Electrical and Electronic Engineering - Theses
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ItemMethods for improving foot motion measurement using inertial sensors( 2010)As a promising alternative to laboratory constrained video capture systems in studies of human movement, inertial sensors (accelerometers and gyroscopes) are recently gaining popularity. Secondary quantities such as velocity, displacement and joint angles can be calculated through integration of acceleration and angular velocities. However, it is broadly accepted that this procedure is significantly influenced by cumulative errors due to integration, arising from sensor noise, non-linearities, asymmetries, sensitivity variations and bias drifts. In this study, new methods for improving foot motion from inertial sensors are explored and assessed. Sensor devices have been developed previously, for example, to detect postural changes that determine potential elderly fallers, and monitor a person’s gait. Recently, a gait variable known as minimum toe clearance (MTC) has been proposed to describe age-related declines in gait with better success as a predictor of falls risk. The MTC is the minimum vertical distance between the lowest point on the shoe and the ground during the mid-swing phase of the gait cycle. It is therefore of our interest to design a cost effective but accurate solution to measure toe clearance data which can then be used to identify the individuals at risk of falling. In this study, hardware, firmware and software features from off-the-shelf inertial sensors and wireless motes are evaluated and their configuration optimized for this application. A strap-down method, which consists of the minimizing of the integration drift due to cumulative errors, is evaluated off-line. Analysis revealed the necessity of band-pass filtering methods to correct systematic sensor errors that dramatically reduce the accuracy in estimating foot motion. Cumulative errors were studied in the frequency domain, employing content of inertial sensor foot motion evaluated against a ’gold standard’ video-based device, namely the Optotrak Certus NDI. In addition, the effectiveness of applying band-pass filtering to raw inertial sensor data is assessed, under the assumption that sensor drift errors occur in the low frequency spectrum. The normalized correlation coefficient ρ of the Fast Fourier Transform (FFT) spectra corresponding to vertical toe acceleration from inertial sensors and from a video capture system as a function of digital band-pass filter parameters is compared. The Root Mean Square Error (RMSE) of the vertical toe displacement is calculated for 5 healthy subjects over a range of 4 walking speeds. The lowest RMSE and highest cross correlation achieved for the slowest walking speed of 2.5km/h was 3.06cmand 0.871 respectively, and 2.96cm and 0.952 for the fastest speed of 5.5km/h.