Real-time Device Research Articles

Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

Identifying the type of vehicle on the road is a challenging task, especially in the natural environment with all its complexities, such that the traditional architecture for object detection requires an excessively large amount of computation. Such lightweight networks as MobileNet are fast but cannot satisfy the performance-related requirements of this task. Improving the detection-related performance of small networks is, thus, an outstanding challenge. In this paper, we use YOLOv5s as the backbone network to propose a large-scale convolutional fusion module called the ghost cross-stage partial network (G_CSP), which can integrate large-scale information from different feature maps to identify vehicles on the road. We use the convolutional triplet attention network (C_TA) module to extract attention-based information from different dimensions. We also optimize the original spatial pyramid pooling fast (SPPF) module and use the dilated convolution to increase the capability of the network to extract information. The optimized module is called the DSPPF. The results of extensive experiments on the bdd100K, VOC2012 + 2007, and VOC2019 datasets showed that the improved YOLOv5s network performs well and can be used on mobile devices in real time.

Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare.

The patients of diabetes require to observe and control their glycemic profile through continuous glucose level monitoring. The blood glucose measurement is possible through invasive, minimally invasive and non-invasive methods. Invasive method is traditional method for instant glucose measurement where glucose is measured by taking blood samples from the body. However, the repeated finger pricking increases the risk of blood-related infections and trauma. Hence, the development of non-invasive real time device is essential for smart healthcare to manage glucose-insulin balance. The paper presents machine learning models for non-invasive glucose measurement. So, various machine learning algorithms including Logistic Regression, KNN, Gaussian Naive Bayes, Linear Regression, Multi-polynomial Regression, Neural Network, XGBoost, Decision Tree, Random Forest and Support Vector Machine are applied on two dataset which are PIDD (UCI repository) and iGLU dataset (iGLU device). The comparative analysis is carried out where accuracy, training time, recall, precision, f-1 score and AUC curve is measured for classification algorithms. For regression algorithms, measures like accuracy, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used for comparison purpose. Random forest with 84% accuracy and 68% recall, 76% precision and 72% f1-score for PIDD and Decision tree with 70% accuracy, 8% mean absolute error (MAE) and 8.5% root mean square error (RMSE) for iGLU dataset gives best results. Clark grid analysis has also been done where all the values fall under zone A which gives 100% accuracy and the device is useful for medication purpose. The proposed work has been also compared with similar methods and the proposed work has excellent results in terms of MAD, mARD, RMSE and AvgE. The device would be ideal as non-invasive solution for continuous glucose monitoring.Graphical abstract

A microfluidic system for rapid nucleic acid analysis based on real-time convective PCR at point-of-care testing.

A microfluidic system for rapid nucleic acid analysis based on real-time convective PCR is developed. To perform ‘sample-in, answer-out’ nucleic acid analysis, a microfluidic chip is developed to efficiently extract nucleic acid, and meanwhile convective PCR (CPCR) is applied for rapid nucleic acid amplification. With an integrated microfluidic chip consisting of reagent pre-storage chambers, a lysis & wash chamber, an elution chamber and a waste chamber, nucleic acid extraction based on magnetic beads can be automatically performed for a large size of test sample within a limited time. Based on an easy-to-operate strategy, different pre-stored reagents can be conveniently released for consecutive reaction at different steps. To achieve efficient mixing, a portable companion device is developed to introduce properly controlled 3-D actuation to magnetic beads in nucleic acid extraction. In CPCR amplification, PCR reagent can be spontaneously and repeatedly circulated between hot and cool zones of the reactor for space-domain thermal cycling based on pseudo-isothermal heating. A handheld real-time CPCR device is developed to perform nucleic acid amplification and in-situ detection. To extend the detection throughput, multiple handheld real-time CPCR devices can be grouped together by a common control system. It is demonstrated that influenza A (H1N1) viruses with the reasonable concentration down to 1.0 TCID50/ml can be successfully detected with the microfluidic system.

Sewer defect instance segmentation, localization, and 3D reconstruction for sewer floating capsule robots

Most automated sewer inspection tasks are based on closed-circuit television (CCTV) methods and focus on image classification or object detection but fail to obtain information on fine-grained sewer defects. Targeting the sewer defect detection from a video and performing the sewer inspection in a lightweight, low-cost, and practical manner, this study develops a sewer detection framework based on computer vision technology for the sewer floating capsule robot. The proposed framework performs three main sub-tasks, including instance segmentation, real-time sewer inspection device localization, and real three-dimensional (3D) model reconstruction, to realize sewer defect detection. An improved mask regional convolutional neural network (Mask RCNN), which integrates split attention module and balanced L1 loss module, is proposed for robust feature representation. In addition, an improved data augmentation method developed according to the sewer defect instance segmentation tasks is introduced. The 3D reconstruction and real-time localization of a sewer scene are achieved using the structure-from-motion techniques for the sewer floating capsule robot. Extensive experiments are conducted, and the experimental results show that the proposed method is effective and robust. The average precision of instance segmentation at the intersection of the union value of 0.5 is 92.7%, and the maximum 3D model measurement error is 1 m. However, video sequence information and multi-sensor fusion, combining the inertial measurement unit and vision technique, could be studied in the future to achieve better generalization and robust results.

Multidimensional nanoarchitectures of TiO2/Au nanofibers with O-doped C3N4 nanosheets for electrochemical detection of nitrofurazone

The electrochemical sensor has high promise for applications in the next-generation real-time monitoring devices and technologies. However, the related further development has been hindered by some intractable problems of the electrochemical sensors. In this work, we develop the nanocomposite of TiO2/Au nanofibers (NFs) with O-C3N4 nanosheets (NSs) by a solvothermal approach for the electrochemical detection of nitrofurazone (NFZ), an antimicrobial drug. The physicochemical characteristics of TiO2/Au-NFs/O-C3N4 nanocomposite is fully analyzed by diverse electron microscopic, spectroscopic and electroanalytical techniques. It is noted that the electrochemical response of TiO2/Au-NFs/O-C3N4 modified electrode is dramatically higher than unmodified and other modified electrodes. The TiO2/Au-NFs/O-C3N4 nanocomposite-based electrochemical sensor owns the superior performance for the NFZ detection, such as a wide working range of 0.008–105 μM, a limit of detection of 0.001 μM and high sensitivity of 1.40 µA µM−1 cm−2. Besides, it displays attractive features in terms of high selectivity, good steady-state stability (90 %), high cycle stability (94.8 %), fine repeatability (2.16 %) and reproducibility (1.34 %). The fabricated electrochemical sensor is also investigated to monitor the quantity of NFZ level in the real samples with the satisfactory results.

The Effect of Using a Real-Time Feedback Device on the Quality of Cardio Pulmonary Resuscitation (CPR)

Cardiopulmonary resuscitation (CPR) is a standard procedure delivered to patients experiencing cardiac arrest. CPR quality is associated with the rate of survival of patients with cardiac arrest. To determine the efficacy of a real-time feedback device in terms of improving CPR performance quality and CPR self-efficacy in nursing staffs. A total of 72 nursing staffs were enrolled in this randomized trial (intervention n = 38, control n = 34). A real-time feedback device was used to monitor CPR quality and provide real-time feedback. The participants performed CPR on a manikin with (intervention) or without (control) feedback for 5 cycles (about 2 minutes). Data on participant demographic characteristics and CPR self-efficacy scores were also collected. The intervention group performed better in terms of the compression adequate rate ratio (76.3 vs. 52.3, p < .001), mean depth (52.8 mm vs. 50.6 mm, p = .003), adequate depth ratio (65.6 vs. 55.1, p = .014), quality of CPR performance score (44.1 vs. 26.1, p < .001), proportion of excellent CPR (60.5 vs. 26.5, p < .001), and CPR self-efficacy score (40.22 vs. 36.71, p < .001). However, no significant differences between the two groups were found in terms of proportion of complete chest decompression and chest compression fracture (p >.05). Using a real-time feedback device may not only significantly improve the quality of CPR performed on a manikin but also improve self-confidence to implement CPR correctly in practice.

Assessment of Real-Time Active Noise Control Devices in Dental Treatment Conditions.

Dental clinics are exposed to various uncomfortable noises. The aim of this study was to quantify the effectiveness of active noise control devices in dental treatment conditions. Two types of commercial headsets (Airpods Pro, QC30) and two types of dental headsets (Alltalk, Quieton Dental) were used for the experiment. Three sounds (high-speed handpiece, low-speed handpiece, and suction system) were measured at three different distances from the dental teeth model, typodont. The distances of 10, 40, and 70 cm reflected the positions of the patient, assistant, and practitioner’s ears, respectively. Sound analysis was performed, and the significance of differences in the maximum noise level using each device was determined with the Kruskal–Wallis test. Dental noise was characterized by the peak in sound pressure level (SPL) at 4–5 kHz and >15 kHz frequencies. The commercial headsets efficiently blocked 1 kHz and 10 kHz of noise. The dental headsets efficiently reduced 4–6 and >15 kHz noise. Quieton had the highest maximum SPL in all situations and positions among the four devices. For a better dental clinic, however, active noise control devices more suitable for the characteristics of dental noise should be developed.

SRCLoc: Synthetic Radio Map Construction Method for Fingerprinting Outdoor Localization in Hybrid Networks

A precise localization system is a key enabling technology for Internet of Things (IoT) applications and location-based services. Fingerprint-based localization methods are well-known and widely used solutions. These methods, however, are time-consuming and laborious for radio map construction during an offline site survey in large-scale applications. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network-based radio map construction method for real-time device localization. The proposed synthetic radio map construction method for fingerprinting outdoor localization (SRCLoc) combined the hybrid support vector machine and deep gated recurrent unit algorithms sequentially. The SRCLoc reduced the workload of site surveying required to build the fingerprint database by up to 85.7%. The results show that the average positioning error of SRCLoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Effect of real-time feedback device compared to use or non-use of a checklist performance aid on post-training performance and retention of infant cardiopulmonary resuscitation: A randomized simulation-based trial

IntroductionThis study aims to determine the best method for achieving optimal performance of pediatric cardiopulmonary resuscitation (CPR) during simulation-based training, whether with or without a performance aid. MethodsIn this randomized controlled study, 46 participants performed simulated CPR in pairs on a Resusci Baby QCPR™ mannequin, repeated after four weeks. All participants performed the first simulation without performance aids. For the second simulation, they were randomly assigned to one of three groups with stratification based on status: throughout CPR, Group A (n = 16) was the control group and did not use a performance aid; Group B (n = 16) used the CPR checklist; Group C (n = 14) used real-time visualization of their CPR activity on a feedback device. Overall performance was assessed using the QCPR™. ResultsAll groups demonstrated improved performance on the second simulation (p < 0.01). Use of the feedback device resulted in better CPR performance than use of the CPR checklist (p = 0.02) or no performance aid (p = 0.04). Additionally, participants thought that the QCPR™ could effectively improve their technical competences. ConclusionsPerformance aid based on continuous feedback is helpful in the learning process. The use of the QCPR™, a real-time feedback device, improved the quality of resuscitation during infant CPR simulation-based training.

The association between CPR quality of In-hospital resuscitation and sex: A hypothesis generating, prospective observational study

IntroductionThe relationship between sex and cardiopulmonary resuscitation (CPR) outcomes remains unclear. Particularly, questions remain regarding the potential contribution of unmeasured confounders. We aimed to examine the differences in the quality of chest compression delivered to men and women. MethodsProspective study of observational data recorded during consecutive resuscitations occurring in a single tertiary center (Feb-1-2015 to Dec-31-2018) with real-time follow-up to hospital discharge. The studied variables included time in CPR, no-flow-time and fraction, compression rate and depth and release velocity. The primary study endpoint was the unadjusted association between patient sex and the chest compression quality (depth and rate). The secondary endpoint was the association between the various components of chest compression quality, sex, and survival to hospital discharge/neurologically intact survival. ResultsOverall 260 in-hospital resuscitations (57.7% male patients) were included. Among these 100 (38.5%) achieved return of spontaneous circulation (ROSC) and 35 (13.5%) survived to hospital discharge. Female patients were significantly older. Ischemic heart disease and ventricular arrhythmias were more prevalent among males. Compression depth was greater in female vs male patients (54.9 ± 11.3 vs 51.7 ± 10.9 mm; p = 0.024). Other CPR quality-metrics were similar. The rates of ROSC, survival to hospital discharge and neurologically intact survival did not differ between males and females. Univariate analysis revealed no association between sex, quality metrics and outcomes. DiscussionWomen received deeper chest compressions during in-hospital CPR. Our findings require corroboration in larger cohorts but nonetheless underscore the need to maintain high-quality CPR in all patients using real-time feedback devices. Future studies should also include data on ventilation rates and volumes which may contribute to survival outcomes.

Design of Highway Icing Warning System

This design discloses a real-time safety device for warning road surface icing, including solar ground lamp (1) and solar LED display (2).This design adopts the facility device composed of solar ground lamp and solar LED display screen. The solar ground lamp is equipped with solar photovoltaic cell, charging and discharging battery, red and green LED lamp, temperature and humidity sensor and D data processing and transmission chip circuit;The solar LED display screen is equipped with solar photovoltaic cell, charging and discharging battery, LED graphic display screen, P data processing transmission chip circuit and red flashing warning light;According to the technical scheme of sending out red light and graphic warning according to the freezing critical data collected from the ground, and transmitting the data to the traffic control center through the Internet, it overcomes the problems and shortcomings of the existing technology that it is difficult to issue the pavement freezing warning in real time, so that the expressway can achieve the purpose of real-time and real-time pavement freezing warning.

Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication.

An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation.

Local activation waves (LAWs) detection in complex fractionated atrial electrograms (CFAEs) during catheter ablation (CA) of atrial fibrillation (AF), the commonest cardiac arrhythmia, is a complicated task due to their extreme variability and heterogeneity in amplitude and morphology. There are few published works on reliable LAWs detectors, which are efficient for regular or low fractionated bipolar electrograms (EGMs) but lack satisfactory results when CFAEs are analyzed. The aim of the present work is the development of a novel optimized method for LAWs detection in CFAEs in order to assist cardiac mapping and catheter ablation (CA) guidance. The database consists of 119 bipolar EGMs classified by AF types according to Wells’ classification. The proposed method introduces an alternative Botteron’s preprocessing technique targeting the slow and small-ampitude activations. The lower band-pass filter cut-off frequency is modified to 20 Hz, and a hyperbolic tangent function is applied over CFAEs. Detection is firstly performed through an amplitude-based threshold and an escalating cycle-length (CL) analysis. Activation time is calculated at each LAW’s barycenter. Analysis is applied in five-second overlapping segments. LAWs were manually annotated by two experts and compared with algorithm-annotated LAWs. AF types I and II showed accuracy and sensitivity. AF type III showed accuracy and sensitivity. The results of this study highlight the efficiency of the developed method in precisely detecting LAWs in CFAEs. Hence, it could be implemented on real-time mapping devices and used during CA, providing robust detection results regardless of the fractionation degree of the analyzed recordings.

Design and Analysis: Servo-Tube-Powered Liquid Jet Injector for Drug Delivery Applications

The current state of commercially available needle-free liquid jet injectors for drug delivery offers no way of controlling the output pressure of the device in real time, as the driving mechanism for these injectors provides a fixed delivery pressure profile. In order to improve the delivery efficiency as well as the precision of the targeted tissue depth, it is necessary to develop a power source that can accurately control the plunger velocity. The duration of a liquid jet injection can vary from 10 to 100 ms, and it generate acceleration greater than 2 g (where g is the gravity); thus, a platform for real-time control must exhibit a response time greater than 1 kHz and good accuracy. Improving the pioneering work by Taberner and others whereby a Lorentz force actuator based upon a voice coil is designed, this study presents a prototype injector system with greater controllability based on the use of a fully closed-loop control system and a classical three-phase linear motor consisting of three fixed coils and multiple permanent magnets. Apart from being capable of generating jets with a required stagnation pressure of 15–16 MPa for skin penetration and liquid injection, as well as reproducing typical injection dynamics using commercially available injectors, the novelty of this proposed platform is that it is proven to be capable of shaping the real-time jet injection pressure profile, including pulsed injection, so that it can later be tailored for more efficient drug delivery.

Effect of time windows in LSTM networks for EEG-based BCIs

People with impaired motor function could be helped by an effective brain–computer interface (BCI) based on a real-time electroencephalogram (EEG) and artificial intelligence algorithms. However, current methodologies for interpreting patient instructions from an EEG are not accurate enough to be completely safe in a real-world situation , where a poor decision would place their physical integrity at risk, such as when traveling in an electric wheelchair in a city. For various reasons, such as the low signal-to-noise ratio of portable EEGs or the effects of signal contamination (disturbances due to user movement, temporal variation of the features of EEG signals, etc.), a long short-term memory network (LSTM) (a type of recurrent neural network) that is able to learn data flow patterns from EEG signals could improve the classification of the actions taken by the user. In this paper, the effectiveness of using an LSTM with a low-cost wireless EEG device in real time is tested, and the time window that maximizes its classification accuracy is studied. The goal is to be able to implement it in the BCI of a smart wheelchair with a simple coded command protocol, such as opening or closing the eyes, which could be executed by patients with reduced mobility. Results show a higher resolution of the LSTM with an accuracy range between 77.61 and 92.14% compared to traditional classifiers (59.71%), and an optimal time window of around 7 s for the task done by users in this work. In addition, tests in real-life contexts show that a trade-off between accuracy and response times is necessary to ensure detection.

Depth-dependent apodization window based on Chebyshev polynomial for ultrasound imaging.

To improve the image resolution of a portable real-time ultrasound imaging device, this paper proposes a beamformer with composite apodization windows based on Chebyshev polynomial. By introducing the Chebyshev window, the level of sidelobe can be flexibly controlled. The value of weighting changes according to the detection depth and ultrasound attenuation such that it can attain better image resolution than traditional beamformers. The simulations and experiments indicate that the proposed method can flexibly narrow the mainlobe width while keeping the performance of the suppressing artifacts. The full width at half-maximum of this method is 14.1%-27.8% and 4.2%-25.0% narrower than those of traditional apodization methods in the field II simulations and Geabr_0 experiment, respectively.

Heart Sound Analysis Using SAINet Incorporating CNN and Transfer Learning for Detecting Heart Diseases

Cardiovascular disease (CVD) is the leading cause of death worldwide. Accurate and early diagnosis of cardiovascular disease (CVD) is essential for its timely treatment and management. However, this is challenging because traditional techniques for detecting heart diseases, such as auscultation, are highly subjective and prone to error. This study addresses this issue by building a novel customised deep learning architecture, SAINet, for automated CVD detection through heart sound analysis. Research is being conducted on the application of artificial intelligence (AI) to analyse phonocardiograms to detect CVD. This study aims to address this challenge by detecting heart disease using a novel customised neural network consisting of transfer learning techniques and convolutional neural networks to analyse heart sounds with increased accuracy, precision and recall and reduced computational complexity compared when compared to others. Approximately 1000 recordings of heart sounds were used to train and test the model. Data augmentation was performed to increase the size of the training data. Two combinations of datasets were used in the experiments. The first combination consisted of two categories of heart sound recording: normal and abnormal. The second combination consisted of one normal and four different abnormal categories of heart sounds. An accuracy of 99.68% was achieved with the first combination, and 99.58% with the second combination. Both combinations yielded values above 99% for precision, recall, specificity, and the F1-score. The method proposed in this study is suitable for embedding CVDs in real-time devices such as an electronic stethoscope.

Preparation of Photoswitchable Polyacrylic Nanocomposite Fibers Containing Au Nanorods and Spiropyran: Optical and Plasmonic Properties

Photoswitchable nanofibers and nanocomposite fibers containing plasmonic nanoparticles have attracted a great deal of interest in optical and plasmonic devices. Herein, photoswitchable poly(methyl methacrylate-co-vinylimidazole-co-spiropyran ethyl acrylate) (MVSP) and its copolymer with butyl acrylate (MBVSP) were prepared via emulsion polymerization, and the corresponding nanofibers (MVSP@NF and MBVSP@NF) and nanocomposite fibers (MVSP/Au@NF and MBVSP/Au@NF) containing AuNRs were fabricated through electrospinning. FTIR and 1H NMR analyses confirmed the progress of the copolymerization reaction. The morphology of the prepared nanofibers containing AuNRs with an aspect ratio of 2.5 was identified by SEM and TEM techniques. The inclusion of vinylimidazole into the copolymer chains resulted in well-dispersed AuNRs. Photoisomerization studies revealed a higher photochromic efficiency for MBVSP@F (reflective intensity of 37.4%) with respect to MVSP@NF (reflective intensity of 62.5%) because of the greater flexibility of the chains. In addition, the presence of AuNRs in the nanocomposite fibers with high absorptivity intensified the photochromic properties for both samples. The polarization-dependent plasmonic band of AuNRs was switched between 650 and 634 nm through the photoisomerization of nonpolar SP to polar MC reversibly for MVSP/Au@NF. This displacement was just 4 nm for MBVSP/Au@NF, owing to the limited coupling between AuNRs and MC isomers. Besides, the capability of both nanocomposite fibers for reversible optical patterning was investigated by fast write-erase cycles. Enhanced photofatigue resistance in those fibers and the photomodulation of the plasmonic band of AuNRs using SP to MC isomerization revealed their promising potential for optical patterning and on-demand real-time plasmonic devices.

Policies for the dynamic traveling maintainer problem with alerts

Downtime of industrial assets such as wind turbines and medical imaging devices comes at a sharp cost. To avoid such downtime costs, companies seek to initiate maintenance just before failure. Unfortunately, this is challenging for the following two reasons: On the one hand, because asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices which signal early degradation. On the other hand, because the available resources to serve a network of geographically dispersed assets are typically limited. In this paper, we propose a novel model referred to as the dynamic traveling maintainer problem with alerts that incorporates these two challenges and we provide three solution approaches on how to dispatch the limited resources. Namely, we propose: (i) Greedy heuristic approaches that rank assets on urgency, proximity and economic risk; (ii) A novel traveling maintainer heuristic approach that optimizes short-term costs; and (iii) A deep reinforcement learning (DRL) approach that optimizes long-term costs. Each approach has different requirements concerning the available alert information. Experiments with small asset networks show that all methods can approximate the optimal policy when given access to complete condition information. For larger networks, the proposed methods yield competitive policies, with DRL consistently achieving the lowest costs.

Ergonomics evaluation of lawn mower operator's working posture using JACK software and kinect interface.

Nowadays, real-time motion tracking devices are widely used for ergonomic assessment of several manual quotidian activities. The real-time tracking of human activities makes it easier to observe the exposure of work-related musculoskeletal disorders (WMSDs) in the human body. This study aims to determine the suitability of a real-time motion tracking device (Kinect v1 interfaced with a commercial ergonomic assessment software, JACK) for real-time ergonomic evaluation of the strenuous operation of the manual lawn mower. The lawn mower operators perform various strenuous activities while operating the manual lawn mower for long intervals of time, which causes WMSDs in the entire body of the operators. These working operators' activities have been captured using Kinect v1 interfaced with JACK, to address the ergonomic issues responsible for the whole-body WMSDs. The forces acting on the lower back, Rapid Upper Limb Assessment score and static strength have been predicted using JACK. This study proves the exposure of the operators towards the whole-body WMSDs while operating the manual lawn mower. The findings provide a quick and straightforward approach for performing the real-time ergonomic evaluation of any operation, which can help the industrial staff estimate the risk of level WMSDs.