Wissenschaftliche Publikationen "Messtechnik + Simulation"

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

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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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

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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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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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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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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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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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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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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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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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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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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