Motivated by potential applications in cardiac research, we consider the task of reconstructing the dynamics within a spatiotemporal chaotic 3D excitable medium from partial observations at the surface. Three artificial neural network methods (a spatiotemporal convolutional long-short-term-memory, an autoencoder, and a diffusion model based on the U-Net architecture) are trained to predict the dynamics in deeper layers of a cube from observational data at the surface using data generated by the Barkley model on a 3D domain. The results show that despite the high-dimensional chaotic dynamics of this system, such cross-prediction is possible, but non-trivial and as expected, its quality decreases with increasing prediction depth.

Changes in gait are associated with an increased risk of falling and may indicate the presence of movement disorders related to neurological diseases or age-related weakness. Continuous monitoring based on inertial measurement unit (IMU) sensor data can effectively estimate gait parameters that reflect changes in gait dynamics. Monitoring using a waist-level IMU sensor is particularly useful for assessing such data, as it can be conveniently worn as a sensor-integrated belt or observed through a smartphone application. Our work investigates the efficacy of estimating gait events and gait parameters based on data collected from a waist-worn IMU sensor. The results are compared to measurements obtained using a GAITRite^® system as reference. We evaluate two machine learning (ML)-based methods. Both ML methods are structured as sequence to sequence (Seq2Seq). The efficacy of both approaches in accurately determining gait events and parameters is assessed using a dataset comprising 17,643 recorded steps from 69 subjects, who performed a total of 3588 walks, each covering approximately 4 m. Results indicate that the Convolutional Neural Network (CNN)-based algorithm outperforms the long short-term memory (LSTM) method, achieving a detection accuracy of 98.94% for heel strikes (HS) and 98.65% for toe-offs (TO), with a mean error (ME) of 0.09 ± 4.69 cm in estimating step lengths.

Changes in gait are associated with an increased risk of falling and may indicate the presence of movement disorders related to neurological diseases or age-related weakness. Continuous monitoring based on inertial measurement unit (IMU) sensor data can effectively estimate gait parameters that reflect changes in gait dynamics. Monitoring using a waist-level IMU sensor is particularly useful for assessing such data, as it can be conveniently worn as a sensor-integrated belt or through a smartphone application. Our work investigates the efficacy of estimating gait events and gait parameters based on data collected from a waist-worn IMU sensor. Results are compared to measurements obtained using a GAITRite\textsuperscript{\textregistered} system as reference. We evaluate two machine learning (ML) based methods. Both ML methods are structured as sequence-to-sequence models (Seq2Seq). The efficacy of both approaches in accurately determining gait events and parameters is assessed using a dataset consisting of 17,643 recorded steps from 69 subjects, who performed a total of 3,588 walks, each covering approximately 4 meters. Results indicate that the CNN-based algorithm outperforms the LSTM method, achieving a detection accuracy of 98.94% for heel strikes and 98.65% for toe offs, with a mean error of 0.09 ± 4.69 cm in estimating step lengths.

Data anonymization is an essential prerequisite that enables data sharing in a privacy-preserving manner. However, anonymization affects the quality of the data and thus might affect the performance of later conducted data analysis. In this work, we investigate the influence of different image-based subject anonymization methods on the performance of the three common computer vision (CV) tasks keypoint detection, instance segmentation, and face detection. We compare the anonymization methods on how much they affect the performance of the CV tasks, as well as their degree of anonymization. Furthermore, a re-identification attack scenario is applied. The study findings indicate a clear trade-off between increased privacy results in decreased CV performance. Furthermore, DeepPrivacy2 (DP2) shows best results on keeping the performance of face detection high, while leading to a lowrecognition rate. However, the retrieval rate of our attack scenario which measures the re-identification rate remains high for all anonymization methods, which contrasts with respective low level of recognition.

Changes in gait are associated with an increased risk of falling and may indicate the presence of movement disorders in connection with a neurological disease or age-related weakness. Continuous monitoring, based on inertial measurement unit (IMU) sensor data can be used to estimate gait parameters that indicate changes in gait. The monitoring from a waist-level IMU sensor is applicable for the assessment of such data, as it can be easily worn either as specific sensor-integrated belts or being suitable as a smartphone application. However, there is also the challenge that data from a waist-level mounted sensor provides a weaker representation of gait compared to signals from sensors placed closer to the legs and feet as sources of motion. Our work examines how well gait parameters can be estimated from the data of a waist worn IMU sensor. The approach employs the processing of the IMUs triaxial accelerometer and gyroscope data to detect gait events and estimate spatio-temporal gait parameters. In addition to the gait events heel strike (HS) and toe off (TO), it estimates the gait parameters step length, cadence, speed, stance time, swing time, and stride time. The results are compared to data measured by a GAITRite ® system as a reference. The present study investigates and compares the accuracy of two different approaches for gait event detection: a rule-based method and a machine learning (ML) approach based on a sequence-to-sequence convolutional neural network (CNN) architecture. The efficacy of these approaches in accurately determining gait parameters is evaluated using a dataset consisting of 21,193 recorded steps from 70 subjects, who performed 4,590 walks, each with a distance of approximately 4 meters. The ML-based algorithm performs better than the rule-based achieving an accuracy of over 99% in detecting gait events and estimating step lengths with a mean error of-0.12±4.49 cm.

Background: Cervical dystonia (CD) is a movement disorder that is characterized by involuntary muscle contractions causing abnormal head postures. Current assessments rely primarily on infrequent clinical evaluations and subjective patient-reported outcomes (about every 3 to 4 months), limiting detailed understanding of symptom progression between treatments. Objective: Improved understanding of symptom progression would enhance personalized treatment with Botulinum toxin A therapy. For this purpose, we introduce Move2Screen, a smartphone application designed for frequent home-based, asynchronous video monitoring of symptom progression for CD patients. We evaluate the usability and adherence of Move2Screen. Methods: Adherence was assessed in a longitudinal home-based observational study involving eight from initially 11 patients with CD who continued participation after the initial on-site acquisition. Usability was measured using the German mHealth App Usability Questionnaire (G-MAUQ) after the two-week mark, which evaluates ease-of-use, interface satisfaction, and usefulness. Adherence was analyzed by tracking the frequency and timing of recorded videos. Results: The app showed good overall usability scores on the G-MAUQ and a high adherence rate, with participants completing 74% of the intended video recordings. However, usability evaluations highlighted some challenges in perceived usefulness and minor technical issues reported by users. Conclusion: The Move2Screen app demonstrates promising usability and adherence for home-based symptom monitoring in CD. However, addressing technical limitations, expanding participant cohorts, and integrating clinical decision support are essential next steps. These developments may enhance individualized care by enabling more informed therapeutic adjustments and improving long-term disease management.