Signal Values Research Articles

Liquid-Solid Triboelectric Nanogenerator-Based DNA Barcode Detection Biosensor for Species Identification.

DNA barcode detection method is widely applied for species identification, which is imperative to evaluate the effect of human economic activities on the biodiversity of ecosystem. However, the wide utilization of existing detection biosensors is limited by bulky and expensive instruments, such as Raman spectroscopy and electrochemical station. Herein, a liquid-solid triboelectric nanogenerator (TENG)-based DNA barcode detection biosensor is proposed, which consists of water flow, fluid channel, and PDMS film attached by specifically designed capture probe. Through sequentially combining capture probe, targeted DNA barcode, and signal probe with Au nanoparticles (NPs), the surface charge density of friction layer of TENG decreases under the effect of AuNPs, verified by the density functional theory (DFT) method. Consequently, the peak value of output current spike signal for targeted DNA is smaller than that for other DNA, which is the working mechanism of the present TENG-based biosensor. Such biosensor successfully recognizes Alvinocaris muricola among different types of Alvinocarididae shrimps, and its low limit detection can reach 1×10-12m. The present work provides a paradigm-shift way to develop an inexpensive and accurate technique to detect DNA barcode for species identification, and paves a novel way for the application of liquid-solid TENG.

Did Crop Domestication Change the Fitness Landscape of Root Response to Soil Mechanical Impedance? An in-silico Analysis.

Root axes with greater penetration ability are often considered to be beneficial in hard soils. We hypothesized that maize root phenotypes with greater plasticity (meaning reduced elongation in response to mechanical impedance, i.e. a 'stop signal') have fitness advantages over phenotypes with reduced plasticity (i.e. unimpeded root elongation) in native (virgin, uncultivated) soils, by reallocating root foraging to softer, presumably wetter, soil domains, and that the value of the stop signal reduced with soil cultivation and crop domestication. We used OpenSimRoot to simulate native and cultivated soils and evaluated maize root phenotypes with varying axial and lateral root penetration ability in water, nitrogen, and impedance regimes associated with Neolithic agriculture. The stop signal was advantageous in native soils but was less beneficial in cultivated, irrigated soils. Reduced root foraging in hard, dry topsoil enabled root growth in deeper domains where water is available, resulting in an improved balance of resource expenditure and acquisition. The value of the stop signal declined during crop domestication with the advent of irrigation, which increased water availability in the topsoil. Soil cultivation reduced N availability, while irrigation increased N leaching, resulting in a shift in the fitness landscape, with greater lateral root length (i.e. reduced plasticity) being advantageous by colocalizing root foraging with N availability. The importance of the stop signal is evident in modern high-input systems in which drought is a limiting factor. Our results support the hypotheses that the reduction of lateral root growth by mechanical impedance is adaptive in native soil, but became less adaptive with soil cultivation and irrigation associated with Neolithic agriculture.

Non-invasively differentiate non-alcoholic steatohepatitis by visualizing hepatic integrin αvβ3 expression with a targeted molecular imaging modality

BACKGROUND Non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH), an inflammatory subtype of non-alcoholic fatty liver disease (NAFLD), are currently unavailable. AIM To develop an integrin αvβ3-targeted molecular imaging modality to differentiate NASH. METHODS Integrin αvβ3 expression was assessed in Human LO2 hepatocytes Scultured with palmitic and oleic acids (FFA). Hepatic integrin αvβ3 expression was analyzed in rabbits fed a high-fat diet (HFD) and in rats fed a high-fat, high-carbohydrate diet (HFCD). After synthesis, cyclic arginine-glycine-aspartic acid peptide (cRGD) was labeled with gadolinium (Gd) and used as a contrast agent in magnetic resonance imaging (MRI) performed on mice fed with HFCD. RESULTS Integrin αvβ3 was markedly expressed on FFA-cultured hepatocytes, unlike the control hepatocytes. Hepatic integrin αvβ3 expression significantly increased in both HFD-fed rabbits and HFCD-fed rats as simple fatty liver (FL) progressed to steatohepatitis. The distribution of integrin αvβ3 in the liver of NASH cases largely overlapped with albumin-positive staining areas. In comparison to mice with simple FL, the relative liver MRI-T1 signal value at 60 minutes post-injection of Gd-labeled cRGD was significantly increased in mice with steatohepatitis (P < 0.05), showing a positive correlation with the NAFLD activity score (r = 0.945; P < 0.01). Hepatic integrin αvβ3 expression was significantly upregulated during NASH development, with hepatocytes being the primary cells expressing integrin αvβ3. CONCLUSION After using Gd-labeled cRGD as a tracer, NASH was successfully distinguished by visualizing hepatic integrin αvβ3 expression with MRI.

The Design and Application of an Assistive Hip Joint Exoskeleton for Tower Climbing

In order to ensure the safety of maintenance personnel during tower climbing and improve the efficiency of power maintenance work, this study designed an assistive hip joint exoskeleton robot and analyzed the kinematic data obtained from tower climbers during the climbing process. A neural-network-based assistive control algorithm for tower climbing was created, and a tower climbing experiment was conducted with volunteers. The surface electromyographic (sEMG) signals of four muscles, namely the biceps femoris (BF), gluteus maximus (GM), semimembranosus (SM), and semitendinosus (ST), were collected to evaluate the performance of the robot. The experimental results show that the exoskeleton robot could reduce the root mean square (RMS) values of the sEMG signals of the main force-generating muscles related to the hip joint. This suggests that the robot can effectively assist personnel in tower climbing operations.

What Affects User Experience of Shared Mobility Services? Insights from Integrating Signaling Theory and Value Framework

What Affects User Experience of Shared Mobility Services? Insights from Integrating Signaling Theory and Value Framework

Feedback projection synchronization of discrete chaotic systems and its application to speech encryption

Abstract Chaotic systems are widely used in secure communication due to their sensitivity to initial values, unpredictability, and complex motion trajectories. In this paper, we study the encryption method of chaotic synchronization and introduce a scaling factor based on traditional feedback control synchronization to achieve more accurate projection synchronization. The effectiveness and robustness of the method in chaotic systems are verified through theoretical proofs and numerical simulations. A chaotic masked speech encryption system utilizing bit similarity is designed; the structural similarity index (SSIM) of the decrypted signal with the original signal is as high as 0.992866, while the SSIM value of the encrypted signal with the original signal is only 0.000030, proving the efficiency and security of the encryption process. Additionally, we analyzed the data transmission process of the encryption system. The fusion of the control signal and the encryption sequence into one transmission sequence in the channel not only saves hardware and software design resources but also reduces inter-channel interference and conflict, improving the reliability and stability of the transmission. Experimental results show that the system performs well in terms of data transmission security and anti-interference capability.

NIMG-85. RADIATION DOSE IS ASSOCIATED WITH CHANGE IN MAGNETIC RESONANCE FINGERPRINTING PROPERTIES OF ANATOMICALLY DISTINCT REGIONS OF INTEREST IN GLIOBLASTOMA MULTIFORME PATIENTS

Abstract Magnetic Resonance Fingerprinting (MRF) is an investigational imaging technique that provides spatially-resolved quantification of tissue properties, in contrast to qualitatively weighted conventional MR imaging. Intentional sequential variation of acquisition parameters throughout the MRF acquisition produces a unique quantitative signal timecourse for each tissue, which generates a spatial “fingerprint” for individual voxels. This study applies MRF to quantitatively correlate radiation dose with MR signal intensity changes, specifically T1, T2, and M0 maps across regions of interest (ROIs) that are involved in cognitive speed and processing. Three patients who underwent radiation therapy for resected glioblastoma multiforme participated in this study. MRF scans were conducted at pre-radiotherapy (pre-RT), mid-radiotherapy (mid-RT), and post-radiotherapy (post-RT) timepoints. ROIs (bilateral hippocampi, amygdalae, dorsolateral prefrontal cortices, and corpus callosum) were contoured and radiation dose to the ROI contours at mid-RT and post-RT timepoints were obtained using MIM treatment planning software (MIM Software, Beachwood, OH). Spearman’s rank correlation test was conducted in MATLAB (MATLAB R2024a, Natick, MA) for mean radiation dose to ROIs against MRF T1 relaxation time, T2 relaxation time, and M0 proton density. Mean radiation dose exhibited statistically significant correlation with absolute changes from pre-RT to post-RT T1 relaxation time, T2 relaxation time, and M0 proton density values across the ROIs. Mid-RT scans showed intermediate changes, suggesting a progressive alteration in tissue characteristics with accumulating radiation dose. The hippocampus and amygdala received lower radiation doses than other ROIs and exhibited less pronounced changes in T1, T2, and M0 values. These results demonstrate a novel application of MRF in quantitative characterization of imaging changes in anatomically distinct ROIs. The correlation between radiation dose and absolute changes in MRF signal values from pre-RT to post-RT MRF scans reflect progressive changes as a result of radiation exposure.

Analyses for Signal Detection Algorithms and Corresponding VLSI Architectures Applied for Massive MIMO Systems

Abstract. In the field of 5G communications, the utilization of Massive MIMO has significantly improved the spectral efficiency. However, signal detection involves large-scale matrix inversion, which is computationally intensive. Traditional techniques for linear detection, such as Zero Forcing and Minimum Mean Square Error, struggle to effectively reduce computational complexity. Therefore, this paper introduces three methods to mitigate the complexity in signal detection: the Alternating Direction Method of Multipliers for conditions where the base stations quantities are nearly equivalent to the users quantities, the Neumann series expansion method that replaces matrix inversion, and iterative methods that approximate the true value through iterative updates. Future work should focus on optimizing the iterative expressions and incorporating tools such as deep learning to further reduce computational complexity and accurately retrieve the signal values.

Adaptive distributed dynamic average consensus with prescribed performance

AbstractA novel adaptive distributed estimation algorithm is developed for dynamic average consensus problem. The algorithm enables a group of agents to accurately track the average value of signals that are available locally to each agent. Unlike most of existing approaches that rely on error transformation techniques, our algorithm employs an adaptive‐gain based barrier function to simultaneously meet prescribed performance specifications of transient and steady state. It is both simple and easy to implement and is not dependent on bounded reference signals and their derivatives. Additionally, it can operate under less strict assumptions than those that assume absolute bounds on the signals. Simulation results are presented to demonstrate the effectiveness of the developed algorithm.

Surface electromyography character of upper limb muscle after open reduction combine with ulnar osteotomy in children with neglected Monteggia fracture.

This study aims to investigate the surface electromyography (sEMG) characteristics of upper limb muscles in children with neglected Monteggia fracture after open reduction of radial head dislocation and ulna osteotomy and bone grafting internal fixation, and to understand the recovery of muscle activity in children after operation, provide reference for clinical rehabilitation. A retrospective analysis was conducted on sixteen children with neglected Monteggia fracture who underwent ulna osteotomy at our hospital from January 2021 to August 2022. The biceps brachii, triceps brachii, flexor carpi radialis, and extensor carpi ulnaris muscle activities were recorded during grip strength tests, flexion and extension of elbow joint while holding a 1 kg dumbbell, as well as gripping tasks. The root mean square (RMS) values of sEMG signals, co-contraction ratio, and elbow joint function scores were compared between pre- and post-operation periods as well as between the affected side and unaffected side. The preoperative maximum grip strength, as well as the average RMS values of flexor carpi radialis and average RMS value of extensor carpi ulnaris on the affected side were significantly lower. After surgery, both the maximum RMS value of biceps brachii and maximum and average RMS value of extensor carpi ulnaris on the affected side remained lower. Prior to surgery, when performing elbow flexion and extension tests while holding a 1 kg dumbbell, both mean RMS values of biceps brachii and flexor carpi radialis on the affected side were smaller. However, after surgery, there was an increase in mean RMS values of biceps brachii on the affected side. Furthermore, postoperative elbow function scores were significantly higher than preoperative scores. Open reduction of radial head dislocation combined with ulna osteotomy and bone grafting can achieve good functional activities in the treatment of neglected Monteggia fractures in children. The EMG activity of the extensor carnosus ulnalis muscle on the affected side related to grip strength was low, and the desired effect was not achieved within the expected time.

Can Catering to Local Area Needs Through CSR Reduce Firm Risk?

ABSTRACTThis paper examines the effect of corporate social responsibility (CSR) spending to cater to the local area's needs (the area of business operations) in reducing the firm's risk in the mandatory CSR setting. Using a sample of 1677 Indian firms spanning the financial year 2009–2022, we present several interesting results. First, we demonstrate that the risk of the firms eligible under mandatory CSR law is higher than those not qualifying. Second, we find that the risk of firms that focus their CSR on the local area's needs is lower. In an additional analysis, we demonstrate that firms with higher spending on the local area experience lower contingent liability, legal expenses, and audit fees. We illustrate that focusing on the local area's needs in CSR efforts retains signaling value under a mandatory CSR regime.

High-count-rate particle tracking in proton and carbon radiotherapy with Timepix2 operated in ultra-short acquisition time

This work investigates the operational acquisition time limits of Timepix3 and Timepix2 detectors operated in frame mode for high-count rate of high deposited energy transfer particles. Measurements were performed using alpha particles from a 241Am laboratory source and proton and carbon ion beams from a synchrotron accelerator. The particle count rate upper limit is determined by overlapping per-pixel particle signals, identifiable by the hits per pixel counter > 2, indicating the need to decrease acquisition time. On the other hand, the lower limit is the time required to collect the particle deposited charge while maintaining spectral properties. Different acquisition times were evaluated for an AdvaPIX Timepix3 detector (500 μm Silicon sensor) with standard per-pixelDigital to Analog Converter (DAC)settings and a Minipix Timepix2 detector (300 μm Silicon sensor) with standard and customized settings the pulse shaping parameter and threshold. For AdvaPIX Timepix3, spectra remained accurate down to 100 μs frame acquisition time; at 10 μs, loss of collected charge occurred, suggesting either avoiding this acquisition time or applying a correction. Timepix2 allowed acquisition times down to 100 ns for single particle track measurements even for high energy loss, enabled by a new Timepix2 feature delaying shutter closure until full particle charge collection. This work represents the first measurement utilizing Timepix-chips pixel detectors in an accelerator beam of clinical energy and intensity without the need to decrease the beam current. This is made possible by exploiting the short shutter feature in Timepix2 and a customized per-pixel energy calibration of the Timepix2 detector with a larger discharging signal value which allowed for shorterTime-over-Threshold (ToT)signal. These customized settings extend the operation of the pixel detectors to higher event rates up to 109 particles/cm2/s.

Diminished negative emotion regulation through affect labeling and reappraisal: insights from functional near infrared spectroscopy on lateral prefrontal cortex activation

BackgroundReappraisal, an emotion regulation strategy, includes reinterpretation and affect labeling involving verbalizing emotions. In general, reappraisal is supported by lateral prefrontal cortical regions, which are also known to underlie cognitive regulation. Other research has shown that affect labeling combined with reappraisal of negative emotions increases lateral prefrontal cortex activity more than reappraisal alone does, suggesting that affect labeling facilitates emotional regulation. However, the influence of affect labeling on the efficacy of reappraisal in reducing subjective negative emotions has not been determined.MethodsIn the experiment, 35 participants (mean age = 28.2 years (SD = 9.63); 12 women and 23 men) viewed vignettes that aroused negative emotion. Then, they rated subjective negative emotions as baseline values. Following the baseline rating, the task branched into four conditions, combining affect labeling and emotion regulation factors. In the affect-labeling factor, participants selected emotional labels consistent with their own emotions or not. Regarding the emotion regulation factor, participants engaged in reappraisal to regulate their negative emotions. Throughout the experiment, the intensity of negative emotions was measured three times, mirroring the baseline measurement. Oxyhemoglobin (OxyHb) signal values in prefrontal cortex regions during tasks were measured by functional near-infrared spectroscopy.ResultsDifferences between the subjective negative emotion ratings at baseline and after reappraisal indicated that reappraisal significantly reduced negative emotion with and without affect labeling (t (1173.05) = 29.97, p < 0.001), and the combination of affect labeling and reappraisal was less effective in regulating negative emotions at the subjective level than reappraisal without affect labeling (t (1172.03) = 3.15, p < 0.01). Additionally, there was an increase in OxyHb signal in the bilateral dorsolateral prefrontal and right ventral prefrontal cortices while participants performed reappraisal with affect labeling.ConclusionOur findings suggest that affect labeling, when performed prior to cognitive reappraisal, may influence the process of negative emotion regulation in complex ways. The interaction between affect labeling and reappraisal appears to modulate prefrontal cortex activity, potentially reflecting changes in cognitive processing during emotion regulation attempts. These results highlight the need for further investigation into the intricate relationship between different emotion regulation strategies.

A Low-Computational-Complexity Digital Predistortion Model for Wideband Power Amplifier.

This paper proposes a Composition Piecewise Memory Polynomial (CPMP) digital predistortion model based on a Vector Switched (VS) behavioral model to address the challenges of severe nonlinearity and strong memory effects in wideband power amplifiers (PAs). To tackle this issue, two thresholds are calculated and used to segment the envelope values of the input signal according to the nonlinear distortion characteristics of the PA. In this approach, a Generalized Memory Polynomial (GMP) model is employed for the lower segment, a Memory Polynomial (MP) model is employed for the middle segment, and a higher-order GMP model is employed for the upper segment. By sharing the fundamental MP among the proposed segmented models and leveraging a design methodology that configures different cross terms, memory depths, and polynomial orders for each segment, this model achieves superior linearization performance while simultaneously reducing the computational complexity associated with model extraction. The experimental results demonstrate that the adjacent channel power ratio (ACPR) of the predistorted PA output signal using the proposed model improves from -36 dBc to -54 dBc, matching the performance of the GMP model. Furthermore, this performance is 0.5 dBc better than the Piecewise Dynamic Deviation Reduction (PDDR) and Decomposed Vector Rotation (DVR) models. Notably, the complexity of the proposed parameter extraction process is 28.8% of the DVR model, 21.79% of the GMP model, and 12.83% of the PDDR model.

Implementing the procedure of measurement phase difference of the model signals using a virtual laboratory practicum

As this virtual laboratory practicum, which is a tool for mastering theoretical principles and practical skills in digital signal processing, was developed, users (mainly students) were given the possibility to parameterize harmonic signals. It includes the determination of the number of samples, sampling rate, amplitudes, initial phases, and other characteristics necessary for accurate metrological measurements. This publication presents the structure of the virtual workshop and the mathematical model underlying it, with an emphasis on aspects of analyzing the procedures for measuring the phase difference of harmonic signal models using the Discrete Fourier Transform. Particular attention is focused on the phase correction algorithm, which is aimed at improving the accuracy of measurements of the phase difference of signals. The presented methodology describes in detail the measurement process, estimation of errors arising from limited measurement time due to non-synchronization, the influence of the initial phase values of signals, and shows how the synchronization process contributes to a significant increase in the accuracy of measuring informative parameters.

Analysis of nerve excitability in the dentate gyrus of the hippocampus in cerebral ischaemia-reperfusion mice

Ischemic stroke often leads to cognitive dysfunction, which delays the recovery process of patients. However, its pathogenesis is not yet clear. In this study, the cerebral ischemia-reperfusion model was built as the experimental object, and the hippocampal dentate gyrus (DG) was the target brain area. TTC staining was used to evaluate the degree of cerebral infarction, and nerve cell membrane potentials and local field potentials (LFPs) signals were collected to explore the mechanism of cognitive impairment in ischemia-reperfusion mice. The results showed that the infarcted area on the right side of the brain of the mice in the model group was white. The resting membrane potential, the number of action potential discharges, the post-hyperpolarization potential and the maximum ascending slope of the hippocampal DG nerve cells in the model mice were significantly lower than those in the control group ( P < 0.01); the peak time, half-wave width, threshold and maximum descending slope of the action potential were significantly higher than those in the control group ( P < 0.01). The time-frequency energy values of LFPs signals in the θ and γ bands of mice in the ischemia and reperfusion groups were significantly reduced ( P < 0.01), and the time-frequency energy values in the reperfusion group were increased compared with the ischemia group ( P < 0.01). The signal complexity of LFPs in the ischemia and reperfusion group was significantly reduced ( P < 0.05), and the signal complexity in the reperfusion group was increased compared with the ischemia group ( P < 0.05). In summary, cerebral ischemia-reperfusion reduced the excitability of nerve cells in the DG area of the mouse hippocampus; cerebral ischemia reduced the discharge activity and signal complexity of nerve cells, and the electrophysiological indicators recovered after reperfusion, but it failed to reach the healthy state during the experiment period.

Real-time target recognition with all-optical neural networks for ghost imaging.

The generation and structural characteristics of random speckle patterns impact the implementation and imaging quality of computational ghost imaging. Their modulation is limited by traditional electronic hardware. We aim to address this limitation using the features of an all-optical neural network. This work proposes a real-time target recognition system based on an all-optical diffraction deep neural network for ghost imaging. We use a trained neural network to perform pure phase modulation on visible light, and directly complete the target recognition task by detecting the maximum value of light intensity signals at different positions. We optimized the system by simulating the effects of parameters, such as the number of layers of the network, photosensitive pixel, unit area etc., on the final recognition performance, and the accuracy of target recognition reached 91.73%. The trained neural network is materialised by 3D printing technology and experiments confirmed that the system successfully performs real-time target recognition at a low sampling rate of 1.25%. It also verified the feasibility and noise resistance of the system in practical application scenarios.

Investigating Potential Interactive Effects Between Limbal Rings and Facial Attractiveness

AbstractLimbal rings are dark bands in the eyes that circle the iris. Previous research suggests that the presence of limbal rings augments perceptions of male faces as healthy and attractive, particularly among female perceivers. Nonetheless, a degree of heterogeneity exists in attractiveness effects across different stimulus sets. One possibility of this discrepancy could be previously undetected differences in attractiveness across different social targets that impede a clearer understanding for the signal value of limbal rings. Namely, it could be possible that the attractiveness advantage of faces with limbal rings is most apparent among highly attractive stimuli. This study sought to reconcile this discrepancy by developing a novel stimulus set that systematically varies the attractiveness of social targets in addition to the presence of limbal rings in male faces. Women evaluated these targets based on perceptions of their health and attractiveness. Although limbal rings continued to augment perceptions of health in male faces, no differences emerged in the attractiveness of faces with and without limbal rings.

Development of an Algorithm for Determining the Class of an Obstacle in the Process of its Interaction with the Manipulator

The article discusses the problem of determining the nature of an obstacle using a force sensor for terrain mapping. Three classes of objects have been introduced: the first class is "static" obstacles, the second class is "moving" obstacles, the third class is "unstable" obstacles. To conduct experiments, a simulation system was built in the Gazebo modeling environment, including a manipulator with the ability to horizontally move a probe with a force sensor and a set of obstacles corresponding to three classes of obstacles. All experiments were carried out using the ROS framework, the Gazebo simulator and the Movelt manipulation software. Processing of signals from the force sensor showed that the sig­nals for the three classes of obstacles are different, which makes it possible to identify the class based on sensor readings. Based on the analysis of data received from the sensor, rules were formed that make it possible to determine the class of an obstacle during its interaction with the manipulator in real time. To determine the obstacle class, the values of the force sensor signals are considered, smoothed by the moving average method. According to the rules discussed in the article, an algorithm was developed to determine the class of obstacle, which was implemented in the Gazebo modeling environment. Testing of the algorithm showed the stability of its operation under the given experimental conditions. Successful determination of the obstacle class will allow one not only to construct a map of the area, but also to prevent damage to the device when exposed to a "static" object with excessive force. The presented approach can be expanded to narrower classes of obstacles that characterize objects that can be encountered by a mobile device when mapping a forest area.

A Machine Learning–Based Ergonomic Assessment of Wireless Hand Control System for Lower‐Limb Disabled Tractor Operators and Abled Female Agricultural Workers

ABSTRACTTractor being the most used power source for agricultural operations needs hand control (HC) and foot control (FC) to maneuver it. FCs restrict lower‐limb disabled agricultural workers from participating in tractor operation, and high requirement of actuation forces to operate FCs may create overexertion and early fatigue to female agricultural workers. Therefore, a sensor‐based HC system has been developed to assist them in tractor operation with minimal actuating force. This study focuses on ergonomic assessment of the HC system to assess the suitability for the abled and disabled agricultural workers, including physiological, psychophysical, and muscle fatigue parameters. Heart rate (HR) of abled male and female, and disabled male and female was observed in the range of 83–118, 85–117, 93–118, and 92–114 beats/min, respectively, during tractor operation. Energy expenditure rate (EER) during tractor operation with FCs (9.7–17.4 kJ/min) was observed higher than with the HC system (7.3–16.5 kJ/min). Body parts discomfort was observed highest for the right hand of all the subjects (4.9–5.3) and maximum overall discomfort was experienced by abled females during the operation with FCs (5.4) as they have to exert higher force. The root mean square (RMS) value of the electromyography signal obtained for extensor digitorum muscle was found to be higher for all the subjects and with both HC and FC (abled male, 17.37–40.43 µV; abled female, 14.76–45.29 µV; disabled male, 15.49–40.23 µV; disabled female, 30.32–54.29 µV) than other upper arm muscles middle deltoid, flexor carpi radialis, and brachioradialis. Muscle workload for all the selected muscles of all the subjects was observed within the recommended limit during the tractor operation with a developed HC system (&lt; 30%). Categorization of overall discomfort rating (ODR) of the subjects using HR, EER, and RMS through machine learning algorithms such as k‐nearest neighbor (KNN), random forest classifier, and support vector machine predicted the ODR with accuracies in the range of 77%–83%. KNN algorithm was found to be most accurate with prediction accuracy of 83%. The developed HC system provides assistantship to the lower‐limb disabled agricultural workers (1%–100% disability of lower limbs) and allows female workers to operate the tractor with minimal physical exertion.