Search

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure. [ABSTRACT FROM AUTHOR]

Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people's lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machinelearning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% accuracy in real-time on a mobile device. [ABSTRACT FROM AUTHOR]

Oxygen uptake ( V ˙ O 2 ) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate ( H R ) is a prominent physiological indicator correlated with V ˙ O 2 and is often used for indirect V ˙ O 2 prediction. This study investigates the impact of H R placement on V ˙ O 2 prediction accuracy by analyzing H R data combined with the respiratory rate ( R E S P ) and minute ventilation ( V ˙ E ) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V ˙ O 2 , R E S P , V ˙ E , and H R were collected and used to develop a neural network model for V ˙ O 2 prediction. The influence of H R position on prediction accuracy was assessed via Bland–Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that H R combined with R E S P and V ˙ E ( V ˙ O 2 H R + R E S P + V ˙ E ) produces the most accurate V ˙ O 2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V ˙ O 2 prediction decreases, particularly within high-intensity exercise. The substitution of H R with different anatomical sites significantly impacts V ˙ O 2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering H R placement in V ˙ O 2 prediction models, with R E S P and V ˙ E serving as effective compensatory factors. These findings contribute to refining indirect V ˙ O 2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements. [ABSTRACT FROM AUTHOR]

Millions of individuals are living with upper extremity amputations, making them potential beneficiaries of hand and arm prostheses. While myoelectric prostheses have evolved to meet amputees' needs, challenges remain related to their control. This research leverages surface electromyography sensors and machine learning techniques to classify five fundamental hand gestures. By utilizing features extracted from electromyography data, we employed a nonlinear, multiple-kernel learning-based support vector machine classifier for gesture recognition. Our dataset encompassed eight young nondisabled participants. Additionally, our study conducted a comparative analysis of five distinct sensor placement configurations. These configurations capture electromyography data associated with index finger and thumb movements, as well as index finger and ring finger movements. We also compared four different classifiers to determine the most capable one to classify hand gestures. The dual-sensor setup strategically placed to capture thumb and index finger movements was the most effective—this dual-sensor setup achieved 90% accuracy for classifying all five gestures using the support vector machine classifier. Furthermore, the application of multiple-kernel learning within the support vector machine classifier showcases its efficacy, achieving the highest classification accuracy amongst all classifiers. This study showcased the potential of surface electromyography sensors and machine learning in enhancing the control and functionality of myoelectric prostheses for individuals with upper extremity amputations. [ABSTRACT FROM AUTHOR]

In situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning (ML) based algorithm. Received signal strength indicator (RSSI) data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building. The noise factor is also taken into account, and the signal-to-noise ratio (SNR) value is recorded for every RSSI measurement. This study concludes the examination of reference point accuracy with the modified KNN method (MKNN). MKNN was created to more precisely anticipate the position of the reference point. The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity. [ABSTRACT FROM AUTHOR]

Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver's field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver's level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations. [ABSTRACT FROM AUTHOR]

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is an important research area in both machine learning and human–computer interactions. Unfortunately, it remains an extremely difficult process owing to unsolvable issues, such as sensor movement, sensor positioning, crowded background, and inherent diversity in task performances by distinct humans. In this study, we developed an ensemble of classification models for the HAR. The proposed HAR has four working phases—preprocessing, segmentation, feature extraction, and classification. The pre-processing phase includes processes such as frame conversion and contrast enhancement. We developed an improved balanced iterative reducing and clustering utilising hierarchies (BIRCH) algorithm, that provides efficient segmentation by utilizing only minimal resources. These segmented images are subjected to feature extraction, in which grey level co-occurrence matrix (GLCM) features, and improved local gradient threshold pattern (LGTP) features are extracted along with conventional bag of visual words (BoVW) to provide better results. An ensemble classification model with classifiers such as Bi-GRU, CNN, and LSTM was developed in this study to provide an accurate classification. To enhance the performance of the proposed model, we developed a blue monkey standardized aquila optimization (BMSAO) approach. Conventional techniques are contrasted with the proposed framework. The proposed mechanism was found to have higher efficiency in HAR after it was experimentally evaluated. [ABSTRACT FROM AUTHOR]

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices. [ABSTRACT FROM AUTHOR]

The accumulation of fat, oil, and grease (FOG) deposits in sanitary sewer systems is a global issue responsible for billions of dollars in annual maintenance costs, environmental impact, and public health concerns. Laboratory and data-driven studies have explored the physical and chemical characteristics of FOG deposits, formation kinetics, and factors influencing their accumulation in sewer networks, such as pipe sags, pipe age, and pipe material. However, a scalable modeling platform capable of incorporating FOG kinetics and the key variables driving accumulation is not currently available. This study introduces a new platform for modeling the formation and attachment of FOG deposits in sewer systems, the FOG Sewer Waste Management System (FOG-SWMS). FOG-SWMS was utilized to quantify the impact of pipe sags, pipe age and roughness, and pipe material on the accumulation of FOG deposits in sewer lines. Results indicate that under typical sewer conditions, pipe sags and pipe age may increase the accumulation of FOG deposit mass by as much as approximately 23% and 150%, respectively. Surface pH≥8 , as a proxy for cementitious pipe materials, also resulted in more than a 172% increase in FOG deposit mass accumulation. These results confirm data from previous laboratory studies and machine learning algorithms, and also provide a mechanistic explanation of formation processes. Two full-scale sewer network case studies, Study Area 1 (SA1) and Study Area 2 (SA2), were analyzed using FOG-SWMS, to evaluate the predictive capability of the model to identify FOG deposit accumulation zones. FOG-SWMS successfully predicted approximately 85% and 73% of known accumulation zones in SA1 and SA2, respectively. Significant changes in the spatial and temporal distribution of FOG deposits in response to new commercial and residential development also were demonstrated with the model. Fat, oil, and grease deposits are solid masses that form in sanitary sewer lines and create a variety of issues for municipalities. They are primarily the result of discharged food waste residuals into sewers, which reacts with typical wastewater components to form a solid that can block sewer lines and cause overflow of wastewater. These impacts are common, and equate to an incredible amount of time and money spent on sewer repairs and maintenance. This study introduces a new computer-based model that allows researchers to visualize the formation of FOG deposits in sewer line segments. The model is not limited to specialized researchers; it also is intended to be used by engineers and pretreatment coordinators who may be concerned with the accumulation of FOG deposits in sewer line engineering structures. The main goals of this study were (1) to provide the sewer line workforce with a tool for predicting the accumulation of FOG deposits in sewer networks, especially when there are changes in commercial establishments; (2) to serve as a platform for integrating modern sewer inspection data and sensor deployment studies that are becoming more popular; and (3) to incorporate future advancements in machine learning and AI algorithms. [ABSTRACT FROM AUTHOR]

Featured Application: This study introduces an innovative approach for optimizing sensor placement in modal testing by applying machine learning with enhanced efficiency and precision. Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial for accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, and their efficiencies are compared with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures. [ABSTRACT FROM AUTHOR]

In response to the personalized needs of the tourism industry, intelligent tourism planning services should be adopted. Through artificial intelligence and Big data technology, users' travel preferences, time, budget and other information are analyzed and mined in depth to provide users with more suitable travel routes and services. Therefore, according to the demand of tourist path recommendation in cultural scenic spots, this paper proposes a light sensor positioning method based on deep learning algorithm. This paper expounds the advantages of using optical sensor positioning for path recommendation, and describes the specific steps and processes of the optical sensor positioning method based on deep learning algorithm in detail. Finally, the feasibility and effect of this method are verified by experiments and data analysis. The results show that the localization of light sensor based on deep learning algorithm has high accuracy and practicability in the travel path recommendation of cultural scenic spots. Therefore, this method can provide accurate and reasonable path recommendation for tourists in cultural scenic spots and improve tourism experience and benefits. With the continuous development of the tourism market and the changing needs of users, this personalized tourism route planning and recommendation system will be increasingly widely applied and promoted. [ABSTRACT FROM AUTHOR]

This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems. [ABSTRACT FROM AUTHOR]

Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use. [ABSTRACT FROM AUTHOR]

In guided wave (GW)-based structural health monitoring (SHM), ultrasonic elastic waves are used to detect damages in structures by comparing the acquired signals with those acquired before defect formation. Making the SHM system automatic, especially for similar structures, such as turbine blades, is rather challenging. The high sensitivity of GWs to environmental and operational conditions, the variabilities due to sensor positioning, sensor coupling, and material variability in composites limit the baseline application. This work presents a machine learning (ML)-based damage detection method using aggregated baselines independent of their damaged states to enhance the generalization capability of ML algorithms by considering similar structures' variabilities. The methodology relies on feature extraction from raw GW signals and training classification algorithms (e.g., kernel machines, ensemble methods, and neural networks). Two experimental data sets on composite panels are used. The first experimental data set of 45 composite panels is used to validate the approach by considering the aforementioned inter-specimen variabilities. Half of the 45 panels provide pristine data, and the rest provide damaged data so that the same sample is present in the training or test set but never in both. High classification performance is obtained, demonstrating that the classifier has successfully learned to recognize defect signatures despite the influence of the variabilities linked to the multiple instrumented specimens. The second experimental data set of 1 composite panel with temperature variation is used. Good classification performance is obtained without using baseline correction methods. [ABSTRACT FROM AUTHOR]

Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development. [ABSTRACT FROM AUTHOR]

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a 'consensus' approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%. [ABSTRACT FROM AUTHOR]

Pressure sensors embodied in very tiny packages are deployed in a wide range of advanced applications. Examples of applications range from industrial to altitude location services. They are also becoming increasingly pervasive in many other application fields, ranging from industrial to military to consumer. However, the inexpensive manufacturing technology of these sensors is strongly affected by environmental stresses, which ultimately affect their measurement accuracy in the form of variations in gain, hysteresis, and nonlinear responses. Thermal stresses are the main source of sensor behavior deviation. They are particularly insidious because even a few minutes of high temperature exposure can cause measurement drift for many days in the sensor responses. Therefore, conventional calibration techniques are challenged in their adequacy to achieve high accuracy and over the entire deployment life of the sensor. To manage this, several costly and time-consuming calibration procedures have to be performed. Machine learning (ML) techniques are known, supported by the universal approximation theorem, to provide effective data-driven solutions to the above problems. In this context, this paper addresses two case studies, corresponding to post-soldering thermal stresses and exposure to moderately high temperatures, for which two separate datasets have been built and 53 different tiny ML models (collected into a zoo) have been devised and compared. The ML zoo has been constructed with models such as artificial neural networks (ANN), random forest (RFR), and support vector regressors (SVR), able to predict the error introduced by the thermal drift and to compensate for the drift of the measurements. The models in the zoo also satisfy the memory, computational, and accuracy constraints associated with their deployment on resource-constrained embedded devices to be integrated at the edge. Quantitative results achieved by the zoo are reported and discussed, as well as their deployability on tiny micro-controllers. These results reveal the suitability of a tiny ML zoo for the long-term compensation of MEMS pressure sensors affected by drift in their measurements. [ABSTRACT FROM AUTHOR]

To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting of a grid of 3 x 3 force-sensitive resistors (FSR) embedded to read the pressure of the person sitting on it. Additionally, the Body Mass Index (BMI) has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures (backward, front, left, and right-leaning) based on the five machine learning algorithms: ensemble boosted trees, ensemble bagged trees, ensemble subspace K-Nearest Neighbors (KNN), ensemble subspace discriminant, and ensemble RUSBoosted trees. The proposed arrangement is novel as existing works have only provided simulations without practical implementation, whereas we have implemented the proposed design in Simulink. The results validate the proposed sensor placements, and the machine learning (ML) model reaches a maximum accuracy of 99.99%, which considerably outperforms the existing works. The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture. [ABSTRACT FROM AUTHOR]

Thermal analysis of spacecraft systems is a critical process for mission operations. However, knowing the temperature distribution of entire systems is not easy due to the uncertainty of the thermal mathematical model (TMM) and limited temperature sensors. This paper proposes a temperature estimation method using physics-informed machine learning (PIML). The PIML-based thermal analysis allows us to estimate the actual temperature distribution by seamlessly bridging the limited observations and the TMM. To evaluate the estimation accuracy of the proposed method, we conducted a numerical experiment using a pseudo small satellite model consisting of 100 nodes. The proposed method was applied to three different model error cases and was found to improve temperature estimation accuracy in all cases. In addition, the impact of the number of temperature sensors and their placement on estimation accuracy was investigated. • A data-driven thermal analysis that can bridge the uncertain model and limited measurements is proposed. • The physics-informed machine learning was applied to the thermal mathematical model. • A numerical experiment was conducted using a pseudo small satellite model. • The effect of the number and placements of sensors on the estimation accuracy for three model error scenarios was discussed. [ABSTRACT FROM AUTHOR]

Sensor node localization is an important feature of many applications, including wireless sensor networks and location-based services. The accurate localization of sensor nodes improves system performance and reliability. This research emphasizes the benefits of using hybrid machine learning and optimization strategies for sensor node localization. Machine Learning (ML) algorithms, such as neural networks and support vector machines, are used to simulate complex correlations between sensor readings and related locations. These models enable precise prediction of node placements based on received signal strength, time of arrival, or other sensory inputs. The survey conducted in this study aims to uncover the latest advancements in localization strategies within Wireless Sensor Networks through the utilization of ML and Optimization Techniques. By thoroughly examining the existing literature, research gaps have been identified when localization techniques are solely employed. To provide a comprehensive understanding, this survey offers a detailed classification of localization algorithms, covering various aspects. Furthermore, the paper elaborates on the implementation of Optimization and Machine Learning approaches, exploring potential combinations with localization techniques. Through the use of analytical tables, the survey presents a comprehensive overview of sensor node localization using ML and optimized approaches. Additionally, the study addresses the challenges encountered and identifies potential future directions for the integration of these techniques. [ABSTRACT FROM AUTHOR]

Directional sensor nodes deployment is indispensable to a large number of applications including Internet of Things applications. Nowadays, with the recent advances in robotic technology, directional sensor nodes mounted on mobile robots can move toward the appropriate locations. Considering the probabilistic sensing model along with the mobility and motility of directional sensor nodes, area coverage in such a network is more complicated than in a static sensor network. In this paper, we investigate the problem of self-deployment and working direction adjustment in directional sensor networks in order to maximize the covered area. Considering the tradeoff between energy consumption and coverage quality, we formulate this problem as a finite strategic game. Then, we present a distributed payoff-based learning algorithm to achieve Nash equilibrium. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of increasing the area coverage. [ABSTRACT FROM AUTHOR]

In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant analysis. First, zone segmentation of the water distribution network is proposed, aiming to reduce the computational cost that involves all possible combinations of two-leak scenarios. Each zone is composed of at least two consecutive nodes, which means that the number of zones is at most half the number of nodes. With this segmentation, the leak identification task is to locate the zones where the pair of leaks are occurring. To quantify the uncertainty degree, a relaxation node criterion is used. The simulation results evidenced that the outcomes are accurate in most cases by using one-relaxation-node and two-relaxation-node criteria. [ABSTRACT FROM AUTHOR]

Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial for accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, and their efficiencies are compared with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design.

Human activity recognition (HAR) is the process of classifying a person's actions, and it is an essential task for many human‐centered applications. Multi‐instance learning (MIL) is a special case of machine learning where the training examples are bags containing many instances, and a single class label is assigned for an entire bag of instances. In this study, we integrated these two concepts by introducing a novel approach: "human activity recognition based on multi‐instance learning", called HAR‐MIL. Unlike previous studies, the proposed HAR‐MIL method represents human activities differently: as a bag of various wearable sensors (gyroscope, magnetometer, accelerometer, and linear acceleration). HAR‐MIL presents an applicable and flexible model by providing multi‐instance representation and eliminating the restrictions of traditional single‐instance representation. Therefore, the adverse effects of missing data, defective sensors, and biased measurement on activity classification performance were minimized. This study is the first to investigate the performance of two MIL algorithms (SimpleMI and MIWrapper) on HAR. In this study, we explored the effect of four main factors (sensor positions, sensor types, base learners, and single or multiple participants) on the multi‐instance representation of relevant human daily activities. The effectiveness of the proposed HAR‐MIL method was demonstrated on 50 participant‐based and sensor‐position‐based activity recognition datasets. The experimental results showed that HAR‐MIL is effective for wearable sensor‐based HAR with high classification accuracy (99.32%). Furthermore, the results showed that the proposed method outperformed the state‐of‐the‐art methods by 10% on average on the same dataset. [ABSTRACT FROM AUTHOR]

In wireless sensor network projects, it is generally desired to cover the area to be monitored at a given cost and to achieve the maximum useful network lifetime. In the deployment of the wireless sensors, it is necessary to know in advance how many sensor nodes will be required, how much the distance between the nodes should be, etc., or what the transmit power level should be, etc. depending on the channel parameters of the area. This necessitates accurate calculation of variables such as maximum network lifetime, communication channel parameters, number of nodes to be used, and distance between nodes. As numbers reach to the order of hundreds, calculation tends to a NP hard problem to solve. At this point, we employed both single-based and stacked ensemble-based machine learning models to speed up the parameter estimations with highly accurate outcomes. Adaboost was superior over other models (Elastic Net, SVR) in single-based models. Stacked ensemble models achieved best results for the WSN parameter prediction compared to single-based models. [ABSTRACT FROM AUTHOR]

This paper presents applying reinforcement learning to find the optimal sensor/actuator placement (OSAP) policy and optimal control for the flexible wing. The "co-design" objective is to find the OSAP and its associate controller to render the optimal closed-loop performance. The nonlinear vibration dynamics of the flexible wing are modeled in the linear parameter varying (LPV) approach so that LPV- H ∞ controllers can be designed. The co-design problem is formulated into mixed-integer semi-definite programming (MISDP). As a special form of combinatorial optimization, MIDSP solves integer optimization for sensor/actuator selection and convex optimization for controller design. A modified reinforcement learning algorithm is applied to solve this NP-hard optimization problem and obtain a converged solution. In addition, RL is compared with the greedy algorithm and genetic algorithm to demonstrate its strengths and drawbacks in solving high-dimensional MISDP. The solutions obtained by RL and the greedy algorithm are verified and compared in the high-fidelity simulation with the full-order model. [ABSTRACT FROM AUTHOR]

In recent years, human activity recognition (HAR) has received significant interest in industrial and academic research due to the widespread sensor deployments like accelerometers and gyroscopes, in products such as smartphones and smartwatches. HAR is utilized in different applications where valuable information about an individual's functional ability and lifestyle is needed namely human computer interaction (HCI), military, healthcare, surveillance, etc. The recent advances of machine learning (ML) and deep learning (DL) models pave a way for the effective design of HAR under restricted experimental setup and adequate training dataset. Therefore, this study develops a new cuckoo search with deep belief network based HAR and classification, named CSODBN-HAR model. The proposed CSODBN-HAR model mianly classifies six different types of human activities such as standing, sitting, lying, walking, walking upstairs, and walking downstairs. The presented CSODBN-HAR model involves standard scalar approach as a pre-processing step. In addition, the DBN model was utilized for the recognition and classification of human activities. Besides, the CSO algorithm is applied for the hyperparameter tuning of the DBN model so that the recognition rate gets considerably enhanced, shows the novelty of the work. A wide range of experimental result analyses is conducted using benchmark dataset and the outcomes are assessed under different aspects. Extensive comparative results stated the superior outcomes of the CSODBN-HAR model over the current state of art HAR systems. [ABSTRACT FROM AUTHOR]

Accurate measurement of micro-climates that include temperature and relative humidity is the bedrock of the control and management of plant life in protected cultivation systems. Hence, the use of a large number of sensors distributed within the greenhouse or mobile sensors that can be moved from one location to another has been proposed, which are both capital and labor-intensive. On the contrary, accurate measurement of micro-climates can be achieved through the identification of the optimal number of sensors and their optimal locations, whose measurements are representative of the micro-climate in the entire greenhouse. However, given the number of sensors, their optimal locations are proven to vary from time to time as the outdoor weather conditions change. Therefore, regularly shifting the sensors to their optimal locations with the change in outdoor conditions is cost-intensive and may not be appropriate. In this paper, a framework based on the dense neural network (DNN) is proposed to predict the measurements (temperature and humidity) corresponding to the optimal sensor locations, which vary relative to the outdoor weather, using the measurements from sensors whose locations are fixed. The employed framework demonstrates a very high correlation between the true and predicted values with an average coefficient value of 0.91 and 0.85 for both temperature and humidity, respectively. In other words, through a combination of the optimal number of fixed sensors and DNN architecture that performs multi-channel regression, we estimate the micro-climate of the greenhouse. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has attracted widespread attention in areas such as human–computer interaction, work performance management, and healthcare. Owing to advantages such as continuous monitoring, reduced cost of deployment, and ease of privacy protection, wearable-sensor-based HAR is preferred over the traditional approach of using external sensors. In this study, the influence of different combinations of seven body-worn accelerometer positions on the classification of 23 complex daily activities was examined. A conventional machine learning model, namely, RandomForest (RF), and two deep-learning (DL) models, convolutional neural network (CNN)-long short-time memory (LSTM) and CNN transformer, were used to understand the impact of using different models on the classification performance. The results showed a strong correlation between the classification models regarding the combinations of sensor positions and classification performance (F1-score). Additionally, the combination of the four sensors from the left and right wrists, right upper arm, and right thigh was determined to be the best. This study also showed that, owing to feature calculation, the RF model took a longer processing time than the DL-based models and that the CNN-LSTM model would be preferable to RF if plenty of data were available for training it. The results can provide a reference for application designers in choosing appropriate combinations of sensor positions based on requirements for wearability and classification performance. [ABSTRACT FROM AUTHOR]

Soft sensors are attracting much attention from researchers worldwide due to their versatility in practical projects. There are already many applications of soft sensors in aspects of life, consisting of human-robot interfaces, flexible electronics, medical monitoring, and healthcare. However, most of these studies have focused on a specific area, such as fabrication, data analysis, or experimentation. This approach can lead to challenges regarding the reliability, accuracy, or connectivity of the components. Therefore, there is a pressing need to consider the sensor's placement in an overall system and find ways to maximize the efficiency of such flexible sensors. This paper proposes a fabrication method for soft capacitive pressure sensors with spacer fabric, conductive inks, and encapsulation glue. The sensor exhibits a good sensitivity of 0.04 kPa−1, a fast recovery time of 7 milliseconds, and stability of 10,000 cycles. We also evaluate how to connect the sensor to other traditional sensors or hardware components. Some machine learning models are applied to these built-in soft sensors. As expected, the embedded wearables achieve a high accuracy of 96% when recognizing human walking phases. [ABSTRACT FROM AUTHOR]

In the last few decades, structural health monitoring has gained relevance in the context of civil engineering, and much effort has been made to automate the process of data acquisition and analysis through the use of data-driven methods. Currently, the main issues arising in automated monitoring processing regard the establishment of a robust approach that covers all intermediate steps from data acquisition to output production and interpretation. To overcome this limitation, we introduce a dedicated artificial-intelligence-based monitoring approach for the assessment of the health conditions of structures in near-real time. The proposed approach is based on the construction of an unsupervised deep learning algorithm, with the aim of establishing a reliable method of anomaly detection for data acquired from sensors positioned on buildings. After preprocessing, the data are fed into various types of artificial neural network autoencoders, which are trained to produce outputs as close as possible to the inputs. We tested the proposed approach on data generated from an OpenSees numerical model of a railway bridge and data acquired from physical sensors positioned on the Historical Tower of Ravenna (Italy). The results show that the approach actually flags the data produced when damage scenarios are activated in the OpenSees model as coming from a damaged structure. The proposed method is also able to reliably detect anomalous structural behaviors of the tower, preventing critical scenarios. Compared to other state-of-the-art methods for anomaly detection, the proposed approach shows very promising results. [ABSTRACT FROM AUTHOR]

Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of diversity. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors. [ABSTRACT FROM AUTHOR]

Precise ventilation rate estimation of a naturally ventilated livestock building can benefit the control of the indoor environment. Machine learning has become a useful technique in many research fields and might be applied to ventilation rate prediction. This paper developed a machine-learning model for ventilation rate prediction from batch computational fluid dynamics (CFD) simulation results. By comparing deep neural networks (DNN), support vector regression (SVR), and random forest (RF), the best machine learning algorithm was selected. By comparing the modeling scheme of direct single-output (ventilation rate) and indirect multiple-output (predict averaged air velocities normal to the openings, then calculate the ventilation rate), the performances of the machine learning models widely applied in ventilation rate prediction were evaluated. In addition, this paper further evaluated the impact of adding indoor air velocity measurement in ventilation rate prediction. The results showed that the modeling performance of the DNN algorithm (Mean Absolute Percentage Error (MAPE) = 20.1%) was better than those of the SVR (MAPE = 23.2%) and RF algorithm (MAPE = 21.0%). The scheme of multiple-output performed better (MAPE < 8%) than the single-output scheme (MAPE = 20.1%), where MAPE was the mean absolute percentage error. Additionally, the comparison of modeling schemes with different inputs showed that the predictive accuracy could be improved by adding indoor velocities to the inputs. The MAPE decreased from 7.7% in the scheme without indoor velocity to 4.4% in the scheme with one indoor velocity, and 3.1% in the scheme with two indoor velocities. The location of the additional air velocity affected the accuracy of the predictive model, with the ones at the bottom layer performing better in the prediction than those at the top layer. This study enables a real-time and accurate prediction of the ventilation rate of a barn and provides a recommendation for optimal indoor sensor placement. [ABSTRACT FROM AUTHOR]

SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the 'SPHERE House' in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016). [ABSTRACT FROM AUTHOR]

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted. [ABSTRACT FROM AUTHOR]

Soils are a complex ecosystem that provides critical services, such as growing food, supplying antibiotics, filtering wastes, and maintaining biodiversity; hence monitoring soil health and domestication is required for sustainable human development. Low-cost and high-resolution soil monitoring systems are challenging to design and build. Compounded by the sheer size of the monitoring area of interest and the variety of biological, chemical, and physical parameters to monitor, naive approaches to adding or scheduling more sensors will suffer from cost and scalability problems. We investigate a multi-robot sensing system integrated with an active learning-based predictive modeling technique. Taking advantage of advances in machine learning, the predictive model allows us to interpolate and predict soil attributes of interest from the data collected by sensors and soil surveys. The system provides high-resolution prediction when the modeling output is calibrated with static land-based sensors. The active learning modeling technique allows our system to be adaptive in data collection strategy for time-varying data fields, utilizing aerial and land robots for new sensor data. We evaluated our approach using numerical experiments with a soil dataset focusing on heavy metal concentration in a flooded area. The experimental results demonstrate that our algorithms can reduce sensor deployment costs via optimized sensing locations and paths while providing high-fidelity data prediction and interpolation. More importantly, the results verify the adapting behavior of the system to the spatial and temporal variations of soil conditions. [ABSTRACT FROM AUTHOR]

This paper presents a predictive computational framework for surrogate modeling of pressure field and optimization of pressure sensor placement for wind engineering applications. Firstly, a machine learning-derived surrogate model, trained by high-fidelity simulation data using finite element-based CFD and informed by a turbulence model, is developed to construct the full-field pressure from scattered sensor measurements in near real-time. Then, the surrogate pressure model is embedded in another neural network (NN) for optimizing pressure sensor placement. The goal of the NN-based optimizer is to learn the best layout of a fixed number of pressure sensors over the structural surface to deliver the most accurate full-field pressure prediction for various inflow wind conditions. We deploy the model to a representative low-rise building subjected to different wind conditions. The performance of the proposed framework is assessed by comparing the predicted results with finite element-based CFD simulation results. The framework shows excellent accuracy and efficiency, which could be potentially integrated with structural health monitoring to enable digital twins of civil structures. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. We model the problem as a biobjective one, where the aim is to minimise both sensor placement time and uncertainty over target localisation. Both objectives may be important in time‐critical localisation scenarios and our approach allows an operator to choose between different optimal solutions, favouring lower placement time or lower localisation uncertainty as operational circumstances require. We develop a two‐phase algorithm, where an offline multiobjective evolutionary phase finds initial Pareto‐non‐dominated solutions to a static problem and then an online multiobjective reinforcement learning phase finds improved solutions using updated information. We find that the evolutionary algorithm improves significantly on standard grid patterns and that the reinforcement learning algorithm improves further on the evolutionary phase. The number of sonobuoys required may also be reduced. [ABSTRACT FROM AUTHOR]

The rotor attitude detection of spherical motors usually requires the installation of position sensors on the rotor shaft or stator to achieve, which occupy a large space and cause negative effects for the practical installation and application of spherical motors. In this paper, a sensorless position detection method for reluctance spherical motors based on the structure and operating characteristics of reluctance spherical motors is proposed. By detecting the stator coil inductance data at different rotor attitude angles, the position prediction model is constructed using the extreme learning machine algorithm. Through comparison and validation, it is demonstrated that the rotor attitude angle obtained by the method is highly accurate, providing a feasible idea for the control and application of reluctance spherical motors. [ABSTRACT FROM AUTHOR]

The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy. [ABSTRACT FROM AUTHOR]

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called "sensor node deployment density" through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes' occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps. [ABSTRACT FROM AUTHOR]

Industry 5.0, also known as the "smart factory", is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system's operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system. [ABSTRACT FROM AUTHOR]

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities. [ABSTRACT FROM AUTHOR]

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians' physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model's performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions. [ABSTRACT FROM AUTHOR]