Wissenschaftliche Publikationen "Sensorik + Aktorik"

The combination of shape memory alloys and micro-electro-mechanical systems possesses to rethink and downscale the size of actuator systems to MEMS level. Thus, the heterogeneous integration of shape memory alloy actuators is a focus of current research to enable the use of standardized semi-finished products such as wires and to exploit their good actuator properties. To integrate the actuator into the mechanical structure, it must first be deformed to fit into the intended installation space and to realize a form-fit connection after the first activation. This pre-deformation has a significant influence on the actuator performance. In this paper, the influence of preforming on the resulting actuator behaviour will be investigated. For this purpose, the change in the transformation temperatures and the resulting stroke are investigated. The experiments carried out were based on experimental plans that were created by means of design of experiments.

 

https://www.vde-verlag.de/proceedings-de/456391023.html

In conventional hydrogen pressure sensors, the stress-sensitive diaphragm is the weakest point in terms of construction. Leading to signal drift of the sensor as hydrogen causes material embrittlement and permeation. We address this issue by using a miniaturized gas pressure sensor without a me-chanical transducer. Our sensor structure consists of a heating element and thermopiles arranged in a thin perforated silicon nitride membrane. In the thermal domain, the structure exhibits a characteristic low-pass behavior whose phase shift is dependent on the hydrogen pressure. A dynamically excited heating element and the evaluation of the thermopile responses are used to measure pressures rang-ing from 100 and 2850 kPa. So far, this is the highest measurement range detected with a thermal pressure sensor that has ever been published in literature. Compared to diaphragm-based MEMS pressure sensors, this chip design offers an intrinsic overload protection as well as low-cost fabrication and simple packaging requirements.

 

https://www.researchgate.net/profile/S-Billat/publication/381631502_73_-_Novel_Miniaturized_Thermoelectric_Hydrogen_Pressure_Sensor/links/66ac8da88f7e1236bc2eda7b/73-Novel-Miniaturized-Thermoelectric-Hydrogen-Pressure-Sensor.pdf

We investigate combined, personalised biomechanical dynamics models and human surface models to synthesise IMU sensor time series data and to improve Human Activity Recognition (HAR) model performance for activities of daily living (ADLs). We analyse two model training scenarios: (1) data fusion of synthetic and measurement IMU data to train HAR models directly, and (2) pretraining HAR models with synthetic and measured IMU data and subsequent transfer learning with public benchmark datasets. Furthermore, we analyse how the synthetic IMU data helps in configurations with scarce measurement data by limiting the number of participants and IMUs in both training scenarios. We evaluate three state-of-the-art HAR models to determine the benefit of our approach. Our results show that the IMU data synthesis approach improves performance across all HAR models in both training scenarios. Depending on the HAR model, synthetic data increased the macroF1 score on average by 8% for configurations with reduced data and by up to 7.5% for transfer learning. In the transfer learning scenario, combining synthetic data with measurement data during pretraining outperformed the results obtained by pretraining with measurement data only, by an average of 5.2% across the public datasets. We conclude that augmenting HAR models with synthetic IMU data provides clear performance improvements for HAR and a versatile approach to accurately reflect human movements

Schlussbericht "Fortschrittliche Glas-Interposer mit Carbon-Kupfer-Verbundmetallisierung" zu IGF-Vorhaben Nr. 337 EBG

We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average < 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.

 

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• This work represents a CMOS-Based nanoporesensing platform for high resolution readout of nanoporeevents.
• CMOS integration reduces the overall capacitance of the readout which reduces the overall noise and thus allows detecting of fast and/or small nanoporeevents.
• The current readout circuit is configurable for different current ranges and bandwidths and optimized for noise suppression. The circuit is divided into four amplifier stages.
• Noise reduction techniques achieve a total integrated noise of only 18 pARMSin a bandwidth of 1 MHz.
• The ASIC was implemented in a 350 nm standard CMOS technology.
• The ASIC consists of five channels. Four of them are used as a MECA.

 

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