Sensors

Bustamante-Garrido, Alejandro;Izquierdo, Mikel;Miarka, Bianca;Cuartero-Navarrete, Ariel;Pérez-Contreras, Jorge;Aedo-Muñoz, Esteban;Cerda-Kohler, Hugo

doi: 10.3390/s23187663pmid: 37765720

Profile determination in field hockey is critical to determining athletes’ physical strengths and weaknesses, and is key in planning, programming, and monitoring training. This study pursued two primary objectives: (i) to provide descriptive data on sprinting, deceleration, and change of direction (COD) abilities and (ii) to elucidate the mechanical variables that influence sprint and COD performance in elite female field hockey players. Using radar and time-gate technology, we assessed performance and mechanical data from 30 m sprinting, deceleration, and COD tests for 26 elite female hockey players. A machine learning approach identified mechanical variables related to sprint and COD performance. Our findings offer a framework for athlete categorization and the design of performance-enhancing training strategies at the international level. Two pivotal mechanical variables—relative maximum horizontal force (F0) and maximum velocity (Vmax)—predominantly influence the times across all tested distances. However, the force–velocity profile (FVP) and horizontal deceleration do not influence the variance in the COD test outcomes. These insights can guide the design, adjustment, and monitoring of training programs, assisting coaches in decision making to optimize performance and mitigate injury risks for female hockey players.

Li, Ke;Huang, Chen;Liang, Jiaping;Zou, Yanbin;Xu, Biao;Yao, Yao;Zhang, Yang;Liu, Dandan

doi: 10.3390/s23187664pmid: 37765722

High-rise building fires pose a serious threat to the lives and property safety of people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescuers. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of the autonomous optimal deployment of base stations in high-rise building fire environments based on a UAV group. A novel problem formulation is proposed that solves the non-line-of-sight (NLOS) positioning problem in complex and unknown environments. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environments. The NLOS positioning problem in the fire field environment is turned into the line-of-sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby at any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations’ deployment goals and to avoid collision. Finally, the positioning accuracy of our method and that of the common method were compared under many different cases. The simulation result showed that the positioning error of a simulated firefighter in the fire field environment was improved from more than 10 m (the positioning error of the traditional method) to less than 1 m.

Arasteh, Emad;Veldhoen, Esther S.;Long, Xi;van Poppel, Maartje;van der Linden, Marjolein;Alderliesten, Thomas;Nijman, Joppe;de Goederen, Robbin;Dudink, Jeroen

doi: 10.3390/s23187665pmid: 37765721

Unobtrusive monitoring of children’s heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children’s RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson’s correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.

Gao, Lei;Cai, Chenzhi;Li, Chao;Mak, Cheuk Ming

doi: 10.3390/s23187666pmid: 37765723

Environmental vibration pollution has serious negative impacts on human health. Among the various contributors to environmental vibration pollution in urban areas, rail transit vibration stands out as a significant source. Consequently, addressing this issue and finding effective measures to attenuate rail transit vibration has become a significant area of concern. An infilled trench can be arranged periodically along the propagation paths of the waves in the soil to attenuate vibration waves in a specific frequency range. However, the periodic infilled trench seems to be unsatisfactory for providing wide band gaps at low and medium frequencies. To improve the isolation performance of wave barriers at low to medium frequencies, a buried PT-WIB consisting of a periodic infilled trench and a wave impedance block barrier has been proposed in this paper. A three-dimensional finite element model has been developed to evaluate the isolation performance of three wave barriers. The influence of the PT-WIB’s parameters on isolation performance has been analyzed. The results indicate that the combined properties of the periodic structure and the wave impedance block barrier can effectively achieve a wide attenuation zone at low and medium frequencies, enhancing the isolation performance for mitigating environmental vibration pollution.

Wei, Suyao;Wu, Zhihui

doi: 10.3390/s23187667pmid: 37765724

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

Ding, Yigao;Li, Zhenwei;Liu, Chengzhi;Kang, Zhe;Sun, Mingguo;Sun, Jiannan;Chen, Long

doi: 10.3390/s23187668pmid: 37765725

Atmospheric drag is an important factor affecting orbit determination and prediction of low-orbit space debris. To obtain accurate ballistic coefficients of space debris, we propose a calculation method based on measured optical angles. Angle measurements of space debris with a perigee height below 1400 km acquired from a photoelectric array were used for orbit determination. Perturbation equations of atmospheric drag were used to calculate the semi-major-axis variation. The ballistic coefficients of space debris were estimated and compared with those published by the North American Aerospace Defense Command in terms of orbit prediction error. The 48 h orbit prediction error of the ballistic coefficients obtained from the proposed method is reduced by 18.65% compared with the published error. Hence, our method seems suitable for calculating space debris ballistic coefficients and supporting related practical applications.

Su, Hsing-Hao;Lu, Chuan-Pin

doi: 10.3390/s23187669pmid: 37765726

Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient’s epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient’s epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients.

Hadj Abdelkader, Oussama;Bouzebiba, Hadjer;Pena, Danilo;Aguiar, António Pedro

doi: 10.3390/s23187670pmid: 37765728

Indoor agriculture is emerging as a promising approach for increasing the efficiency and sustainability of agri-food production processes. It is currently evolving from a small-scale horticultural practice to a large-scale industry as a response to the increasing demand. This led to the appearance of plant factories where agri-food production is automated and continuous and the plant environment is fully controlled. While plant factories improve the productivity and sustainability of the process, they suffer from high energy consumption and the difficulty of providing the ideal environment for plants. As a small step to address these limitations, in this article we propose to use internet of things (IoT) technologies and automatic control algorithms to construct an energy-efficient remote control architecture for grow lights monitoring in indoor farming. The proposed architecture consists of using a master–slave device configuration in which the slave devices are used to control the local light conditions in growth chambers while the master device is used to monitor the plant factory through wireless communication with the slave devices. The devices all together make a 6LoWPAN network in which the RPL protocol is used to manage data transfer. This allows for the precise and centralized control of the growth conditions and the real-time monitoring of plants. The proposed control architecture can be associated with a decision support system to improve yields and quality at low costs. The developed method is evaluated in emulation software (Contiki-NG v4.7),its scalability to the case of large-scale production facilities is tested, and the obtained results are presented and discussed. The proposed approach is promising in dealing with control, cost, and scalability issues and can contribute to making smart indoor agriculture more effective and sustainable.

Fernández-Vázquez, Diego;Calvo-Malón, Gabriela;Molina-Rueda, Francisco;López-González, Raúl;Carratalá-Tejada, María;Navarro-López, Víctor;Miangolarra-Page, Juan Carlos

doi: 10.3390/s23187671pmid: 37765727

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Gait abnormalities, such as altered joint kinematics, are common in people with MS (pwMS). Traditional clinical gait assessments may not detect subtle kinematic alterations, but advances in motion capture technology and analysis methods, such as statistical parametric mapping (SPM), offer more detailed assessments. The aim of this study was to compare the lower-limb joint kinematics during gait between pwMS and healthy controls using SPM analysis. Methods: A cross-sectional study was conducted involving pwMS and healthy controls. A three-dimensional motion capture system was used to obtain the kinematic parameters of the more affected lower limb (MALL) and less affected lower limb (LALL), which were compared using the SPM analysis. Results: The study included 10 pwMS with mild disability (EDSS ≤ 3) and 10 healthy controls. The results showed no differences in spatiotemporal parameters. However, significant differences were observed in the kinematics of the lower-limb joints using SPM. In pwMS, compared to healthy controls, there was a higher anterior pelvis tilt (MALL, p = 0.047), reduced pelvis elevation (MALL, p = 0.024; LALL, p = 0.044), reduced pelvis descent (MALL, p = 0.033; LALL, p = 0.022), reduced hip extension during pre-swing (MALL, p = 0.049), increased hip flexion during terminal swing (MALL, p = 0.046), reduced knee flexion (MALL, p = 0.04; LALL, p < 0.001), and reduced range of motion in ankle plantarflexion (MALL, p = 0.048). Conclusions: pwMS with mild disability exhibit specific kinematic abnormalities during gait. SPM analysis can detect alterations in the kinematic parameters of gait in pwMS with mild disability.

Alshehry, Awwad H.;Alshahry, Saleh M.;Alhazmi, Abdullah K.;Chodavarapu, Vamsy P.

doi: 10.3390/s23187672pmid: 37765729

We describe a study on the effect of temperature variations on multi-channel time-to-digital converters (TDCs). The objective is to study the impact of ambient thermal variations on the performance of field-programmable gate array (FPGA)-based tapped delay line (TDL) TDC systems while simultaneously meeting the requirements of high-precision time measurement, low-cost implementation, small size, and low power consumption. For our study, we chose two devices, Artix-7 and ProASIC3L, manufactured by Xilinx and Microsemi, respectively. The radiation-tolerant ProASIC3L device offers better stability in terms of thermal sensitivity and power consumption compared to the Artix-7. To assess the performance of the TDCs under varying thermal conditions, a laboratory thermal chamber was utilized to maintain ambient temperatures ranging from −75 to 80 °C. This analysis ensured a comprehensive evaluation of the TDCs’ performance across a wide operational range. By utilizing the Artix-7 and ProASIC3L devices, we achieved root mean square (RMS) resolution of 24.7 and 554.59 picoseconds, respectively. Total on-chip power of 0.968 W was achieved using Artix-7, while 1.997 mW of power consumption was achieved using the ProASIC3L device. We worked to determine the temperature sensitivity for both FPGA devices, which could help in the design and optimization of FPGA-based TDCs for many applications.