Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. Our proposed toolbox Neuroscope addresses this demand by offering state-of-the-art visualization algorithms for image classification and newly adapted methods for semantic segmentation of convolutional neural nets (CNNs). With its easy to use graphical user interface (GUI), it provides visualization on all layers of a CNN. Due to its open model-view-controller architecture, networks generated and trained with Keras and PyTorch are processable, with an interface allowing extension to additional frameworks. We demonstrate the explanation abilities provided by Neuroscope using the example of traffic scene analysis.

Background: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. Objective: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. Methods: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Results: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. Conclusions: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

A robust digital infrastructure, built upon overarching frameworks and software tools, is essential for the ongoing digital transformation in materials science and engineering. This user journey demonstrates the seamless integration of distinct technical solutions for data handling and analysis, enabling (a) the pursuit of a specific scientific question and (b) adherence to FAIR principles. The scientific study selected for this user journey focuses on comparing different measures of the elastic modulus of a typical engineering material. The user journey involves three research groups replicating real-world collaborative research scenarios. Specifically, it integrates existing digital solutions for experimental data management (PASTA-ELN), simulation workflow execution (pyiron), and image processing workflow execution (Chaldene). Within the auxiliary data management workflow, generated data and metadata are systematically stored in repositories, with metadata aligned to the MatWerk Ontology. Key insights from this user journey include lessons learned from scientists’ perspectives and recommendations for improvement, such as machine-readable experimental protocols, standardized workflow representation, and automated metadata extraction.

Image processing workflows typically consist of a series of different functions, each working with “image” inputs and outputs in an abstract sense. However, the specific in-memory representation of images differs between and sometimes within libraries. Conversion is therefore necessary when integrating functions from several sources into a single program. The conversion process forces users to consider low-level implementation details, including data types, color channels, channel order, minibatch layout, memory locations, and pixel intensity ranges. Specifically in the case of visual programming languages (VPLs), this distracts from high-level operations. We introduce im2im, a Python library that automates the conversion of in-memory image representations. The central concept of this library is a knowledge graph that describes image representations and how to convert between them. The system queries this knowledge graph to generate the conversion code and execute it, converting an image to the desired representation. The effectiveness of the approach is evaluated through two case studies in VPLs. In each case, we compared a workflow created in a basic block-based VPL with the same workflow enhanced using im2im. These evaluations show that im2im automates type conversions and eliminates the need for manual intervention. Additionally, we compared the overhead of using explicit intermediate representations versus im2im, both of which avoid manual type conversions in VPLs. The results indicate that im2im generates only the necessary conversions, avoiding the runtime overhead associated with converting to and from intermediate formats. A performance comparison between the step-by-step approach used by im2im and a single-function approach demonstrates that the overhead introduced by im2im does not impact practical usability. While focused on block-based VPLs, im2im can be generalized to other VPLs and textual programming environments. Its principles are also applicable to domains other than images. The source code and analyses are available via GitHub.

BACKGROUND Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. OBJECTIVE For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. METHODS In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. RESULTS Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. CONCLUSIONS While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.