bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quadriceps mechanomyography reflects muscle fatigue during electrical stimulus-sustained standing in adults with spinal cord injury – a proof of concept
Authors: Ibitoye Morufu Olusola , Hamzaid Nur Azah , Abdul Wahab Ahmad Khairi , Hasnan Nazirah , Davis Glen M. 
Published: September 2019

AbstractThis study investigates whether mechanomyography (MMG) produced from contracting muscles as a measure of their performance could be a proxy of muscle fatigue during a sustained functional electrical stimulation (FES)-supported standing-to-failure task. Bilateral FES-evoked contractions of quadriceps and glutei muscles, of four adults with motor-complete spinal cord injury (SCI), were used to maintain upright stance using two different FES frequencies: high frequency (HF – 35 Hz) and low frequency (LF – 20 Hz). The time at 30° knee angle reduction was taken as the point of critical “fatigue failure”, while the generated MMG characteristics were used to track the pattern of force development during stance. Quadriceps fatigue, which was primarily responsible for the knee buckle, was characterized using MMG-root mean square (RMS) amplitude. A double exponential decay model fitted the MMG fatigue data with good accuracy [R2 = 0.85–0.99; root mean square error (RMSE) = 2.12–8.10] implying changes in the mechanical activity performance of the muscle’s motor units. Although the standing duration was generally longer for the LF strategy (31–246 s), except in one participant, when compared to the HF strategy, such differences were not significant (p > 0.05) but suggested a faster muscle fatigue onset during HF stimulation. As MMG could discriminate between different stimulation frequencies, we speculate that this signal can quantify muscle fatigue characteristics during prolonged FES applications.
Neighborhood Optimization for Therapy Decision Support
Authors: Gräßer Felix , Malberg Hagen , Zaunseder Sebastian 
Published: September 2019

Abstract
This work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.
Gait analysis for early Parkinson’s disease detection based on deep learning
Authors: Kondragunta Jyothsna , Wiede Christian , Hirtz Gangolf 
Published: September 2019

Abstract
Better handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.
Combination of sensor-embedded and secure server-distributed artificial intelligence for healthcare applications
Authors: Gembaczka Pierre , Heidemann Burkhard , Bennertz Bernhard , Groeting Wolfgang , Norgall Thomas , Seidl Karsten 
Published: September 2019

Abstract
The application of artificial intelligence (AI) in the areas of health, care and social participation offers great opportunities but also involves great challenges. Extensive regulatory, ethical and data-security related requirements exist for data recording, storage and processing of respective personalized and patient-related data. “Artificial Intelligence as a Service” (AIaaS) is pushed for consumer applications by global players, which implies data storage on external database server. However, the available solutions do not meet the requirements. Moreover, small and medium-sized enterprises (SMEs) in the field of healthcare fear the loss of data sovereignty and information outflow. In this paper, we propose a secure and resource-efficient approach by embedding AI directly close to the sensor in combination with secure and distributed data processing on local server or certified “Trusted Data Center”. For this purpose, we have developed the Artificial Intelligence for Embedded Systems (AIfES) platform-independent machine learning library in C programming language. It contains a fully configurable deep artificial neural network with feedforward structure. The library can be run directly on a microcontroller and even allows to train the neural network. Possible healthcare applications include direct (pre-) processing of sensor data, sensor calibration, pattern recognition and classification.
Surface EMG-based Surgical Instrument Classification for Dynamic Activity Recognition in Surgical Workflows
Authors: Bieck Richard , Fuchs Reinhard , Neumuth Thomas 
Published: September 2019

Abstract
We introduce a wearable-based recognition system for the classification of natural hand gestures during dynamic activities with surgical instruments. An armbandbased circular setup of eight EMG-sensors was used to superficially measure the muscle activation signals over the broadest cross-section of the lower arm. Instrument-specific surface EMG (sEMG) data acquisition was performed for 5 distinct instruments. In a first proof-of-concept study, EMG data were analyzed for unique signal courses and features, and in a subsequent classification, both decision tree (DTR) and shallow artificial neural network (ANN) classifiers were trained. For DTR, an ensemble bagging approach reached precision and recall rates of 0.847 and 0.854, respectively. The ANN network architecture was configured to mimic the ensemble-like structure of the DTR and achieved 0.952 and 0.953 precision and recall rates, respectively. In a subsequent multi-user study, classification achieved 70 % precision. Main errors potentially arise for instruments with similar gripping style and performed actions, interindividual variations in the acquisition procedure as well as muscle tone and activation magnitude. Compared to hand-mounted sensor systems, the lower arm setup does not alter the haptic experience or the instrument gripping, which is critical, especially in an intraoperative environment. Currently, drawbacks of the fixed consumer product setup are the limited data sampling rate and the denial of frequency features into the processing pipeline.
Modification of joint prosthesis surfaces by ultrashort pulse laser treatment for improved joint lubrication
Authors: Drescher Philipp , Oldorf Paul , Dreier Tim , Peters Rigo , Seitz Hermann 
Published: September 2019

Abstract
Endoprostheses such as hip replacements are subject to wear. Lubrication of the joint interface plays a key role in the wear process, but the mechanisms of lubrication is challenging to understand. The main issue is the three-body abrasion which leads to a shorter life cycle. In order to improve the life cycle, the surfaces of the articulating components can be modified, for example by pulsed femtosecond-laser microstructuring. By microstructuring of the implant surface, the viscosity of the synovial fluid between the joint can be increased due to the non-Newtonian properties of the synovia. This leads to better lubrication and therefore lower particle abrasion. The objective of this study was to evaluate the impact of different microstructures on the viscosity of a joint fluid substitute. Various microstructures were investigated in a modified rheometer setup featuring a decreased gap size. As a test fluid, a synovial fluid substitute was used. The results show that an increase in the viscosity of the synovial fluid substitute can be achieved by microstructuring. An increase of viscosity of up to 20 % compared to the unstructured reference was observed with ring-structures with a diameter of 100 μm and a depth of 20 μm.
Influence of ECG Lead Reduction Techniques for Extracellular Potassium and Calcium Concentration Estimation
Authors: Hernández Mesa María , Pilia Nicolas , Dössel Olaf , Loewe Axel 
Published: September 2019

Abstract
Chronic kidney disease (CKD) affects 13% of the worldwide population and end stage patients often receive haemodialysis treatment to control the electrolyte concentrations. The cardiovascular death rate increases by 10% - 30% in dialysis patients than in general population. To analyse possible links between electrolyte concentration variation and cardiovascular diseases, a continuous non-invasive monitoring tool enabling the estimation of potassium and calcium concentration from features of the ECG is desired. Although the ECG was shown capable of being used for this purpose, the method still needs improvement. In this study, we examine the influence of lead reduction techniques on the estimation results of serum calcium and potassium concentrations.We used simulated 12 lead ECG signals obtained using an adapted Himeno et al. model. Aiming at a precise estimation of the electrolyte concentrations, we compared the estimation based on standard ECG leads with the estimation using linearly transformed fusion signals. The transformed signals were extracted from two lead reduction techniques: principle component analysis (PCA) and maximum amplitude transformation (Max- Amp). Five features describing the electrolyte changes were calculated from the signals. To reconstruct the ionic concentrations, we applied a first and a third order polynomial regression connecting the calculated features and concentration values. Furthermore, we added 30 dB white Gaussian noise to the ECGs to imitate clinically measured signals. For the noisefree case, the smallest estimation error was achieved with a specific single lead from the standard 12 lead ECG. For example, for a first order polynomial regression, the error was 0.0003±0.0767 mmol/l (mean±standard deviation) for potassium and -0.0036±0.1710 mmol/l for calcium (Wilson lead V1). For the noisy case, the PCA signal showed the best estimation performance with an error of -0.003±0.2005 mmol/l for potassium and -0.0002±0.2040 mmol/l for calcium (both first order fit). Our results show that PCA as ECG lead reduction technique is more robust against noise than MaxAmp and standard ECG leads for ionic concentration reconstruction.
Parameter sensitivity analysis on a mathematical model of reepithelialization
Authors: Dawn Parente Jacquelyn , Möller Knut , Kannan Bala Amala , Hensler Sabine , Kuhlbach Claudia , Mueller Margareta M. 
Published: September 2019

Abstract
Reepithelialization is the single requirement to define a wound as healed when the barrier function of the skin is restored. An existing reepithelialization mathematical model (RM) simulates wound healing in vitro. This work performs a parameter sensitivity analysis on an existing RM to see how robust the model is for changing wound healing rates for application to chronic wounds (inhibition) and wound healing therapies (activation). The existing RM balances the optimal distance between cells and basal membrane segments (BMs) according to the calculation of intercellular pressure and adhesion force. The RM mimics cell behavior and their interaction by passive migration, which is the displacement of cells from its initial position. First, this work reproduces the RM. The initial case recreates the interaction of a cell with its surrounding cells, while the second case recreates the interaction of the cell with its nearest BMs. These two cases were implemented in MATLAB to estimate optimal distance, intercellular pressure, an adhesive force between cells and the BMs. The analysis computes movement vectors and new positions of each cell at different time steps. Parameter sensitivity analysis was then conducted on the adhesion coefficient, where the original value in the RM was unknown. The results obtained at the assumed original parameter values are similar to the existing RM. As a result of the parameter sensitivity analysis, increasing the adhesion coefficient increases cell movement. High basal adhesion causes passive movement of cells, which in the simulation results is seen as a cellular movement towards wound closure. The existing RM is robust to changing adhesion coefficient values which change the rate of the advancing reepithelialization front. Future work includes fitting adhesion coefficient parameter values to an in vitro wounded tissue visualized by live dyes in treatment therapy experiments.
The effect of dipole housing and feeding wires in physical phantoms for EEG
Authors: Machts René , Hunold Alexander , Haueisen Jens 
Published: September 2019

Abstract
Current dipoles are well established models in the localization of neuronal activity to electroencephalography (EEG) data. In physical phantoms, current dipoles can be used as signal sources. Current dipoles are often powered by constant current sources connected via twisted pair wires mostly consisting of copper. The poles are typically formed by platinum wires. These wires as well as the dipole housing might disturb the electric potential distributions in physical phantom measurements. We aimed to quantify this distortion by comparing simulation setups with and without the wires and the housing. The electric potential distributions were simulated using finite element method (FEM). We chose a homogenous volume conductor surrounding the dipoles, which was 100 times larger than the size of the dipoles. We calculated the difference of the electric potential at the surface of the volume conductor between the simulations with and without the connecting wires and the housing. Comparing simulations neglecting all connecting wires and the housing rod to simulations considering them, the electric potential at the surface of the volume conductor differed on average by 2.85 %. Both platinum and twisted pair copper wires had a smaller effect on the electric potentials with a maximum average change of 6.38 ppm. Consequently, source localization of measurements in physical head phantoms should consider these rods in the forward model.
Evaluation of machine learning methods for seizure prediction in epilepsy
Authors: Eberlein Matthias , Müller Jens , Yang Hongliu , Walz Simon , Schreiber Janina , Tetzlaff Ronald , Creutz Susanne , Uckermann Ortrud , Leonhardt Georg 
Published: September 2019

Abstract
Epilepsy affects about 50 million people worldwide of which one third is refractory to medication. An automated and reliable system that warns of impending seizures would greatly improve patient’s quality of life by overcoming the uncertainty and helplessness due to the unpredicted events. Here we present new seizure prediction results including a performance comparison of different methods. The analysis is based on a new set of intracranial EEG data that has been recorded in our working group during presurgical evaluation. We applied two different methods for seizure prediction and evaluated their performance pseudoprospectively. The comparison of this evaluation with common statistical evaluation reveals possible reasons for overly optimistic estimations of the performance of seizure forecasting systems.
Medical robotics simulation framework for application-specific optimal kinematics
Authors: Böttger Sven , Çallar Tolga-Can , Schweikard Achim , Rückert Elmar 
Published: September 2019

Abstract
Most kinematic structures in robot architectures for medical tasks are not optimal. Further, the workspace and payloads are often oversized which results in high product prices that are not suitable for a clinical technology transfer. To investigate optimal kinematic structures and configurations, we have developed an adaptive simulation framework with an associated workflow for requirement analyses, modelling and simulation of specific robot kinematics. The framework is used to build simple and cost effective medical robot designs and was evaluated in a tool manipulation task where medical instruments had to be positioned precisely and oriented on the patient's body. The model quality is measured based on the maximum workspace coverage according to a configurable scoring metric. The metric generalizes among different human body shapes that are based on anthropometric data from UMTRI Human Shape. This dexterity measure is used to analyze different kinematic structures in simulations using the open source simulation tool V-REP. Therefor we developed simulation and visualization procedures for medical tasks based on a patchwork of size-variant anatomical target regions that can be configured and selectively activated in a motion planning controller. In our evaluations we compared the dexterity scores of a commercial lightweight robot arm with 7 joints to optimized kinematic structures with 6, 7 and 8 joints. Compared to the commercial hardware, we achieved improvements of 59% when using an optimized 6- dimensional robot arm, 64% with the 7-dimensional arm and 96% with an 8-dimensional robot arm. Our results show that simpler robot designs can outperform the typically used commercial robot arms in medical applications where the maximum workspace coverage is essential. Our framework provides the basis for a fully automatic optimization tool of the robot parameters that can be applied to a large variety of problems.
Investigation of a non-invasive venous blood flow measurement device
Authors: Stewart Kent , Dangelmaier Simon , Pott Peter , Anders Jens 
Published: September 2019

Abstract
Venous blood circulation can be restricted due to various conditions commonly indicating a related medical condition. However, current non-invasive methods for determining venous blood flow are limited to be either very inaccurate or expensive. Alternatively, a method to measure sap flow non-invasively in trees is through thermal mass measurement principles. This paper investigates applying the thermal mass flow measurement principle to determine venous blood flow. A simplified finite element model (FEM) and simulation are created to determine the operating behavior and expected response of a thermal mass flow meter with venous blood flow under the skin. An initial prototype of a thermal mass venous blood flow meter is designed using a Peltier-element and RTD thermistors. Initial tests were done on N = 8 subjects identifying the presence of blood flow and, testing the devices basic functionality and performance. The simplified FEM model of venous blood flow proved the thermal mass blood flow device is feasible, and determined the initial characteristics of the first prototype. The initial prototype proved to be functional detecting rises in temperature downstream of +1.4 K (0.8 - 1.8 inter- quartile range) when the blood flow was released (t = 90 s after release), compared to when blood was not flowing. The initial prototype proved to be able to detect the presence of blood flow in all subjects. However, further work is required to increase the differences in temperature values or gradient measured for a change in flow rate so the actual flow rate can be determined.
Impact of lung volume changes on perfusion estimates derived by Electrical Impedance Tomography
Authors: Krueger-Ziolek Sabine , Gong Bo , Laufer Bernhard , Moeller Knut 
Published: September 2019

Abstract
Electrical Impedance Tomography (EIT), an imaging technique which operates non-invasively and without radiation exposure, provides information about ventilation- and cardiac-synchronous (pulsatile) changes in the lung. It is well known, that perfusion within the thorax is influenced by lung volume or intrathoracic pressure. In this observational study, it shall be investigated if this phenomenon can be monitored by EIT. Therefore, the impact of the amount of air within the lung on the pulsatile EIT signal was evaluated by carrying out EIT measurements with a spontaneously breathing lung healthy subject holding the breath at three different inspiratory and three various expiratory volume levels during normal tidal breathing. For EIT data analysis, a region of interest was defined by including lung tissue and excluding the heart region. The EIT data revealed, that the shape and the amplitude of the pulsatile EIT signal (evaluated per heartbeat) during the phases of breath holding were dependent on the enclosed lung volume. For lung volumes > 4 L, the amplitude of the pulsatile EIT signal increased with rising inspiratory level and the shape remained almost unchanged. For lung volumes < 4 L, a change in shape was visible but the amplitude remained more or less the same with decreasing expiratory level. Since the results of this observational study show that the pulsatile EIT signal is influenced by the lung volume, it might be used in future to draw conclusions of cardiacpulmonary interactions or intrathoracic pressure states, benefitting the treatment of intensive care patients.
Modeling and Analysis of a Visual Feedback System to Support Efficient Object Grasping of an EMG-Controlled Prosthetic Hand
Authors: Kara Tolgay , Soliman Masri Ahmad 
Published: September 2019

Abstract
Millions of people around the world have lost their upper limbs mainly due to accidents and wars. Recently in the Middle East, the demand for prosthetic limbs has increased dramatically due to ongoing wars in the region. Commercially available prosthetic limbs are expensive while the most economical method available for controlling prosthetic limbs is the Electromyography (EMG). Researchers on EMG-controlled prosthetic limbs are facing several challenges, which include efficiency problems in terms of functionality especially in prosthetic hands. A major issue that needs to be solved is the fact that currently available low-cost EMG-controlled prosthetic hands cannot enable the user to grasp various types of objects in various shapes, and cannot provide the efficient use of the object by deciding the necessary hand gesture. In this paper, a computer vision-based mechanism is proposed with the purpose of detecting and recognizing objects and applying optimal hand gesture through visual feedback. The objects are classified into groups and the optimal hand gesture to grasp and use the targeted object that is most efficient for the user is implemented. A simulation model of the human hand kinematics is developed for simulation tests to reveal the efficacy of the proposed method. 80 different types of objects are detected, recognized, and classified for simulation tests, which can be realized by using two electrodes supplying the input to perform the action. Simulation results reveal the performance of proposed EMG-controlled prosthetic hand in maintaining optimal hand gestures in computer environment. Results are promising to help disabled people handle and use objects more efficiently without higher costs.