Robert Bosch Stiftung

The development in automotive industries towards electrification and automation raises special attention to Safety-Related Availability (SaRA) in onboard power supply systems (powernets), where reliable battery diagnosis becomes the crucial enabler. The Safety of the Intended Functionality (SOTIF, ISO 21448) complements traditional Functional Safety (FuSa, ISO 26262) by addressing functional insufficiencies of Advanced Driver Assistance Systems (ADAS). However, applying SOTIF to other use cases, such as the battery diagnosis, remains underexplored.This work presents a SOTIF-oriented probabilistic validation framework that systematically integrates uncertainty quantification (UQ) and reliability analysis (RA) techniques. This framework adopts a Bayesian neural network (BNN) with heteroscedastic noise modeling as the core modeling tools. We jointly quantify epistemic and aleatory uncertainties to guide risk estimation, active test selection, and hazardous scenario discovery. To improve explainability, we applied SHAP-based sensitivity analysis with additional care given to input dependencies.The framework is validated using battery test data generated from a high-fidelity electrochemical battery model, which serves as a representative proxy for real-world batteries. This work aims to provide a practical extension to SOTIF for real-world deployment of complex safety functions and encourage further research in this direction.

The L-test is a performance-based measure to assess balance and mobility. Currently, the primary outcome from this test is the time required to finish it. In this study we present the instrumented L-test (iL-test), an L-test wherein mobility is evaluated by means of a wearable inertial sensor worn at the lower back. We analyzed data from 113 people across seven cohorts: healthy adults, chronic obstructive pulmonary disease, multiple sclerosis, congestive heart failure, Parkinson's disease, proximal femoral fracture, and transfemoral amputation. The iL-test automatic segmentation was validated using stereophotogrammetry. Univariate and multivariate analyses were performed on 164 kinematic features derived from inertial signals to identify distinct patterns across different cohorts. The iL-test accurately recognized and segmented activities during the L-test for all cohorts (technical validity). A random forest classifier revealed that proximal femoral fracture and transfemoral amputation induced significantly different mobility patterns compared to healthy people with AUC values of 0.89 and 0.99, respectively. Strong correlations were found between kinematic features and clinical scores in multiple sclerosis, congestive heart failure, proximal femoral fracture, and transfemoral amputation, with consistent patterns of decreased movement ranges and smoothness with increasing disease severity. Furthermore, features derived from 90° and 180° turns were found to be important contributors to differentiation amongst cohorts, underscoring the need to evaluate different turn degrees and directions. This study emphasizes the iL-test potential to deliver automated mobility assessment across a wide range of clinical conditions, indicating a prospective avenue for improved mobility assessment and, eventually, more informed healthcare interventions.

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G-DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project's vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.

The scientific approval of hypnotherapy for certain mental disorders is still not confirmed. In a randomized-controlled study comparing the efficacy of hypnotherapy (HT) with cognitive behavioral therapy (CBT) for mild to moderate depressive episodes, a non-inferiority of HT compared to CBT could be found. The aim of this study was to examine depressive symptomatology in the long-term course three and a half years after end of the treatment. A total of 152 randomized patients received outpatient individual psychotherapy with 16 to 20 sessions over a period of six months. All were invited to participate in a follow-up three and a half years after the end of treatment where depressive symptoms were assessed via self- and clinician-ratings. In the per-protocol (PP) analysis, only those with available data were included, but a comparison of characteristics was made with individuals without participation in the follow-up survey. An additional intention-to-treat (ITT) analysis was conducted with multiple imputed data for missing data. A total of 71 subjects (46.7%) participated in the follow-up. The noninferiority in the percentage symptom reduction assessed with the clinician-rating of HT compared with CBT was confirmed in the PP and the ITT sample. The symptom improvement in self- and clinician-rating by the end of therapy persisted during the follow-up. Response rates and remission rates for both self- and clinician-ratings are reported. In this study, indications were found that HT was noninferior to CBT in the treatment of depression, even in the long term. Further studies should examine the efficacy of HT in larger multicenter samples and identify predictors of individual treatment success.

BackgroundAlthough digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.MethodsTwenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.ResultsWe identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture).Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.ConclusionsOverall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances.Trial registration ISRCTN – 12246987.