@article{58701,
  abstract     = {{<jats:p>Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim of this study was to investigate ML approaches in predicting knee joint loading during sport-specific agility tasks. We explored the possibility of predicting high and low knee abduction moments (KAMs) from kinematic data collected in a laboratory setting through wearable sensors and of predicting the actual KAM from kinematics. Xsens MVN Analyze and Vicon motion analysis, together with Bertec force plates, were used. Talented female football (soccer) players (n = 32, age 14.8 ± 1.0 y, height 167.9 ± 5.1 cm, mass 57.5 ± 8.0 kg) performed unanticipated sidestep cutting movements (number of trials analyzed = 1105). According to the findings of this technical note, classification models that aim to identify the players exhibiting high or low KAM are preferable to the ones that aim to predict the actual peak KAM magnitude. The possibility of classifying high versus low KAMs during agility with good approximation (AUC 0.81–0.85) represents a step towards testing in an ecologically valid environment.</jats:p>}},
  author       = {{Benjaminse, Anne and Nijmeijer, Eline M. and Gokeler, Alli and Di Paolo, Stefano}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  number       = {{11}},
  publisher    = {{MDPI AG}},
  title        = {{{Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I}}},
  doi          = {{10.3390/s24113652}},
  volume       = {{24}},
  year         = {{2024}},
}

@article{48781,
  abstract     = {{In a punch-bending machine, wire products are manufactured for a wide range of industrial sectors, such as the electronics industry. The raw material for this process is flat wire made of high-strength steel. During the manufacturing process of the flat wire, residual stresses and plastic deformations are induced into the wire. These residual stresses and deformations fluctuate over the length of the semi-finished product and have a negative effect on the final product quality. Straightening machines are used to reduce this influence to a minimum. So far, the adjustment of a straightening machine has been performed manually, which is a lengthy and complex task even for an experienced worker. This inevitably leads to the use of inefficient straightening strategies and causes high rejection rates in the entire production process. Due to a lack of sensor information from the straightening operation, application of modern feedback control methods has not been practicable. This paper presents a novel design for a straightening machine with an integrated, precise straightening force measurement. By simultaneously monitoring the position of the straightening rollers, state variables of the straightening operation can be derived. Additionally, a tension control for feeding the flat wire is introduced. This is implemented to mitigate the disturbing effects caused by irregularities in the wire-feeding process. In the results of this article, the high precision of the developed force measurement design and its possible applications are shown.}},
  author       = {{Bathelt, Lukas and Scurk, Maximilian and Djakow, Eugen and Henke, Christian and Trächtler, Ansgar}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  number       = {{22}},
  title        = {{{Novel Straightening-Machine Design with Integrated Force Measurement for Straightening of High-Strength Flat Wire}}},
  doi          = {{10.3390/s23229091}},
  volume       = {{23}},
  year         = {{2023}},
}

@article{45134,
  abstract     = {{<jats:p>The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p &lt; 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.</jats:p>}},
  author       = {{Di Paolo, Stefano and Nijmeijer, Eline M. and Bragonzoni, Laura and Gokeler, Alli and Benjaminse, Anne}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  keywords     = {{Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry}},
  number       = {{4}},
  publisher    = {{MDPI AG}},
  title        = {{{Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach}}},
  doi          = {{10.3390/s23042176}},
  volume       = {{23}},
  year         = {{2023}},
}

@article{45859,
  abstract     = {{<jats:p>Sport-related concussions (SRC) are characterized by impaired autonomic control. Heart rate variability (HRV) offers easily obtainable diagnostic approaches to SRC-associated dysautonomia, but studies investigating HRV during sleep, a crucial time for post-traumatic cerebral regeneration, are relatively sparse. The aim of this study was to assess nocturnal HRV in athletes during their return to sports (RTS) after SRC in their home environment using wireless wrist sensors (E4, Empatica, Milan, Italy) and to explore possible relations with clinical concussion-associated sleep symptoms. Eighteen SRC athletes wore a wrist sensor obtaining photoplethysmographic data at night during RTS as well as one night after full clinical recovery post RTS (&gt;3 weeks). Nocturnal heart rate and parasympathetic activity of HRV (RMSSD) were calculated and compared using the Mann–Whitney U Test to values of eighteen; matched by sex, age, sport, and expertise, control athletes underwent the identical protocol. During RTS, nocturnal RMSSD of SRC athletes (Mdn = 77.74 ms) showed a trend compared to controls (Mdn = 95.68 ms, p = 0.021, r = −0.382, p adjusted using false discovery rate = 0.126) and positively correlated to “drowsiness” (r = 0.523, p = 0.023, p adjusted = 0.046). Post RTS, no differences in RMSSD between groups were detected. The presented findings in nocturnal cardiac parasympathetic activity during nights of RTS in SRC athletes might be a result of concussion, although its relation to recovery still needs to be elucidated. Utilization of wireless sensors and wearable technologies in home-based settings offer a possibility to obtain helpful objective data in the management of SRC.</jats:p>}},
  author       = {{Delling, Anne Carina and Jakobsmeyer, Rasmus and Coenen, Jessica and Christiansen, Nele and Reinsberger, Claus}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  keywords     = {{Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry}},
  number       = {{9}},
  publisher    = {{MDPI AG}},
  title        = {{{Home-Based Measurements of Nocturnal Cardiac Parasympathetic Activity in Athletes during Return to Sport after Sport-Related Concussion}}},
  doi          = {{10.3390/s23094190}},
  volume       = {{23}},
  year         = {{2023}},
}

@article{63230,
  abstract     = {{<jats:p>Quartz crystal microbalance with dissipation monitoring (QCM-D) is a well-established technique for studying soft films. It can provide gravimetric as well as nongravimetric information about a film, such as its thickness and mechanical properties. The interpretation of sets of overtone-normalized frequency shifts, ∆f/n, and overtone-normalized shifts in half-bandwidth, ΔΓ/n, provided by QCM-D relies on a model that, in general, contains five independent parameters that are needed to describe film thickness and frequency-dependent viscoelastic properties. Here, we examine how noise inherent in experimental data affects the determination of these parameters. There are certain conditions where noise prevents the reliable determination of film thickness and the loss tangent. On the other hand, we show that there are conditions where it is possible to determine all five parameters. We relate these conditions to the mathematical properties of the model in terms of simple conceptual diagrams that can help users understand the model’s behavior. Finally, we present new open source software for QCM-D data analysis written in Python, PyQTM.</jats:p>}},
  author       = {{Johannsmann, Diethelm and Langhoff, Arne and Leppin, Christian and Reviakine, Ilya and Maan, Anna M. C.}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  title        = {{{Effect of Noise on Determining Ultrathin-Film Parameters from QCM-D Data with the Viscoelastic Model}}},
  doi          = {{10.3390/s23031348}},
  volume       = {{23}},
  year         = {{2023}},
}

@article{31706,
  author       = {{Brumann, C and Kukuk, M and Reinsberger, Claus}},
  issn         = {{1424-8220}},
  journal      = {{Sensors (Basel)}},
  number       = {{13}},
  pages        = {{4550}},
  title        = {{{Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash. }}},
  volume       = {{21}},
  year         = {{2021}},
}

@article{63236,
  abstract     = {{<jats:p>The response of the quartz crystal microbalance (QCM, also: QCM-D for “QCM with Dissipation monitoring”) to loading with a diverse set of samples is reviewed in a consistent frame. After a brief introduction to the advanced QCMs, the governing equation (the small-load approximation) is derived. Planar films and adsorbates are modeled based on the acoustic multilayer formalism. In liquid environments, viscoelastic spectroscopy and high-frequency rheology are possible, even on layers with a thickness in the monolayer range. For particulate samples, the contact stiffness can be derived. Because the stress at the contact is large, the force is not always proportional to the displacement. Nonlinear effects are observed, leading to a dependence of the resonance frequency and the resonance bandwidth on the amplitude of oscillation. Partial slip, in particular, can be studied in detail. Advanced topics include structured samples and the extension of the small-load approximation to its tensorial version.</jats:p>}},
  author       = {{Johannsmann, Diethelm and Langhoff, Arne and Leppin, Christian}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  number       = {{10}},
  publisher    = {{MDPI AG}},
  title        = {{{Studying Soft Interfaces with Shear Waves: Principles and Applications of the Quartz Crystal Microbalance (QCM)}}},
  doi          = {{10.3390/s21103490}},
  volume       = {{21}},
  year         = {{2021}},
}

@article{17426,
  abstract     = {{<jats:p>The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.</jats:p>}},
  author       = {{Hoffmann, Martin W. and Wildermuth, Stephan and Gitzel, Ralf and Boyaci, Aydin and Gebhardt, Jörg and Kaul, Holger and Amihai, Ido and Forg, Bodo and Suriyah, Michael and Leibfried, Thomas and Stich, Volker and Hicking, Jan and Bremer, Martin and Kaminski, Lars and Beverungen, Daniel and zur Heiden, Philipp and Tornede, Tanja}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  title        = {{{Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions}}},
  doi          = {{10.3390/s20072099}},
  year         = {{2020}},
}

@article{35723,
  abstract     = {{<jats:p>The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.</jats:p>}},
  author       = {{Hoffmann, Martin W. and Wildermuth, Stephan and Gitzel, Ralf and Boyaci, Aydin and Gebhardt, Jörg and Kaul, Holger and Amihai, Ido and Forg, Bodo and Suriyah, Michael and Leibfried, Thomas and Stich, Volker and Hicking, Jan and Bremer, Martin and Kaminski, Lars and Beverungen, Daniel and zur Heiden, Philipp and Tornede, Tanja}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  keywords     = {{Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry}},
  number       = {{7}},
  publisher    = {{MDPI AG}},
  title        = {{{Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions}}},
  doi          = {{10.3390/s20072099}},
  volume       = {{20}},
  year         = {{2020}},
}

@article{63239,
  abstract     = {{<jats:p>A quartz crystal microbalance (QCM) is described, which simultaneously determines resonance frequency and bandwidth on four different overtones. The time resolution is 10 milliseconds. This fast, multi-overtone QCM is based on multi-frequency lockin amplification. Synchronous interrogation of overtones is needed, when the sample changes quickly and when information on the sample is to be extracted from the comparison between overtones. The application example is thermal inkjet-printing. At impact, the resonance frequencies change over a time shorter than 10 milliseconds. There is a further increase in the contact area, evidenced by an increasing common prefactor to the shifts in frequency, Δf, and half-bandwidth, ΔΓ. The ratio ΔΓ/(−Δf), which quantifies the energy dissipated per time and unit area, decreases with time. Often, there is a fast initial decrease, lasting for about 100 milliseconds, followed by a slower decrease, persisting over the entire drying time (a few seconds). Fitting the overtone dependence of Δf(n) and ΔΓ(n) with power laws, one finds power-law exponents of about 1/2, characteristic of semi-infinite Newtonian liquids. The power-law exponents corresponding to Δf(n) slightly increase with time. The decrease of ΔΓ/(−Δf) and the increase of the exponents are explained by evaporation and formation of a solid film at the resonator surface.</jats:p>}},
  author       = {{Leppin, Christian and Hampel, Sven and Meyer, Frederick Sebastian and Langhoff, Arne and Fittschen, Ursula Elisabeth Adriane and Johannsmann, Diethelm}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  number       = {{20}},
  publisher    = {{MDPI AG}},
  title        = {{{A Quartz Crystal Microbalance, Which Tracks Four Overtones in Parallel with a Time Resolution of 10 Milliseconds: Application to Inkjet Printing}}},
  doi          = {{10.3390/s20205915}},
  volume       = {{20}},
  year         = {{2020}},
}

@article{13882,
  author       = {{Schmitt, Martin and Olfert, Sergei and Rautenberg, Jens and Lindner, Gerhard and Henning, Bernd and Reindl, Leonhard}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  pages        = {{2777--2785}},
  title        = {{{Multi Reflection of Lamb Wave Emission in an Acoustic Waveguide Sensor}}},
  doi          = {{10.3390/s130302777}},
  year         = {{2013}},
}

@article{25964,
  abstract     = {{Capacitive sensors are the most commonly used devices for the detection of humidity because they are inexpensive and the detection mechanism is very specific for humidity. However, especially for industrial processes, there is a lack of dielectrics that are stable at high temperature (>200 °C) and under harsh conditions. We present a capacitive sensor based on mesoporous silica as the dielectric in a simple sensor design based on pressed silica pellets. Investigation of the structural stability of the porous silica under simulated operating conditions as well as the influence of the pellet production will be shown. Impedance measurements demonstrate the utility of the sensor at both low (90 °C) and high (up to 210 °C) operating temperatures.}},
  author       = {{Wagner, Thorsten and Krotzky, Sören and Weiß, Alexander and Sauerwald, Tilman and Kohl, Claus-Dieter and Roggenbuck, Jan and Tiemann, Michael}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  pages        = {{3135--3144}},
  title        = {{{A High Temperature Capacitive Humidity Sensor Based on Mesoporous Silica}}},
  doi          = {{10.3390/s110303135}},
  year         = {{2011}},
}

@article{25992,
  abstract     = {{We report on the synthesis and CO gas-sensing properties of mesoporous tin(IV) oxides (SnO2). For the synthesis cetyltrimethylammonium bromide (CTABr) was used as a structure-directing agent; the resulting SnO2 powders were applied as films to commercially available sensor substrates by drop coating. Nitrogen physisorption shows specific surface areas up to 160 m2·g-1 and mean pore diameters of about 4 nm, as verified by TEM. The film conductance was measured in dependence on the CO concentration in humid synthetic air at a constant temperature of 300 °C. The sensors show a high sensitivity at low CO concentrations and turn out to be largely insensitive towards changes in the relative humidity. We compare the materials with commercially available SnO2-based sensors.}},
  author       = {{Wagner, Thorsten and Kohl, Claus-Dieter and Fröba, Michael and Tiemann, Michael}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  pages        = {{318--323}},
  title        = {{{Gas Sensing Properties of Ordered Mesoporous SnO2}}},
  doi          = {{10.3390/s6040318}},
  year         = {{2006}},
}