INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward

OBJECTIVE:In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving. METHODS:Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians. RESULTS:The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and-in the worst case-falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented. CONCLUSIONS:The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems

Let's go below: Potential of Undervolting on Low-Power FPGAs

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Adaptive j-wave detection architecture for online BCG-complex recognition on FPGA

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Demo: BCG measurement by differential sensing in real-time

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Demo: BCG Measurement by differential Sensing in Real-Time

Kulau U, Rust J, Albrecht U-V. Demo: BCG Measurement by differential Sensing in Real-Time. In: 2022 International Conference on Distributed Computing in Sensor Systems (DCOSS). DCOSS 2022. Piscataway, NJ: IEEE; Accepted: 75-78

Performance evaluation of space-grade FPGA architectures

In this paper a comprehensive overview about State-of-The-Art Space-Grade FPGA and SoC-FPGA platforms from AMD, Microchip, NanoXlore and Frontgrade is presented. To this end, a resource-optimized LEON3 soft-core processor system is taken into account as a core design. For evaluation, resource utilization, achievable operating frequency, power consumption as well as radiation performance are taken into account. The results clearly point out the strengths and weaknesses of all space-grade FPGAs selected as well as it helps in the assessment and selection of the optimal candidate for upcoming space applications.PeerReviewe

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study

Pustozerov E, Kulau U, Albrecht U-V. Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study. Bioengineering. 2024;11(6): 596.In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity—reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved

DUO: Integration of dependable undervolting in operating systems

DUO is a co-processor-based energy harvesting solution that integrates the existing undervolting approach IdealVolting into the RIOT operating system. By integrating the existing approach into an operating system, the benefits of undervolting can be used in regular applications. The user can use IdealVolting along with existing applications and take advantage of an active undervolting approach. Especially in energy harvesting applications, that already use an energy harvesting chip with adjustable output voltage, the approach can be easily integrated. Our evaluation compares a duty-cycle sample application within RIOT OS with and without IdealVolting. It shows that DUO can save up to 20 % compared to the plain application only by adding IdealVolting to the application. Our poster highlights important aspects of our implementation and motivates the benefits of a co-processor in such a system

Representation Learning for Sensor-based Device Pairing

The emergence of on-body gadgets has introduced a novel research direction: unobtrusive and continuous device pairing. Existing approaches leveraged contextual information collected by sensors to generate secure communication keys. The secret information is represented throught hand-engineered features. In this paper, we propose a learning method based on Siamese neural networks to extract features that signify on-body context while separating off-body devices.Peer reviewe

A differential BCG sensor system for long term health monitoring experiment on the ISS

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