Motion analysis is of great interest to a variety of applications, such as virtual and augmented reality and medical diagnostics. Hand movement tracking systems, in particular, are used as a human–machine interface. In most cases, these systems are based on optical or acceleration/angular speed sensors. These technologies are already well researched and used in commercial systems. In special applications, it can be advantageous to use magnetic sensors to supplement an existing system or even replace the existing sensors. The core of a motion tracking system is a localization unit. The relatively complex localization algorithms present a problem in magnetic systems, leading to a relatively large computational complexity. In this paper, a new approach for pose estimation of a kinematic chain is presented. The new algorithm is based on spatially rotating magnetic dipole sources. A spatial feature is extracted from the sensor signal, the dipole direction in which the maximum magnitude value is detected at the sensor. This is introduced as the “maximum vector”. A relationship between this feature, the location vector (pointing from the magnetic source to the sensor position) and the sensor orientation is derived and subsequently exploited. By modelling the hand as a kinematic chain, the posture of the chain can be described in two ways: the knowledge about the magnetic correlations and the structure of the kinematic chain. Both are bundled in an iterative algorithm with very low complexity. The algorithm was implemented in a real-time framework and evaluated in a simulation and first laboratory tests. In tests without movement, it could be shown that there was no significant deviation between the simulated and estimated poses. In tests with periodic movements, an error in the range of 1° was found. Of particular interest here is the required computing power. This was evaluated in terms of the required computing operations and the required computing time. Initial analyses have shown that a computing time of 3 $\mathsf{\mu }$s per joint is required on a personal computer. Lastly, the first laboratory tests basically prove the functionality of the proposed methodology.

The problem of feature selection is a significant one in classification problems, where the addition of too many features to the classification fails to lead to significant increases in classification accuracy. This problem is especially significant within the context of multitemporal remote sensing classifications, where the costs and efforts associated with the acquisition of additional imagery can be extensive. It would thus be beneficial to identify the most important seasons for acquiring imagery for specific land cover types. This study uses a phenologically-adjusted 21 date RapidEye time-series in order to evaluate two methods of feature selection. The two methods compared in this study are a genetic algorithm (GA) and a semi-exhaustive method (EXH), both of which compare permutations of sequential date and band combinations. These methods are employed using a seven class support vector machine classification on a Normalized Difference Vegetation Index (NDVI)-transformed dataset. Overall accuracy (OAA) is used as the performance metric, and OAA significance is assessed using the McNemar test. The results from the feature selection methods are compared on the basis of phenological seasons selected across all iterations and the ideal number of combinations, based on the ratio of better performing classifications to all other classifications. The results suggest that the GA has a moderate but insignificant correlation when compared with the EXH for identifying ideal phenological seasons (overall Spearman’s ρ= 0.60, p = 0.13), but is comparable when considering the number of seasons and image combinations.

Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value. Therefore, we propose a distributed magnetic tracking system based on early-stage demonstrators of novel magnetoelectric (ME) sensors. The system consists of two body-worn magnetic actuators and four ME sensor arrays (body-worn and fixed). It is enabled by a comprehensive signal processing framework with sensor-specific signal enhancement and a gradient descent-based system calibration. As a pilot study, we evaluated the technical feasibility of the described system for motion tracking in general (Scenario A) and for operation during treadmill walking (Scenario B). At distances of up to 60 cm, we achieved a mean absolute distance error of 0.4 cm during gait experiments. Our results show that the modular system is capable of centimeter-level motion tracking of the lower extremities during treadmill walking and should therefore be investigated for clinical gait parameter assessment.