Battery Runtime Optimization Toolbox for Wearable Biomedical Sensors - Tobola, Andreas; Leutheuser, Heike; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; Fischer, Georg (13th International Conference on Wearable and Implantable Body Sensor Networks BSN, 2016)
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 ultralow- 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.
elib2cris::1512001600,tobola2016
Keywords: Batteries; Electrocardiography; Runtime; Power demand; Computational modeling; Temperature sensors; Biological system modeling

Data Quality in IoT Temperature Sensor Systems: Demonstrated on Time-Dependent Temperature Fluctuations - Ruhland, Tim; Tobola, Andreas; Scholl, Christoph; Lübke, Maximilian; Franchi, Norman (IEEE Sensors Journal, 2024)
The growing integration of the Internet of Things (IoT) in Industry 4.0 is primarily attributed to its low cost and high adaptability. Our research addresses the expansive application of IoT temperature sensor systems within industrial environments characterized by harsh conditions and rapid temperature fluctuations. Typically, these sensors lack awareness of the data quality they measure and provide. The article mitigates this limitation by introducing data quality models that enhance the accuracy and timeliness of temperature sensor data. These models interpret temperature readings affected by thermal lag and delayed response times. Timeliness is determined using three distinct methods that reflect data volatility, while accuracy is assessed by applying the Savitzky-Golay filter, which provides temperature transients. Both data quality models precede a knowledge-based approach, accumulating necessary information for the sensor system through calibration. These quality categories are combined into a unified Quality of Sensing (QoS) parameter, utilizing fuzzy logic to abstract the quality dimensions, thereby having profound implications for the IoT sensor system. This methodology has been encapsulated within a comprehensive framework and validated using an industrial-grade IoT temperature sensor in a real-world measurement setup. By transcending the conventional reliance on raw sensor outputs, our framework empowers the extraction of intrinsic quality information associated with each temperature reading, enhancing the resilience and reliability of IoT temperature sensor systems.<br

Dynamic Service Switching for the Medical Internet of Things - Kindt, Philipp; Yunge, Daniel; Tobola, Andreas; Fischer, Georg; Chakraborty, Samarjit (IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2016)
elib2cris::1512001600,kindt2016

Sampling rate impact on energy consumption of biomedical signal processing systems - Tobola, Andreas; Streit, Franz-Josef; Espig, Chris; Korpok, Oliver; Leutheuser, Heike; Sauter, Christian; Lang, Nadine; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; et al. (12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.sampli

Scalable ECG Hardware and Algorithms for Extended Runtime of Wearable Sensors - Tobola, Andreas; Espig, Chris; Streit, Franz-Josef; Korpok, Oliver; Leutheuser, Heike; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; Fischer, Georg (2015 IEEE International Symposium on Medical Measurements and Applications, 2015)
Everything in nature tries to reach the lowest possible energy level. Therefore any natural or artificial system must have the ability to adjust itself to the changing requirements of its surrounding environment. In this paper we address this issue by an ECG sensor designed to be adjustable during runtime, having the ability to reduce the power consumption at cost of the informational content. Accessible for everyone, standard ECG hardware and open source software has been used to realize an ECG processing system for wearable applications. The average power consumption has been measured for each mode of operation. Finally we take conclusion to conciser context-aware scaling as key feature to address the energy issue of wearable sensor system
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.scalab

ULPSEK – Ultra-Low-Power Sensor Evaluation Kit - Tobola, Andreas; Rybalko, Ruslan; Korpok, Oliver; Streit, Franz J.; Espig, Chris; Hofmann, Christian; Schmitz, Björn; Struck, Matthias; Weigand, Christian; Fischer, Georg (FOCUS Session "Wearable Sensors", BMT, 2015, 2015)
elib2cris::1512001600,tobola2015b