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In order to evaluate the impact of neuromuscular disorders affecting the upper extremities, the functional use of the arm need to be evaluated during daily activities. A system suitable for measuring arm kinematics should be ambulatory and not interfere with activities of daily living. A measurement system based on miniature accelerometers and gyroscopes is adequate because the sensors are small and do not suffer from line of sight problems. A disadvantage of such sensors is the cumulative drift around the vertical and the problems with aligning the sensor with the segment. A method that uses constraints in the elbow to measure the orientation of the lower arm with respect to the upper arm is described. This requires a calibration method to determine the exact orientation of each of the sensors with respect to the segment. Some preliminary measurements were analyzed and they indicated a strong reduction in orientation error around the vertical. It seemed that the accuracy of the method is limited by the accuracy of the sensor to segment calibration.

This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p < 0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4 degrees (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6 degrees root means square.

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.

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.

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%.

A statistical study of the fragmentation behaviour of 138 model peptides, containing 3-9 amino acid residues (n = 3-9) under high-energy collision conditions is presented. The aim was to identify characteristic patterns of ions in the spectra of peptides which can be translated into general rules to be used in the spectral interpretation and provide a better insight into their fragmentation behaviour. It was found that both number and nature of the amino acids are important factors directing the fragmentation behaviour. The spectra of tri- and tetrapeptides exhibit a comparable probability for the formation of B2- and Y"n-2 ions, whereas larger peptides show a preference for the formation of Bn-1 ions. This generally observed fragmentation pattern of peptides is changed significantly when basic amino acid residues (Arg, Lys and His) and/or Pro are present Arginine appears to have the most pronounced influence on the fragmentation behaviour and overrules that of the other amino acid residues.