Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial.

Background: Mental health problems are prevalent among people with diabetes, yet often under-diagnosed. Smart sensing, utilizing passively collected digital markers through digital devices, is an innovative diagnostic approach that can support mental health screening and intervention. However, the acceptance of this technology remains unclear. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aimed to investigate (1) the acceptance of smart sensing in a diabetes sample, (2) the determinants of acceptance, and (3) the effectiveness of an acceptance facilitating intervention (AFI). Methods: A total of N = 132 participants with diabetes were randomized to an intervention group (IG) or a control group (CG). The IG received a video-based AFI on smart sensing and the CG received an educational video on mindfulness. Acceptance and its potential determinants were assessed through an online questionnaire as a single post-measurement. The self-reported behavioral intention, interest in using a smart sensing application and installation of a smart sensing application were assessed as outcomes. The data were analyzed using latent structural equation modeling and t-tests. Results: The acceptance of smart sensing at baseline was average (M = 12.64, SD = 4.24) with 27.8% showing low, 40.3% moderate, and 31.9% high acceptance. Performance expectancy (γ = 0.64, p < 0.001), social influence (γ = 0.23, p = .032) and trust (γ = 0.27, p = .040) were identified as potential determinants of acceptance, explaining 84% of the variance. SEM model fit was acceptable (RMSEA = 0.073, SRMR = 0.059). The intervention did not significantly impact acceptance (γ = 0.25, 95%-CI: -0.16-0.65, p = .233), interest (OR = 0.76, 95% CI: 0.38-1.52, p = .445) or app installation rates (OR = 1.13, 95% CI: 0.47-2.73, p = .777). Discussion: The high variance in acceptance supports a need for acceptance facilitating procedures. The analyzed model supported performance expectancy, social influence, and trust as potential determinants of smart sensing acceptance; perceived benefit was the most influential factor towards acceptance. The AFI was not significant. Future research should further explore factors contributing to smart sensing acceptance and address implementation barriers.

Methodological Selection of Optimal Features for Object Classification Based on Stereovision System.

With the expansion of green energy, more and more data show that wind turbines can pose a significant threat to some endangered bird species. The birds of prey are more frequently exposed to collision risk with the wind turbine blades due to their unique flight path patterns. This paper shows how data from a stereovision system can be used for an efficient classification of detected objects. A method for distinguishing endangered birds from common birds and other flying objects has been developed and tested. The research focused on the selection of a suitable feature extraction methodology. Both motion and visual features are extracted from the Bioseco BPS system and retested using a correlation-based and a wrapper-type approach with genetic algorithms (GAs). With optimal features and fine-tuned classifiers, birds can be distinguished from aeroplanes with a 98.6% recall and 97% accuracy, whereas endangered birds are delimited from common ones with 93.5% recall and 77.2% accuracy.

Rapid, portable and visualizing nitrite detection enabled by a rationally designed meso-aminoindole substituted pyronine-based fluorescent probe.

Nitrite (NO2-) widely exists in our daily diet, and its excessive consumption can lead to detrimental effects on the human central nervous system and an elevated risk of cancer. The fluorescence probe method for the determination of nitrite has developed rapidly due to its simplicity, rapidity and sensitivity. Despite establishing various nitrite sensing platforms to ensure the safety of foods and drinking water, the simultaneous achievement of rapid, specific, affordable, visualizing, and on-site nitrite detection remains challenging. Here, we designed a novel fluorescent probe by using Rhodamine 800 as the fluorescent skeleton and 5-aminoindole as the specific reaction group to solve this problem. The probe shows a maximal fluorescence emission at 602 nm, thereby avoiding background emission interference when applied to food samples. Moreover, this unique probe exhibited excellent sensing capabilities for detecting nitrite. These included: a rapid response time within 3 min, a noticeable color change that the naked eye can observe, a low detection limit of 13.8 nM, and a remarkable selectivity and specificity to nitrite. Besides that, the probe can detect nitrite quantitatively in barreled drinking water, ham sausage, and pickles samples, with good recoveries ranging from 89.0 % to 105.8 %. More importantly, based on the probe fixation and signal processing technology, a portable and smart sensing platform was fabricated and made convenient and rapid analysis the content of NO2- in real samples possible. The results obtained in this work provide a new strategy for the design of high-performance nitrite probes and feasible technology for portable, rapid and visual detection of nitrite, and this probe holds the potential as a practical tool for alleviating concern regarding nitrite levels.

AuNi bimetallic aerogel with ultra-high stability applied in smart and portable biosensing.

Glucose detection is of significant importance in providing information to the human health management. However, conventional enzymatic glucose sensors suffer from a limited long-term stability due to the losing activity of the enzymes. In this work, the AuNi bimetallic aerogel with a well-defined nanowire network is synthesized and applied as the sensing nanomaterial in the non-enzymatic glucose detection. The three-dimensional (3D) hierarchical porous structure of the AuNi bimetallic aerogel ensures the high sensitivity of the sensor (40.34 µA mM-1 cm-2). Theoretical investigation unveiled the mechanism of the boosting electrocatalytic activity of the AuNi bimetallic aerogel toward glucose. A better adhesion between the sensing nanomaterial and the screen-printing electrodes (SPEs) is obtained after the introduction of Ni. On the basis of a wide linearity in the range of 0.1-5 mM, an excellent selectivity, an outstanding long-term stability (90 days) as well as the help of the signal processing circuit and an M5stack development board, the as-prepared glucose sensor successfully realizes remote monitoring of the glucose concentration. We speculate that this work is favorable to motivating the technological innovations of the non-enzymatic glucose sensors and intelligent sensing devices.

Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial.

Objective: Smart sensing has the potential to make psychotherapeutic treatments more effective. It involves the passive analysis and collection of data generated by digital devices. However, acceptance of smart sensing among psychotherapy patients remains unclear. Based on the unified theory of acceptance and use of technology (UTAUT), this study investigated (1) the acceptance toward smart sensing in a sample of psychotherapy patients (2) the effectiveness of an acceptance facilitating intervention (AFI) and (3) the determinants of acceptance. Methods: Patients (N = 116) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a control video. An online questionnaire was used to assess acceptance of smart sensing, performance expectancy, effort expectancy, facilitating conditions and social influence. The intervention effects of the AFI on acceptance were investigated. The determinants of acceptance were analyzed with structural equation modeling (SEM). Results: The IG showed a moderate level of acceptance (M = 3.16, SD = 0.97), while the CG showed a low level (M = 2.76, SD = 1.0). The increase in acceptance showed a moderate effect in the intervention group (p < .05, d = 0.4). For the IG, performance expectancy (M = 3.92, SD = 0.7), effort expectancy (M = 3.90, SD = 0.98) as well as facilitating conditions (M = 3.91, SD = 0.93) achieved high levels. Performance expectancy (γ = 0.63, p < .001) and effort expectancy (γ = 0.36, p < .001) were identified as the core determinants of acceptance explaining 71.1% of its variance. The fit indices supported the model's validity (CFI = .95, TLI = .93, RMSEA = .08). Discussion: The low acceptance in the CG suggests that enhancing the acceptance should be considered, potentially increasing the use and adherence to the technology. The current AFI was effective in doing so and is thus a promising approach. The IG also showed significantly higher performance expectancy and social influence and, in general, a strong expression of the UTAUT factors. The results support the applicability of the UTAUT in the context of smart sensing in a clinical sample, as the included predictors were able to explain a great amount of the variance of acceptance.

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Smart Sensing of Nerve Agents.

The recent international scenario highlights the importance to protect human health and environmental quality from toxic compounds. In this context, organophosphorous (OP) Nerve Agents (NAs) have received particular attention, due to their use in terrorist attacks. Classical instrumental detection techniques are sensitive and selective, but they cannot be used in real field due to the high cost, specialized personnel requested and huge size. For these reasons, the development of practical, easy and fast detection methods (smart methods) is the future of this field. Indeed, starting from initial sensing research, based on optical and/or electrical sensors, today the development and use of smart strategies to detect NAs is the current state of the art. This review summarizes the smart strategies to detect NAs, highlighting some important parameters, such as linearity, limit of detection and selectivity. Furthermore, some critical comments of the future on this field, and in particular, the problems to be solved before a real application of these methods, are provided.

Environmental Chamber Characterization of an Ice Detection Sensor for Aviation Using Graphene and PEDOT:PSS.

In the context of improving aircraft safety, this work focuses on creating and testing a graphene-based ice detection system in an environmental chamber. This research is driven by the need for more accurate and efficient ice detection methods, which are crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors' heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors tested under varied and controlled atmospheric conditions exhibit a remarkable potential to enhance ice detection systems for aircraft. Their lightweight, efficient, and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems.

The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs.

Nanoscale Pickering emulsion food preservative films/coatings: Compositions, preparations, influencing factors, and applications.

Pickering emulsion (PE) technology effectively addresses the issues of poor compatibility and low retention of hydrophobic active ingredients in food packaging. Nonetheless, it is important to recognize that each stage of the preparation process for PE films/coatings (PEFCs) can significantly influence their functional properties. With the fundamental considerations of environmental friendliness and human safety, this review extensively explores the potential of raw materials for PEFC and introduces the preparation methods of nanoparticles, emulsification technology, and film-forming techniques. The critical factors that impact the performance of PEFC during the preparation process are analyzed to enhance food preservation effectiveness. Moreover, the latest advancements in PE packaging across diverse food applications are summarized, along with prospects for innovative food packaging materials. Finally, the preservation mechanism and application safety have been systematically elucidated. The study revealed that the PEFCs provide structural flexibility, where designable nanoparticles offer unique functional properties for intelligent control over active ingredient release. The selection of the dispersed and continuous phases, along with component proportions, can be customized for specific food characteristics and storage conditions. By employing suitable preparation and emulsification techniques, the stability of the emulsion can be improved, thereby enhancing the effectiveness of the films/coatings in preserving food. Including additional substances broadens the functionality of degradable materials. The PE packaging technology provides a safe and innovative solution for extending the shelf life and enhancing the quality of food products by protecting and releasing active components.

Shaped Magnetogel Microparticles for Multispectral Magnetic Resonance Contrast and Sensing.

Multispectral magnetic resonance imaging (MRI) contrast agents are microfabricated three-dimensional magnetic structures that encode nearby water protons with discrete frequencies. The agents have a unique radiofrequency (RF) resonance that can be tuned by engineering the geometric parameters of these microstructures. Multispectral contrast agents can be used as sensors by incorporating a stimulus-driven shape-changing response into their structure. These geometrically encoded magnetic sensors (GEMS) enable MRI-based sensing via environmentally induced changes to their geometry and their corresponding RF resonance. Previously, GEMS have been made using thin-film lithography techniques in a cleanroom environment. While these approaches offer precise control of the microstructure, they can be a limitation for researchers who do not have cleanroom access or microfabrication expertise. Here, an alternative approach for GEMS fabrication based on soft lithography is introduced. The fabrication scheme uses cheap, accessible materials and simple chemistry to produce shaped magnetic hydrogel microparticles with multispectral MRI contrast properties. The microparticles can be used as sensors by fabricating them out of shape-reconfigurable, "smart" hydrogels. The change in shape causes a corresponding shift in the resonance of the GEMS, producing an MRI-addressable readout of the microenvironment. Proof-of-principle experiments showing a multispectral response to pH change with cylindrical shell-shaped magnetogel GEMS are presented.

A Review on the Progress in Core-Spun Yarns (CSYs) Based Textile TENGs for Real-Time Energy Generation, Capture and Sensing.

This review is a critical analysis of the current state-of-the-art in core spun yarn textile triboelectric nanogenerators (CSY-T-TENGs) for self-powered smart sensing applications. The rapid expansion of wireless communication, flexible conductive materials, and wearable electronics over the last ten years is now demanding autonomous energy, which has created a new research space in the field of wearable T-TENGs. Current research is exploring T-TENGs made from CSYs as stable and reliable energy harvesters and sensing devices for modern wearable IoT platforms. CSY-TENGs are emerging as an important technology due to its simple structure, low cost, and excellent performance in converting mechanical energy into electrical energy and due to its sensing ability. This paper provides a critical review on current progress, it analyzes the unique advantages of CSYs T-TENGs over conventional T-TENGs, it describes fabrication techniques and discusses the materials used along with their properties and electrical performance characteristics, and it highlights the recent advancements in their integration with self-excitation circuits, charge storage devices and IoT-enabled smart sensing applications, such as environmental and health monitoring. In the conclusion, it discusses the challenges and future directions of CSYs T-TENGs and it provides a future road map for optimization, upscaling, and commercialization of the technology.

Acceptance of smart sensing: a barrier to implementation-results from a randomized controlled trial.

Background: Accurate and timely diagnostics are essential for effective mental healthcare. Given a resource- and time-limited mental healthcare system, novel digital and scalable diagnostic approaches such as smart sensing, which utilizes digital markers collected via sensors from digital devices, are explored. While the predictive accuracy of smart sensing is promising, its acceptance remains unclear. Based on the unified theory of acceptance and use of technology, the present study investigated (1) the effectiveness of an acceptance facilitating intervention (AFI), (2) the determinants of acceptance, and (3) the acceptance of adults toward smart sensing. Methods: The participants (N = 202) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a video on mindfulness. A reliable online questionnaire was used to assess acceptance, performance expectancy, effort expectancy, facilitating conditions, social influence, and trust. The self-reported interest in using and the installation of a smart sensing app were assessed as behavioral outcomes. The intervention effects were investigated in acceptance using t-tests for observed data and latent structural equation modeling (SEM) with full information maximum likelihood to handle missing data. The behavioral outcomes were analyzed with logistic regression. The determinants of acceptance were analyzed with SEM. The root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were used to evaluate the model fit. Results: The intervention did not affect the acceptance (p = 0.357), interest (OR = 0.75, 95% CI: 0.42-1.32, p = 0.314), or installation rate (OR = 0.29, 95% CI: 0.01-2.35, p = 0.294). The performance expectancy (γ = 0.45, p < 0.001), trust (γ = 0.24, p = 0.002), and social influence (γ = 0.32, p = 0.008) were identified as the core determinants of acceptance explaining 68% of its variance. The SEM model fit was excellent (RMSEA = 0.06, SRMR = 0.05). The overall acceptance was M = 10.9 (SD = 3.73), with 35.41% of the participants showing a low, 47.92% a moderate, and 10.41% a high acceptance. Discussion: The present AFI was not effective. The low to moderate acceptance of smart sensing poses a major barrier to its implementation. The performance expectancy, social influence, and trust should be targeted as the core factors of acceptance. Further studies are needed to identify effective ways to foster the acceptance of smart sensing and to develop successful implementation strategies. Clinical Trial Registration: identifier 10.17605/OSF.IO/GJTPH.

A multifunctional copper single-atom electrocatalyst aerogel for smart sensing and producing ammonia from nitrate.

Despite modern chemistry's success in providing affordable fertilizers for feeding the population and supporting the ammonia industry, ineffective nitrogen management has led to pollution of water resources and air, contributing to climate change. Here, we report a multifunctional copper single-atom electrocatalyst-based aerogel (Cu SAA) that integrates the multiscale structure of coordinated single-atomic sites and 3D channel frameworks. The Cu SAA demonstrates an impressive faradaic efficiency of 87% for NH3 synthesis, as well as remarkable sensing performance with detection limits of 0.15 ppm for NO3- and 1.19 ppm for NH4+. These multifunctional features enable precise control and conversion of nitrate to ammonia in the catalytic process, facilitating accurate regulation of the ammonium and nitrate ratios in fertilizers. We thus designed the Cu SAA into a smart and sustainable fertilizing system (SSFS), a prototype device for on-site automatic recycling of nutrients with precisely controlled nitrate/ammonium concentrations. The SSFS represents a forward step toward sustainable nutrient/waste recycling, thus permitting efficient nitrogen utilization of crops and mitigating pollutant emissions. This contribution exemplifies how electrocatalysis and nanotechnology can be potentially leveraged to enable sustainable agriculture.

Vibration-Based Smart Sensor for High-Flow Dust Measurement.

Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, and the finest category, referred to as sands, encompasses the so-called filler particles present in the mixture, which are smaller than 0.063 mm. As part of the H2020 CAPRI project, the authors present a prototype for measuring filler flow, through vibration analysis. The vibrations are generated by the filler particles crashing to a slim steel bar capable of withstanding the challenging conditions of temperature and pressure within the aspiration pipe of an industrial baghouse. This paper presents a prototype developed to address the need for quantifying the amount of filler in cold aggregates, considering the unavailability of commercially viable sensors suitable for the conditions encountered during asphalt mix production. In laboratory settings, the prototype simulates the aspiration process of a baghouse in an asphalt plant, accurately reproducing particle concentration and mass flow conditions. The experiments performed demonstrate that an accelerometer positioned outside the pipe can replicate the filler flow within the pipe, even when the filler aspiration conditions differ. The obtained results enable extrapolation from the laboratory model to a real-world baghouse model, making it applicable to various aspiration processes, particularly those involving baghouses. Moreover, this paper provides open access to all the data and results used, as part of our commitment to the CAPRI project, with the principles of open science.

Effect of Vulcanization on the Electro-Mechanical Sensing Characteristics of Multi-Walled Carbon Nanotube/Silicone Rubber Composites.

In order to realize effective monitoring for the working performance of seismic isolation structures, a multi-walled carbon nanotube (MWCNT)/methyl vinyl silicone rubber (VMQ) composite was prepared via mechanical blending using dicumyl peroxide (DCP) and 2,5-dimethyl-2,5-di(tert-butyl peroxy)hexane (DBPMH) as vulcanizing agents. The effects of the different vulcanizing agents on the dispersion of the MWCNT, electrical conductivity, mechanical properties, and resistance-strain response of the composites were investigated. The experimental results showed that the percolation threshold of the composites prepared with the two vulcanizing agents was low, while the DCP-vulcanized composites showed high mechanical properties and a better resistance-strain response sensitivity and stability, especially after 15,000 loading cycles. According to the analysis using scanning electron microscopy and Fourier infrared spectroscopy, it was found that the DCP contributed higher vulcanization activity, a denser cross-linking network, better and uniform dispersion, and a more stable damage-reconstruction mechanism for the MWCNT network during the deformation load. Thus, the DCP-vulcanized composites showed better mechanical performance and electrical response abilities. When employing an analytical model based on the tunnel effect theory, the mechanism of the resistance-strain response was explained, and the potential of this composite for real-time strain monitoring for large deformation structures was confirmed.

QoS Review: Smart Sensing in Wake of COVID-19, Current Trends and Specifications With Future Research Directions.

Smart Sensing has shown notable contributions in the healthcare industry and revamps immense advancement. With this, the present smart sensing applications such as the Internet of Medical Things (IoMT) applications are elongated in the COVID-19 outbreak to facilitate the victims and alleviate the extensive contamination frequency of this pathogenic virus. Although, the existing IoMT applications are utilized productively in this pandemic, but somehow, the Quality of Service (QoS) metrics are overlooked, which is the basic need of these applications followed by patients, physicians, nursing staff, etc. In this review article, we will give a comprehensive assessment of the QoS of IoMT applications used in this pandemic from 2019 to 2021 to identify their requirements and current challenges by taking into account various network components and communication metrics. To claim the contribution of this work, we explored layer-wise QoS challenges in the existing literature to identify particular requirements, and set the footprint for future research. Finally, we compared each section with the existing review articles to acknowledge the uniqueness of this work followed by the answer of a question why this survey paper is needed in the presence of current state-of-the-art review papers.

ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation.

Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user's identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings.

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation.

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation.