Predictive Walking-Age Health Analyzer
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abstract
A simple, low-power and wearable health analyzer for early identification and management of some diseases is presented. To achieve this goal, we propose a walking pattern analysis system that uses features, such as speed, energy, turn ratio, and bipedal behavior to characterize and classify individuals in distinct walking-ages. A database is constructed from 74 healthy young adults in the age range from 18 to 60 years using the combination of inertial signals from an accelerometer and a gyroscope on a level path including turns. An efficient advanced signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN) was used for feature extraction. Analyzes show that the gait of healthy able-bodied individuals exhibits a natural bipedal asymmetry to a certain level depending on the activity-type and age, which relate to individual's functional attributes rather than pathological gait. The analysis of turn ratio, a measure of activity-transition energy change and stability, indicated turning to be less locally stable than straight-line walking making it a more reliable measure for determining falls and other health issues. Extracted features were used to analyze two distinct walking-age groups of the healthy young adults based on their walking pattern, classifying 18-45 years old individuals in one group and 46-60 years old in the other group. Our proposed simple, inexpensive walking analyzer system can be easily used as an ambulatory screening tool by clinicians to identify at risk population at the early onset of some diseases.
