Scientific Publications "Measurement Technology + Simulation"

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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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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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