In palliative care, effective communication about anticipated death is critical for aligning therapeutic goals, managing family expectations, and ensuring dignified care. However, prognostic uncertainty - particularly regarding the time of death - remains a challenge due to the limited reliability of current methods. This study explores the potential of radar-derived motion biomarkers as a novel approach to distinguish between living and deceased patients, addressing the need for objective decision-support tools in palliative care. Using continuous-wave radar, we recorded the torso displacement (distance signal) of 16 palliative care patients during their dying phase and derived ground-truth annotations from electronic health records (EHR). Machine learning (ML) algorithms processed 5-minute segments of radar-derived motion signals for binary vital status classification. We evaluated the results with balanced accuracy, Gini gain, and SHAP values. Palliative care specialists provided qualitative feedback to ensure clinical relevance. The ML models achieved balanced accuracy of 0.92-0.98 in distinguishing vital states, demonstrating radar technology's potential as an objective monitoring tool. This study is the first to investigate continuous motion biomarkers in end-of-life patients under real-world clinical conditions, capturing physiological changes during this critical phase. Limitations include the challenges in EHR-derived annotation accuracy, as well as the inherent complexity of physiological variability near death. Our findings highlight radar technology's viability for complementary vital status monitoring in palliative care settings. By providing objective data, this approach could reduce prognostic uncertainty while maintaining patient dignity. This work bridges technological innovation with palliative care's humanistic ethos, offering new possibilities for evidence-based end-of-life management.

Random splitting of datasets in image segmentation often leads to unrepresentative test sets, resulting in biased evaluations and poor model generalization. While stratified sampling has proven effective for addressing label distribution imbalance in classification tasks, extending these ideas to segmentation remains challenging due to the multi-label structure and class imbalance typically present in such data. Building on existing stratification concepts, we introduce Iterative Pixel Stratification (IPS), a straightforward, label-aware sampling method tailored for segmentation tasks. Additionally, we present Wasserstein-Driven Evolutionary Stratification (WDES), a novel genetic algorithm designed to minimize the Wasserstein distance, thereby optimizing the similarity of label distributions across dataset splits. We prove that WDES is globally optimal given enough generations. Using newly proposed statistical heterogeneity metrics, we evaluate both methods against random sampling and find that WDES consistently produces more representative splits. Applying WDES across diverse segmentation tasks, including street scenes, medical imaging, and satellite imagery, leads to lower performance variance and improved model evaluation. Our results also highlight the particular value of WDES in handling small, imbalanced, and low-diversity datasets, where conventional splitting strategies are most prone to bias.

Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance. We introduced ULPSEK-Ultra-Low-Power Sensor Evaluation Kit-for evaluation of biomedical sensors and monitoring applications (http://ulpsek.com). ULPSEK includes a multiparameter sensor measuring and processing electrocardiogram, respiration, motion, body temperature, and photoplethysmography. Instead of a battery, ULPSEK is powered using an efficient body heat harvester. The harvester produced 171 W on average, which was sufficient to power the sensor below 25 C ambient temperature. We present design issues regarding the power supply and the power distribution network of the ULPSEK sensor platform. Due to the security aspect of self-powered health sensors, we suggest a hybrid solution consisting of a battery charged by a harvester.

Arrhythmia detection algorithms require the exact and instantaneous detection of fiducial points in the ECG signal. These fiducial points (QRS-complex, P- and T-wave) correspond to distinct cardiac contraction phases. The performance evaluation of different fiducial points detection algorithms require the existence of large databases (DBs) encompassing reference annotations. Up to last year, P- and T-wave annotations were only available for the QT DB. This was addressed by Elgendi et al. who provided P- and T-wave annotations to the MIT-BIH arrhythmia DB. A variety of ECG fiducial points detection algorithms exists in literature, whereas, to the best knowledge of the authors, we could not identify any single-lead algorithm ready for instantaneous P- and T-wave detection. In this work, we present three P- and T-wave detection algorithms: a revised version for QRS detection using line fitting capable to detect P- and T-wave, an expeditious version of a wavelet based ECG delineation algorithm, and a fast naive fiducial points detection algorithm. The fast naive fiducial points detection algorithm performed best on both DBs with sensitivities ranging from 73.0% (P-wave detection, error interval of ± 40 ms) to 89.4% (T-wave detection, error interval of ± 80 ms). As this algorithm detects a wave event in every search window, it has to be investigated how this affects arrhythmia detection algorithms. The reference Matlab implementations are available for download to encourage the development of high-accurate and automated ECG processing algorithms for the integration in daily life using mobile computers.

Battery runtime is a critical concern for practical usage of wearable biomedical sensor systems. A long runtime requires an interdisciplinary low-power knowledge and appropriate design tools. We addressed this issue designing a toolbox in three parts: (1) Modular evaluation kit for development of wearable ultra-low-power biomedical sensors; (2) Miniaturized, wearable, and code compatible sensor system with the same properties as the development kit; (3) Web-based battery runtime calculator for our sensor systems. The purpose of the development kit is optimization of the power consumption. Once optimization is finished, the same embedded software can be transferred to the miniaturized body worn sensor. The web-based application supports development quantifying the effects of use case and design decisions on battery runtime. A sensor developer can select sensor modules, configure sensor parameters, enter use case specific requirements, and select a battery to predict the battery runtime for a specific application. Our concept adds value to development of ultra-low-power biomedical wearable sensors. The concept is effective for professional work and educational purposes.

Athletes and their coaches aim for enhancing the sports performance. Collecting data from athletes, transforming them into useful information related to their sports performance (e.g., their type of gait), and transmitting the information to the coaches supports the enhancement. The types of gait standing, walking, and running were often examined. Lack of research remains for the two types of running, jogging and sprinting. In this work, standing, walking, jogging, and sprinting were classified with a single inertial-magnetic measurement unit that was placed at a novel position at the trunk. A comparison was made between classification systems using different combinations of accelerometer, gyroscope, and magnetometer data as well as different classifiers (Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, Adaptive Boosting). After collecting data from 15 male subjects, the data were preprocessed, features were extracted and selected, and the data were classified. All classification systems were successful. With a mean true positive rate of 95.68% ±1.80%, the classification system using accelerometer and gyroscope data as well as the Naïve Bayes classifier performed best. The classification system can be used for applications in sport and sports performance analysis in particular.

Far too many people are dying from stroke or other heart related diseases each year. Early detection of abnormal heart rhythm could trigger the timely presentation to the emergency department or outpatient unit. Smartphones are an integral part of everyone;s life and they form the ideal basis for mobile monitoring and real-time analysis of signals related to the human heart. In this work, we investigated the performance of arrhythmia classification systems using only features calculated from the time instances of individual heart beats. We built a sinusoidal model using N (N = 10, 15, 20) consecutive RR intervals to predict the (N+1)th RR interval. The integration of the innovative sinusoidal regression feature, together with the amplitude and phase of the proposed sinusoidal model, led to an increase in the mean class-dependent classification accuracies. Best mean class-dependent classification accuracies of 90% were achieved using a Naïve Bayes classifier. Well-performing realtime analysis arrhythmia classification algorithms using only the time instances of individual heart beats could have a tremendous impact in reducing healthcare costs and reducing the high number of deaths related to cardiovascular diseases.

Long battery runtime is one of the most wanted properties of wearable sensor systems. The sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. They all are motivated from one point of view — the signal quality. In this paper we motivate to keep the sampling rate as low as possible. Therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. The Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.

Insufficient physical activity is the 4th leading risk factor for mortality. The physical activity of a person is reflected in the walking behavior. Different methods for the calculation of the accurate step number exists and most of them are evaluated using different walking speeds measured on a treadmill or using a small sample size of overground walking. In this paper, we introduce the BaSA (Basic Step Activities) dataset consisting of four different step activities (walking, jogging, ascending, and descending stairs) that were performed under natural conditions. We further compare two step segmentation algorithms (a simple peak detection algorithm vs. subsequence Dynamic Time Warping (sDTW)). We calculated a multivariate Analysis of Variance (ANOVA) with repeated measures followed by multiple dependent t-tests with Bonferroni correction to test for significant differences in the two algorithms. sDTW performed equally good compared to the peak detection algorithm, but was not considerably better. In further analysis, continuous, real walking signals with transitions from one step activity to the other step activity should be considered to investigate the adaptability of these two step segmentation algorithms.