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 < 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.
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From the publisher: Potentiometric sensors will generally suffer from unwanted responses as a result to changing temperatures by generating an electromotive force. Typically, this voltage drift has a non-linear character and therefore it is difficult to compensate using linear algorithms implemented in the analogue domain. A solution is proposed to improve the sensor characteristics by combining the digitized output of two CO2 rubidium silver iodide sensors with a specially designed digital algorithm to improve the limits of detection (LOD). Experiments show that this method has the capability to improve the LOD of the sensor with a factor 4.5x during temperature variations of 22 °C over a measurement period of 22 h. It enables potentiometric sensors to be used in low power wireless sensor networks for long term air quality control. Furthermore, the influence of depletion of the rubidium silver iodide electrolyte layer can be effectively compensated by determining the decay of the active layer according to the Nernst equation. Knowing the function of depletion over time helps to correct the sensor output and thereby improves the accuracy of the sensor.
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This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S3R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.
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Robots need sensors to operate properly. Using a single image sensor, various aspects of a robot operating in its environment can be measured or monitored. Over the past few years, image sensors have improved a lot: frame rate and resolution have increased, while prices have fallen. As a result, data output has increased and in a number of applications data transfer to a processing unit has become the limiting factor for performance. Local processing in the sensor is one way of reducing data transfer. A report on the Vision in Robotics and Mechatronics project
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In face of climate change and urbanization, the need for thermally comfortable outdoor urban spaces is increasing. In the design of the thermally comfortable urban spaces and decision making about interventions that enhance thermal comfort, scientists and professionals that work for cities use meteorological measurements and models. These measurements can be done by professional and accurate meteorological sensors, but also by simpler mobile instruments such as the easy-to-use Kestrel weather meters. In using these simple type of sensors, it is important to know what the performance of these sensors is for outdoor thermal comfort assessments and how they can be used by scientists and professionals in decision making about urban designs that enhance thermal comfort.To answer these questions, we carried out three experiments in the summer of 2020 in Amsterdam, in which we tested the 11 Kestrel 5400 heat stress sensors and assessed the performance of this equipment for thermal comfort studies. We concluded that Kestrel sensors can be used very well for assessing differences in air temperature and PET (Physiological Equivalent Temperature) between outdoor built environments. For both air temperature and PET, the RMSE between the 11 Kestrel sensors was 0.5 °C maximum when measuring the same conditions. However, Kestrel sensors that were placed in the sun without a wind vane mounted to the equipment showed large radiation errors. In this case, temperature differences up to 3.4 °C were observed compared to Kestrels that were shaded. The effect of a higher air temperature on the PET calculation is, however, surprisingly small. A sensitivity analysis showed that an increase of 3 °C in the air temperature results in a maximal PET reduction of 0.5 °C. We concluded that Kestrel sensors can very well be used for assessing differences between air temperatures and PET between two locations and assessing the thermal effects of urban designs, but care should be taken when air temperature measurements are carried out in the sun. We always recommend using the wind vanes to deviate from high radiant input orientations for the temperature sensor, and placing the stations next to each other at the beginning and at the end of the measurements to check whether the stations actually measure the same values. Any differences can be corrected afterwards.
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From the article: "A facile approach for the fabrication of large-scale interdigitated nanogap electrodes (nanogap IDEs) with a controllable gap was demonstrated with conventional micro-fabrication technology to develop chemocapacitors for gas sensing applications. In this work, interdigitated nanogap electrodes (nanogap IDEs) with gaps from 50–250 nm have been designed and processed at full wafer-scale. These nanogap IDEs were then coated with poly(4-vinyl phenol) as a sensitive layer to form gas sensors for acetone detection at low concentrations. These acetone sensors showed excellent sensing performance with a dynamic range from 1000 ppm to 10 ppm of acetone at room temperature and the observed results are compared with conventional interdigitated microelectrodes according to our previous work. Sensitivity and reproducibility of devices are discussed in detail. Our approach of fabrication of nanogap IDEs together with a simple coating method to apply the sensing layer opens up possibilities to create various nanogap devices in a cost-effective manner for gas sensing applications"
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We present a method for measuring gait velocity using data from an existing ambient sensor network. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations our method imposes no constraints on the elderly. We studied different probabilistic models for the description of the sensor patterns. Experiments are carried out on 15 months of data and include repeated assessments from an occupational therapist. We showed that the measured gait velocities correlate with these assessments.
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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 < 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.
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