Gait parameter estimation from a miniaturized ear-worn sensor using singular spectrum analysis and longest common subsequence

This paper presents a new approach to gait analysis and parameter estimation from a single miniaturised earworn sensor embedded with a triaxial accelerometer. Singular spectrum analysis (SSA) combined with the longest common subsequence (LCSS) algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20 minutes on a treadmill with an increasing incline of 2% every 2 minutes. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance and stride times were obtained as 35.5±3.99ms, 36.9 ± 3.84ms, and 17.9 ± 2.29ms, respectively.