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Sensor systems for motion analysis represent an important class of embedded sensor systems for health, and are usually based on MEMS technology (Micro-electro-mechanical systems). Gyroscopes and accelerometers are two examples of MEMS motion sensors that are characterized by their small size, low weight, low power consumption, and low cost. This makes them suitable to be used in wearable systems, intended to measure body movements and posture, and to provide the input for advanced human motion analyzes. However, MEMS-sensors usually are sensitive to environmental disturbances, such as shock, vibration and temperature changes. A large portion of the measured MEMS-sensor signal actually origins from error sources such as noise, offset, and drift. Especially, temperature drift is a well-known error source. Accumulation errors increase the effect of the error during integration of acceleration or angular rate to determine the position or angle. Thus, methods to limit or eliminate the influence of the sources of errors are urgent. Due to MEMS-sensor characteristics and the measurement environment in human motion analysis, signal processing is regarded as an important and necessary part of a MEMS-sensor based human motion analysis system.
This licentiate thesis focuses on signal processing for MEMS-sensor based human motion analysis systems. Different signal processing algorithms were developed, comprising noise reduction, offset/drift estimation and reduction, position accuracy and system stability. Further, real time performance was achieved, also fulfilling the hardware requirement of limited calculation capacity. High-pass filter, LMS algorithm and Kalman filter were used to reduce offset, drift and especially temperature drift in a MEMS-gyroscope based system, while low-pass filter, LMS algorithm, Kalman filter and WFLC algorithms were used for noise reduction. Simple methods such as thresholding with delay and velocity estimation were developed to improve the signal during the position measurements. A combination method of Kalman filter, WFLC algorithm and thresholding with delay was developed with focus on the static stability and position accuracy of the MEMS-gyroscope based system. These algorithms have been implemented into a previously developed MEMS-sensor based motion analysis system. The computational times of the algorithms were all acceptable. Kalman filtering was found efficient to reduce the problem of temperature drift and the WFLC algorithm was found the most suitable method to reduce human physiological tremor and electrical noise. With the Trapezoidal method and low-pass filter, threshold with delay method and velocity estimation method reduced integrated drift in one minute by about 20 meters for the position measurements with MEMS-accelerometer. The threshold with delay method made the signal around zero level to zero without interrupting the continuous movement signal. The combination method of Kalman filter, WFLC algorithm and threshold with delay method showed its superiority on improving the static stability and position accuracy by reducing noise, offset and drift simultaneously, 100% error reduction during the static state, 98.2% position error correction in the case of movements without drift, and 99% with drift.
Reviewer: Nicholas Wickström, Halmstad Univeristy
Grading committee: Nicholas Wickström, Shahina Begum, Nikola Petrovic (Mats Björkman reserve)
Supervisors: Maria Lindén main-supervisor, Magnus Otterskog co-supervisor
Company: Motion Control i Västerås AB