MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors.

We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a variety of optical sensors over the past few years, and contains hardware for complete raw data acquisition, georeferencing, real-time data analysis, and immediate visualization on in-car displays. We demonstrate the capabilities of MODISSA by giving a deeper insight into experiments with its specific configuration in the scope of three different applications. Other research groups can benefit from these experiences when setting up their own mobile sensor system, especially regarding the selection of hardware and software, the knowledge of possible sources of error, and the handling of the acquired sensor data.

General Movement Assessment from videos of computed 3D infant body models is equally effective compared to conventional RGB video rating.

BACKGROUND: General Movement Assessment (GMA) is a powerful tool to predict Cerebral Palsy (CP). Yet, GMA requires substantial training challenging its broad implementation in clinical routine. This inspired a world-wide quest for automated GMA. AIMS: To test whether a low-cost, marker-less system for three-dimensional motion capture from RGB depth sequences using a whole body infant model may serve as the basis for automated GMA. STUDY DESIGN: Clinical case study at an academic neurodevelopmental outpatient clinic. SUBJECTS: Twenty-nine high risk infants were assessed at their clinical follow-up at 2-4 month corrected age (CA). Their neurodevelopmental outcome was assessed regularly up to 12-31 months CA. OUTCOME MEASURES: GMA according to Hadders-Algra by a masked GMA-expert of conventional and computed 3D body model ("SMIL motion") videos of the same GMs. Agreement between both GMAs was tested using dichotomous and graded scaling with Kappa and intraclass correlations, respectively. Sensitivity and specificity to predict CP at ≥12 months CA were assessed. RESULTS: Agreement of the two GMA ratings was moderate-good for GM-complexity (κ = 0.58; ICC = 0.874 [95%CI 0.730; 0.941]) and substantial-good for fidgety movements (FMs; Kappa = 0.78, ICC = 0.926 [95%CI 0.843; 0.965]). Five children were diagnosed with CP (four bilateral, one unilateral CP). The GMs of the child with unilateral CP were twice rated as mildly abnormal with FMs. GM-complexity and somewhat less FMs, of both conventional and SMIL motion videos predicted bilateral CP comparably to published literature. CONCLUSIONS: Our computed infant 3D full body model is an attractive starting point for automated GMA in infants at risk of CP.

Self-Calibration for the Time Difference of Arrival Positioning.

The time-difference-of-arrival (TDOA) self-calibration is an important topic for many applications, such as indoor navigation. One of the most common methods is to perform nonlinear optimization. Unfortunately, optimization often gets stuck in a local minimum. Here, we propose a method of dimension lifting by adding an additional variable into the l 2 norm of the objective function. Next to the usual numerical optimization, a partially-analytical method is suggested, which overdetermines the system of equations proportionally to the number of measurements. The effect of dimension lifting on the TDOA self-calibration is verified by experiments with synthetic and real measurements. In both cases, self-calibration is performed for two very common and often combined localization systems, the DecaWave Ultra-Wideband (UWB) and the Abatec Local Position Measurement (LPM) system. The results show that our approach significantly reduces the risk of becoming trapped in a local minimum.

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences.

Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learning a statistical 3D Skinned Multi-Infant Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely moving infants. Quantitative experiments show that SMIL faithfully represents the RGB-D data and properly factorizes the shape and pose of the infants. To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants. SMIL provides a new tool for analyzing infant shape and movement and is a step towards an automated system for GMA.

Unique 4-DOF Relative Pose Estimation with Six Distances for UWB/V-SLAM-Based Devices.

In this work we introduce a relative localization method that estimates the coordinate frame transformation between two devices based on distance measurements. We present a linear algorithm that calculates the relative pose in 2D or 3D with four degrees of freedom (4-DOF). This algorithm needs a minimum of five or six distance measurements, respectively, to estimate the relative pose uniquely. We use the linear algorithm in conjunction with outlier detection algorithms and as a good initial estimate for iterative least squares refinement. The proposed method outperforms other related linear methods in terms of distance measurements needed and in terms of accuracy. In comparison with a related linear algorithm in 2D, we can reduce 10% of the translation error. In contrast to the more general 6-DOF linear algorithm, our 4-DOF method reduces the minimum distances needed from ten to six and the rotation error by a factor of four at the standard deviation of our ultra-wideband (UWB) transponders. When using the same amount of measurements the orientation error and translation error are approximately reduced to a factor of ten. We validate our method with simulations and an experimental setup, where we integrate ultra-wideband (UWB) technology into simultaneous localization and mapping (SLAM)-based devices. The presented relative pose estimation method is intended for use in augmented reality applications for cooperative localization with head-mounted displays. We foresee practical use cases of this method in cooperative SLAM, where map merging is performed in the most proactive manner.

Decawave UWB Clock Drift Correction and Power Self-Calibration.

The position accuracy based on Decawave Ultra-Wideband (UWB) is affected mainly by three factors: hardware delays, clock drift, and signal power. This article discusses the last two factors. The general approach to clock drift correction uses the phase-locked loop (PLL) integrator, which we show is subject to signal power variations, and therefore, is less suitable for clock drift correction. The general approach to the estimation of signal power correction curves requires additional measurement equipment. This article presents a new method for obtaining the curve without additional hardware and clock drift correction without the PLL integrator. Both correction methods were fused together to improve two-way ranging (TWR).

Body pose estimation in depth images for infant motion analysis.

Motion analysis of infants is used for early detection of movement disorders like cerebral palsy. For the development of automated methods, capturing the infant's pose accurately is crucial. Our system for predicting 3D joint positions is based on a recently introduced pixelwise body part classifier using random ferns, to which we propose multiple enhancements. We apply a feature selection step before training random ferns to avoid the inclusion of redundant features. We introduce a kinematic chain reweighting scheme to identify and to correct misclassified pixels, and we achieve rotation invariance by performing PCA on the input depth image. The proposed methods improve pose estimation accuracy by a large margin on multiple recordings of infants. We demonstrate the suitability of the approach for motion analysis by comparing predicted knee angles to ground truth angles.

Ellipsometry and XPS comparative studies of thermal and plasma enhanced atomic layer deposited Al2O3-films.

We report on results on the preparation of thin (<100 nm) aluminum oxide (Al2O3) films on silicon substrates using thermal atomic layer deposition (T-ALD) and plasma enhanced atomic layer deposition (PE-ALD) in the SENTECH SI ALD LL system. The T-ALD Al2O3 layers were deposited at 200 °C, for the PE-ALD films we varied the substrate temperature range between room temperature (rt) and 200 °C. We show data from spectroscopic ellipsometry (thickness, refractive index, growth rate) over 4" wafers and correlate them to X-ray photoelectron spectroscopy (XPS) results. The 200 °C T-ALD and PE-ALD processes yield films with similar refractive indices and with oxygen to aluminum elemental ratios very close to the stoichiometric value of 1.5. However, in both also fragments of the precursor are integrated into the film. The PE-ALD films show an increased growth rate and lower carbon contaminations. Reducing the deposition temperature down to rt leads to a higher content of carbon and CH-species. We also find a decrease of the refractive index and of the oxygen to aluminum elemental ratio as well as an increase of the growth rate whereas the homogeneity of the film growth is not influenced significantly. Initial state energy shifts in all PE-ALD samples are observed which we attribute to a net negative charge within the films.