Jahr Name
2023

Modelling and Characterization of an Electro-Thermal MEMS Device for Gas Property Determination

P. Raimann, F. Hedrich, S. Billat, A. Dehé

Smart Systems Integration (SSI), 28.-30.03.2023, Brügge, Belgien

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2023

Characterization and Modeling of Thermal MEMS for Selective Determination of Gas Properties

P. Raimann, F. Hedrich, S. Billat, A. Dehé

Sensor and Measurement Science International (SMSI), 08.-11.05.2023, Nürnberg, Deutschland

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2023

Multisensorische Werkzeuge für die Kaltmassivumformung (Multisensorische Werkzeuge)

K. Grötzinger, A. Schott, B. Ehrbrecht

Abschlussbericht IGF-Vorhaben Nr. 21520 N

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An diesem Projekt haben 3 Institute gearbeitet:
Institut für Umformtechnik (IFU) der Universität Stuttgart, welche den mechanischen Aufbau und die zur Umformung benötigte Presse zur Verfügung gestellt hat. Zudem wurden die zu erwartenden Sensordaten durch Simulationen berechnet.

 

Fraunhofer-Institut für Schicht- und Oberflächentechnik IST, welche das Umformungswerkzeug (Stempel) mit den sensorischen Schichten ausgestattet hat. Im Rahmen des Projektes wurden auch Kraftmessscheiben mit mehreren Sensoren zur Messung der bei der Umformung entstehenden Kräfte entwickelt. Diese sollen insbesondere eine Verkippung des Stempels oder fehlerhafte Rohlinge erkennen.

 

Hahn-Schickard-Gesellschaft für Angewandte Forschung e.V. (HS), welche eine Embedded Elektronik zur Erfassung, Auswertung und Übertragung der Messdaten über eine USB-Schnittstelle bzw. drahtlos per Bluetooth LE entwickelt hat. Eine besondere Herausforderung haben die teilweise sehr hochohmigen Sensoren dargestellt, da deren Signale leicht durch Elektromagnetische Strahlung, wie sie in solch schweren Maschinen üblich sind, gestört werden. Für die Visualisierung der Messdaten wurde eine PC-Anwendung erstellt.

 

Die Langfassung des Abschlussberichtes kann bei der FSV, Goldene Pforte 1, 58093 Hagen, angefordert werden

2023

Analysis of tool heating in cold forging using thin-film sensors

K. C. Grötzinger, A. Schott, M. Rekowski, B. Ehrbrecht, T. Hehn, D. Gerasimov, M. Liewald

International ESAFORM Conference, 19.-21. April 2023, Krakow, Poland

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Data acquisition and data analysis to gain a better process understanding are one of the most promising trends in manufacturing technology. Especially in cold forging processes, data acquisition close to the deformation zone seems challenging due to the high surface pressure. Thus far, process parameters such as die temperature are mainly measured with state-of-the-art sensors, including standard thermocouples, which are integrated into the tooling system. The application of thin-film sensors has been tested in hot forging processes for local die temperature measurement. However, the process conditions regarding tribology and tool load in cold forging are even more difficult. In this contribution, the use of thin-film sensors, applied on a cold forging punch for cup backward extrusion, is subjected. The aim is to investigate the applicability of such thin-film sensors in cold forging with special emphasis on temperature measurement in cyclic forming processes. The thin-film sensor system and its manufacturing procedure by vacuum coating technology combined with microstructuring are described. With these thin-film sensors the cup backward cold extrusion of steel billets was investigated experimentally. Cyclic tool heating simulations with thermal parameter variations were performed as a reference to
experimental results.

 

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2023

Comparison of non-pulsating reflective PPG signals in skin phantom, wearable device prototype, and Monte Carlo simulations

M. Reiser, T. Müller, K. Flock, O. Amft, A. Breidenassel

45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 24. – 27.07.2023, Sydney, Australia

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We obtain and compare the non-pulsating part of reflective Photoplethysmogram (PPG) measurements in a porcine skin phantom and a wearable device prototype with Monte Carlo simulations and analyse the received signal. In particular, we investigate typical PPG wavelengths at 520, 637 and 940 nm and source-detector distances between 1.5 and 8.0 mm. We detail the phantom’s optical parameters, the wearable device design, and the simulation setup. Monte Carlo simulations were using layer-based and voxel-based structures. Pattern of the detected photon weights showed comparable trends. PPG signal, differential pathlength factor (DPF), mean maximum penetration depth, and signal level showed dependencies on the source-detector distance d for all wavelengths.We demonstrate the signal dependence of emitter and detection angles, which is of interest for the development of wearables. 

2023

„EDIH Südwest“ – Beratungs- und Technologieangebote zur digitalen Transformation

S. Spieth

14. InnovationForum Smarte Technologien & Systeme, 15. Juni 2023, Donaueschingen

2023

„EDIH Südwest“ – Beratungs- und Technologieangebote zur digitalen Transformation von Unternehmen und Verwaltung

S. Spieth

microTEC Südwest Clusterkonferenz 2023, 15.-16. Mai 2023, Freiburg

2023

Multi-scale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation

A. Baronetto, L. Graf, S. Fischer, M. Neurath, O. Amft

JMIR Preprints, doi: 10.2196/preprints.51118

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Background:

Abdominal auscultation, i.e. listening to Bowel Sounds (BS), can be used to analyse digestion. An automated retrieval of BS would be beneficial to assess gastro-intestinal disorders non-invasively.

Objective:

To develop a multi-scale spotting model to detect BS in continuous audio data from a wearable monitoring system.

Methods:

We designed a spotting model based on Efficient-U-Net (EffUNet) architecture to analyse 10-second audio segments at a time and spot BS with a temporal resolution of 25 ms. Evaluation data was collected across different digestive phases from 18 healthy participants and 9 patients with Inflammatory Bowel Disease (IBD). Audio data were recorded in a daytime setting with a T-Shirt that embeds digital microphones. The dataset was annotated by independent raters with substantial agreement (Cohen’s κ between 0.70 and 0.75), resulting in 136 h of labelled data. In total, 11482 BS were analysed, with BS duration ranging between 18 ms and 6.3 s. The share of BS in the dataset (BS ratio) was 0.89%. We analysed performance depending on noise level, BS duration, and BS event rate, as well as report spotting timing errors.

Results:

Leave-One-Participant-Out cross-validation of BS event spotting yielded a median F1 score of 0.73 for both, healthy volunteers and patients. EffUNet detected BS in different noise conditions with 0.73 recall and 0.72 precision. In particular, for SNR > 4 dB, more than 83% of BS were recognised, with precision ≥ 0.77. EffUNet recall dropped below 0.60 for BS duration ≥ 1.5 s. At BS ratio > 5%, our model precision was > 0.83. For both healthy participants and patients, insertion and deletion timing errors were the largest, with a total of 15.54 min insertion errors and 13.08 min of deletion errors over the total audio dataset. On our dataset, EffUNet outperform existing BS spotting models that provide similar temporal resolution.

Conclusions:

The EffUNet spotter is robust against background noise and can retrieve BS with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.

 

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2023

Segment-based Spotting of Bowel Sounds using Pretrained Models in Continuous Data Streams

A. Baronetto, L. Graf, S. Fischer, M. Neurath, O. Amft

IEEE Journal of Biomedical and Health Informatics, 3164 - 3174, doi: 10.1109/JBHI.2023.3269910

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We analyse pretrained and non-pretrained deep neural models to detect 10-seconds Bowel Sounds (BS) audio segments in continuous audio data streams. The models include MobileNet, EfficientNet, and Distilled Transformer architectures. Models were initially trained on AudioSet and then transferred and evaluated on 84 hours of labelled audio data of eighteen healthy participants. Evaluation data was recorded in a semi-naturalistic daytime setting including movement and background noise using a smart shirt with embedded microphones. The collected dataset was annotated for individual BS events by two independent raters with substantial agreement (Cohen’s Kappa κ = 0.74). Leave-One-Participant-Out cross-validation for detecting 10-second BS audio segments, i.e. segment-based BS spotting, yielded a best F1 score of 73% and 67%, with and without transfer learning respectively. The best model for segment-based BS spotting was EfficientNet-B2 with an attention module. Our results show that pretrained models could improve F1 score up to 26%, in particular, increasing robustness against background noise. Our segment-based BS spotting approach reduces the amount of audio data to be reviewed by experts from 84 h to 11 h, thus by ∼87%.

 

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2023

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

L. Uhlenberg, A. Derungs, O. Amft

ISSN: 2296-4185

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We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

 

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2022

Simulation framework for reflective PPG signal analysis depending on sensor placement and wavelength

M. Reiser, A. Breidenassel, O. Amft

IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, 27. – 30. September 2022, Ioannina, Griechenland

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We analyse the influence of reflective photoplethysmography (PPG) sensor positioning relative to blood vessels. A voxel based Monte Carlo simulation framework was developed and validated to simulate photon-tissue interactions. An anatomical model comprising a multi-layer skin description with a blood vessel is presented to simulate PPG sensor positioning at the volar wrist. The simulation framework was validated against standard test cases reported in literature. The blood vessel was considered in regular and dilated states. Simulations were performed with 10 8 photon packets and repeated five times for each condition, including wavelength, relative position of PPG sensor and vessel, and vessel dilation state. Statistical weights were associated to photon packets to represent absorption and scattering effects. A symmetrical arrangement of the PPG sensor around the blood vessel showed the maximum AC …

 

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2022

Proximity-based Eating Event Detection in Smart Eyeglasses with Expert and Data Models

A. Saphala, R. Zhang, O. Amft

ACM International Symposium on Wearable Computers, 11. – 15. September 2022, Atlanta & Cambridge, USA

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We compare performances of an expert model-based approach and a data-based baseline for eating event detection using proximity sensor data of smart eyeglasses. Proximity sensors in smart eyeglasses can provide dynamic distance estimates of cyclic temporalis muscle contraction during chewing without skin contact. Our expert model is based on proximity signal preprocessing and two-threshold grid search. In contrast, baseline data models were based on One-class Support-Vector-Machines. We evaluate both models with in-lab and free-living data from 15 participants. Free-living data were obtained across one day of wearing smart eyeglasses with temple-integrated proximity sensors in unconstrained settings. Overall, the retrieval performance F1 score of the two-threshold-based algorithm for free-living data ranged between 0.6 and 0.7, and outperformed all tested SVM model configurations. While SVM …

 

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2022

Non-contact temporalis muscle monitoring to detect eating in free-living using smart eyeglasses

A. Saphala, R. Zhang, T. Nam Thái, O. Amft

IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, 27. – 30. September 2022, Ioannina, Griechenland

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We investigate non-contact sensing of temporalis muscle contraction in smart eyeglasses frames to detect eating activity. Our approach is based on infra-red proximity sensors that were integrated into sleek eyeglasses frame temples. The proximity sensors capture distance variations between frame temple and skin at the frontal, hair-free section of the temporal head region. To analyse distance variations during chewing and other activities, we initially perform an in-lab study, where proximity signals and Electromyography (EMG) readings were simultaneously recorded while eating foods with varying texture and hardness. Subsequently, we performed a free-living study with 15 participants wearing integrated, fully functional 3Dprinted eyeglasses frames, including proximity sensors, processing, storage, and battery, for an average recording duration of 8.3hours per participant. We propose a new chewing sequence …

 

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2022

Feasibility And Acceptability Of Wearable Sensor Placement For Young Children.: 744

E. A Willis, D. Hales, F. Smith, R. Burney, M. C Rzepka, O. Amft, R. Barr, K. R Evenson, M. R Kosorok, D. S Ward

Proceedings of the: Feasibility And Acceptability Of Wearable Sensor Placement For Young Children.: 744

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PURPOSE: To examine parent perceptions of young children’s acceptability of different methods for wearable sensor placement and the feasibility of a free-living 3 to 7-day wear protocol.
METHODS: This study was conducted in three phases. During phase 1, parents of 3 to 8-year-old children (n= 105) and child care providers (n= 56) completed an online survey to rate aspects of fitting and likelihood of wear for 7 methods (headband, eyeglasses, skin adhesive patch, shirt clip/badge, mask, necklace, vest). During phase 2, parent/child (3-8 years old) dyads (n= 30) were asked to wear one of the top 5 prototypes of each wearable for three days (n= 6 children per method; no active sensor). During phase 3, parent/child (3-8 years old) dyads (n= 22) were recruited to wear prototypes of the top 3 wearables (from phase 2; n=~ 7 children per method; no active sensor) for 7 days. In phases 2 and 3, parents completed wear …

 

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2022

The Quest towards Automated Dietary Monitoring & Intervention in Free-living

O. Amft

International Workshop on Multimedia Assisted Dietary Management, 10. Oktober 2022, Lisbon, Portugal

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In the first part of this talk, I will review the hunt for sensors that started of the field of automated dietary monitoring (ADM) and continues to play a role in shaping current research. Moreover, I will describe the eyeglasses-based sensors that we currently develop and their perspectives for free-living monitoring. Moving on, in part two, I will discuss digital twin-based co-simulation as a novel system design approach for wearable devices and their relevance for supporting machine learning algorithm development. Finally, in part three, I will extend the scope into technology-based dietary intervention, i.e., how ADM can support users in their daily life when targeting a diet change or body weight reduction. I will show examples from our work to create digital twins that model individual behavior, identify behavior changes, and interaction strategies to integrate in everyday life.

 

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2022

IMUAngle: Joint Angle Estimation with Inertial Sensors in Daily Activities

L. Uhlenberg, S. Hassan Gangaraju, O. Amft

ACM International Symposium on Wearable Computers, 11.09. – 15.09.2022, Atlanta & Cambridge, USA

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We present a framework for IMU-based joint angle estimation during activities of daily living (ADL). Personalised musculoskeletal models were created from anthropometric data. Three sensor fusion algorithms were optimised to estimate orientation from IMU data and used as input for the simulation framework. Four ADLs, involving upper and lower limbs were simulated. Joint kinematics of IMU-based simulations were compared to optical marker-based simulations. Results for IMU-based simulations showed median RMSE of 0.8 − 15.5 ° for lower limbs and 1.5 − 33.9 ° for upper limbs. Median RMSE were 4.4 °, 5.8 °, 6.9 °, 6.5 ° for ankle plantarflexion, knee-, hip flexion, and hip rotation, respectively. For upper limbs, elbow flexion showed best median RMSE  ∼ 3.7 °, whereas elevation angles (∼ 24.5 °) and shoulder rotation (∼ 12.5 °) performed worst. Increased RMSE at upper limbs was attributed to the degrees of freedom at the shoulder region compared to the hip. Overall, transversal plane movements (rotations) showed higher median RMSE compared to sagittal plane movements  (flexion/extension). Optimisation of orientation estimators improved performance considerably depending on ADL (up to ∼ 20 °). Comparing sensor fusion algorithms, Madgwick and Mahony produced comparable joint kinematics, whereas the Extended Kalman Filter performance showed larger variability depending on the ADL. Our approach offers a realistic representation of joint kinematics and can be supported by optimising parameters of sensor fusion algorithms.

 

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2022

Reflecting on Approaches to Monitor User’s Dietary Intake

J. Keppel, U. Gruenefeld, M. Strauss, L. Ignacio Lopera Gonzalez, O. Amft, S. Schneegass

ACM International Conference on Mobile Human-Computer Interaction, 28.09. – 01.10.2022, Vancouver, Kanada

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Monitoring dietary intake is essential to providing user feedback and achieving a healthier lifestyle. In the past, different approaches for monitoring dietary behavior have been proposed. In this position paper, we first present an overview of the state-of-the-art techniques grouped by image-and sensor-based approaches. After that, we introduce a case study in which we present a Wizard-of-Oz approach as an alternative and non-automatic monitoring method.

 

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2022

Comparison of Surface Models and Skeletal Models for Inertial Sensor Data Synthesis

L. Uhlenberg, O. Amft

IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, 27. – 30. September 2022, Ioannina, Griechenland

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We present a modelling and simulation framework to synthesise body-worn inertial sensor data based on personalised human body surface and biomechanical models. Anthropometric data and reference images were used to create personalised body surface mesh models. The mesh armature was aligned using motion capture reference pose and afterwards mesh and armature were parented. In addition, skeletal models were created using an established musculoskeletal dynamic modelling framework. Four activities of daily living (ADL), including upper and lower limbs were simulated with surface and skeletal models using motion capture data as stimuli. Acceleration and angular velocity data were simulated for 12 body areas of surface models and 8 body areas of skeletal models. We compared simulated inertial sensor data of both models against physical IMU measurements that were obtained simultaneously …

 

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2023

Review on Excess Noise Measurements of Resistors

Daniela Walter, André Bülau and André Zimmermann

MDPI Sensors MDPI Sensors

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Increasing demands for precision electronics require individual components such as resistors
to be specified, as they can be the limiting factor within a circuit. To specify quality and longterm
stability of resistors, noise measurements are a common method. This review briefly explains
the theoretical background, introduces the noise index and provides an insight on how this index
can be compared to other existing parameters.

2022

Untersuchungen zu flüssigkeitsbasierten, kapazitiven Neigungswinkelsensoren

Adrian Schwenck

OPUS - Online Publikationen der Universität Stuttgart