In the medical field, there is a need for small ambulatory sensor systems for measuring the kinematics of body segments. Current methods for ambulatory measurement of body orientation have limited accuracy when the body moves. The aim of the paper was to develop and validate a method for accurate measurement of the orientation of human body segments using an inertial measurement unit (IMU). An IMU containing three single-axis accelerometers and three single-axis micromachined gyroscopes was assembled in a rectangular box, sized 20 x 20 x 30 mm. The presented orientation estimation algorithm continuously corrected orientation estimates obtained by mathematical integration of the 3D angular velocity measured using the gyroscopes. The correction was performed using an inclination estimate continuously obtained using the signal of the 3D accelerometer. This reduces the integration drift that originates from errors in the angular velocity signal. In addition, the gyroscope offset was continuously recalibrated. The method was realised using a Kalman filter that took into account the spectra of the signals involved as well as a fluctuating gyroscope offset. The method was tested for movements of the pelvis, trunk and forearm. Although the problem of integration drift around the global vertical continuously increased in the order of 0.50 degrees s(-1), the inclination estimate was accurate within 3 degrees RMS. It was shown that the gyroscope offset could be estimated continuously during a trial. Using an initial offset error of 1 rad s(-1), after 2 min the off-set error was roughly 5% of the original offset error. Using the Kalman filter described, an accurate and robust system for ambulatory motion recording can be realised.

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In the medical field, accelerometers are often used for measuring inclination of body segments and activity of daily living (ADL) because they are small and require little power. A drawback of using accelerometers is the poor quality of inclination estimate for movements with large accelerations. This paper describes the design and performance of a Kalman filter to estimate inclination from the signals of a triaxial accelerometer. This design is based on assumptions concerning the frequency content of the acceleration of the movement that is measured, the knowledge that the magnitude of the gravity is 1 g and taking into account a fluctuating sensor offset. It is shown that for measuring trunk and pelvis inclination during the functional three-dimensional activity of stacking crates, the inclination error that is made is approximately 20 root-mean square. This is nearly twice as accurate as compared to current methods based on low-pass filtering of accelerometer signals.

Abstract-In this paper we propose a 6DOF tracking system combining Ultra-Wideband measurements with low-cost MEMS inertial measurements. A tightly coupled system is developed which estimates position as well as orientation of the sensorunit while being reliable in case of multipath effects and NLOS conditions. The experimental results show robust and continuous tracking in a realistic indoor positioning scenario.

Fourier transform infrared analysis (FTIR) was used in combination with partial least squares regression (PLS) to predict the concentration of acetone in milk. FTIR spectra were compared with results of a gas-chromatographic head space method. Principal component analysis of whole spectra (3000 to 1000 cm(-1)) suggested to reduce the spectrum of analysis for acetone to 1450 to 1200 cm(-1). A second derivative was applied to the spectra to remove baseline effects and further enhance the spectral features. Full cross-validation was used to compare the reference with predicted acetone concentrations of samples not included in model development. PLS applied to the full spectral range resulted in a complex 19-factor model with a cross-validation error of 0.22 mM. After reducing the spectrum and taking the second derivative, we obtained a model with seven factors that yielded a cross-validation error of 0.21 mM. This compares favorably with a previously reported model with 20 factors and an error of 0.25 mM. Using PLS predictions to identify cows with subclinical ketosis resulted in 95 to 100% sensitivity and 96 to 100% specificity when the threshold for subclinical ketosis was 0.4 to 1.0 mM. The corresponding positive predictive values were > or = 76% and the negative predictive values > 98% throughout an assumed range of subclinical ketosis prevalence of 10 to 30%.

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