Wissenschaftliche Publikationen "Messtechnik + Simulation"

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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In this paper the development of a compact condition classification system for electromagnetic proportional valves is shown. It allows the generation of training data as well as a fast testing and comparison of different trained neuronal networks. By using quantization and pruning, a neuronal network with drastically reduced complexity has been created, so an FPGA implementation was possible. The developed and implemented network shows a very high classification rate and can distinguish 12 different error reasons of the valves. The system requires the measurement of the supply current only, which allows a simple integration of such a false detection circuitry into existing systems. In the future, the system can be modified easily, e.g. to use and test a hardware based AI accelerator instead of the FPGA implementation.

 

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It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.

 

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