| 1. |
- Du, Jiaying, et al.
(författare)
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Noise reduction for a MEMS-gyroscope-based head mouse
- 2015
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Ingår i: Studies in Health Technology and Informatics, Volume 211. - Västerås, Sweden : IOS Press. - 9781614995159 ; , s. 98-104
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Konferensbidrag (refereegranskat)abstract
- In this paper, four different signal processing algorithms which can be applied to reduce the noise from a MEMS-gyroscope-based computer head mouse are presented. MEMS-gyroscopes are small, light, cheap and widely used in many electrical products. MultiPos, a MEMS-gyroscope-based computer head mouse system was designed for persons with movement disorders. Noise such as physiological tremor and electrical noise is a common problem for the MultiPos system. In this study four different signal processing algorithms were applied and evaluated by simulation in MATLAB and implementation in a dsPIC, with aim to minimize the noise in MultiPos. The algorithms were low-pass filter, Least Mean Square (LMS) algorithm, Kalman filter and Weighted Fourier Linear Combiner (WFLC) algorithm. Comparisons and system tests show that these signal processing algorithms can be used to improve the MultiPos system. The WFLC algorithm was found the best method for noise reduction in the application of a MEMS-gyroscope-based head mouse.
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| 2. |
- Du, Jiaying, et al.
(författare)
-
Signal processing algorithms for position measurement with MEMS-based accelerometer
- 2015
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Ingår i: IFMBE Proceedings, vol. 48. - Cham : Springer International Publishing. - 9783319129662 ; , s. 36-39
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Konferensbidrag (refereegranskat)abstract
- This paper presents signal processing algorithms for position measurements with MEMS-accelerometers in a motion analysis system. The motion analysis system is intended to analyze the human motion with MEMS-based-sensors which is a part of embedded sensor systems for health. MEMS-accelerometers can be used to measure acceleration and theoretically the velocity and position can be derived from the integration of acceleration. However, there normally is drift in the measured acceleration, which is enlarged under integration. In this paper, the signal processing algorithms are used to minimize the drift during integration by MEMS-based accel-erometer. The simulation results show that the proposed algorithms improved the results a lot. The algorithm reduced the drift in one minute by about 20 meters in the simulation. It can be seen as a reference of signal processing for the motion analysis system with MEMS-based accelerometer in the future work.
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