Identify Subjects at Risk for Falling Using Acceleration Based Gait Analysis System

Overview

Because the increasing fall problem, mainly due to an impaired gait and balance ability and partly caused by trips, this study will investigate fall risk by detecting fall related movement characteristics and by detecting stumbles inclusive the compensation mechanism to recover from the trip. Based on the promising results using accelerometry for accurate and objective gait analysis, fall risk will be measured in younger and older (\>60y) subjects using a triaxial accelerometer.

To investigate fall risk objectively, fall related movement characteristics (based on gait and balance) and the ability to compensate for near falls are analyzed in younger and older (\>60y) subjects under standardized laboratory conditions. Four tests will be performed to link specific movement parameters, balance performance and the ability to recover from a trip with fall risk: 1. Fall risk will be assessed using the Tinetti Scale, the gold standard for fall risk assessment . This scale consist of a gait and balance score. Based on this scale, subjects are classified as being at risk/ not at risk for falling. 2. A gait test will be performed to analyze movement parameters. Subjects have to walk 6 times a 20 meter distance at preferred speed while a small (56mmx61mmx15mm), light weight (5g) and ambulant accelerometer is attached on the sacrum with an elastic belt. The accelerometer measures accelerations of the body in three directions (anterior-posterior, media-lateral and cranial-caudal) with a sample frequency of 100Hz. 3. The balance ability will be tested by performing 4 balance tasks while the same accelerometer measures the movements of the body. Subjects have to stand with feet closed on a normal or foam surface while having the eyes open and closed. 4. Finally a stumble experiment is done to assess the ability to compensate for a trip. Subjects are asked to walk at their preferred speed on a treadmill, while wearing the accelerometer attached at the sacrum. After 2 minutes of normal walking, stumbles are simulated unexpectedly using an extending tripping leash attached to both legs. The subjects are able to move freely due to unwinding and winding of the cord on a spill on a fixed frame behind the treadmill. This spill has a blocking device capable of blocking the leash very shortly. During the mid or initial swing of one of the legs, the examiner blocks the leash causing the subject to trip. Falling, however, is not possible due to a safety harness attached to the ceiling. When this harness starts bearing weight an emergency switch is engaged stopping the treadmill immediately. After a perturbation, subjects get several seconds to recover until a new perturbation is applied. This measurement is repeated while walking at a slow (40% Fr preferred speed) and fast (20% Fr preferred speed) speed. The whole experiment will be recorded on video to validate the acceleration based stumble detection. To investigate the underlying mechanism of age related mobility changes, two other tests are incorporated: * The cognition of the subjects will be tested using a reaction time test. Subjects sit behind the computer with their middle and forefinger on two keys of the typewriter. Four squares will appear at the computer screen. Or all squares are red and one of them will change in green (=uncued condition) or two red squares appear at the left (right) side while one square at the right (left) side will turn green. Subjects have to push the corresponding key on the typewriter as fast but also as accurate as possible. With this test the ability to change an automatic response (in stead of responding left when stimulus appears left) can be investigated using reaction times. First the subjects get an instruction, than they get the possibility to try and exercise 10 times, while afterwards the real test will start. 120 attempts are performed in which the cued and uncued stimuli appear with different time intervals (100ms, 250ms, 500ms, 750ms and 12000 ms). * Muscle strength of the right lower leg is tested using the Cybex. Subjects have to produce maximal force during an extension and flexion test. All acceleration data will be analyzed using specific algorithms programmed in Matlab(c). Statistical analysis will be performed in SPSS using pearson correlation to investigate correlations between gait parameters, balance characteristics and the ability to recover from a trip. Moreover Pearson correlations and regression analysis will be done to investigate the relation of cognition and muscle strength with fall risk (gait, stumble recovery and balance). Pearson correlation will also be used to validate the objective gait and balance test with the Berg Balance Scale. Differences in muscle strength, cognition gait, balance and compensation ability between younger and older subjects will be investigated using ANOVA (p\< 0.005).

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