subjects with longer reaction times. Older adults walked more slowly on stairs, but they were not significantly slower on flat surfaces. Using logistic regression, faster step rate in stair descent was associated with multiple prospective falls over 12 months. No significant associations were shown from gait parameters derived during walking upstairs or on flat surfaces. These results suggest that stair descent gait may provide more insight into fall risk than regular walking and stair ascent, and that further sensor-based investigation into unsupervised gait on different terrains would be valuable.

Estimation and validation of temporal gait features using a markerless 2D video system

“…The use of inertial sensors can be used to tackle the identified shortcomings of the optoelectronic motion capture system [15], as many studies have demonstrated their validity in the estimation and assessment of gait features [3]. However, these sensors also need to be mounted on distinct and specific locations of the users" body when one wishes to monitor their gait.…”

“…Clinical gait analysis using temporal gait features, such as those mentioned above, has gained popularity for instance for the evaluation of rehabilitation progression in patients after a surgical procedure (e.g., after a knee replacement) [2], or in the prediction of fall risks in the elderly population [3]. However, the acquisition and evaluation of the features from which BGIs are derived is usually performed in dedicated laboratories, often resulting in an expensive and a timeconsuming task.…”

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

“…Specifically, fall prediction can be verified by analyzing fall risk assessments of the elderly, considering combinations of risk factors, and data produced by wearable sensors [41]. Body-worn inertial sensors can be used to explore the differences in semi-free-living gait between activity on stairs and on a regular, flat floor surface in regard to elderly and impaired daily activities [42]. A single standing time model was also used for predicting the fall risk of the elderly population [43].…”

A Multi-Sensor Cane Can Detect Changes in Gait Caused by Simulated Gait Abnormalities and Walking Terrains

“…The gold standard for mobility and gait monitoring most often includes full-body motion capture systems, video systems, and force places that are capable of providing highly detailed and quantitative gait data [6,7]. These systems, however, are often prohibitively expensive for most clinical settings, require the need for additional skilled technicians, and are limited to indoor use in laboratory settings [8][9][10]. Moreover, these solutions can only provide gait data for a limited number of strides thus preventing them from being a viable option for community-based, long-term monitoring.…”