Gait Motion Research Articles

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.

Human gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user's natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG. This study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle. The results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19. This study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

Intra- and interlimb effects of gait retraining in individuals with knee hyperextension

BackgroundGait retraining, which typically focuses on the most severely affected limb or joint, has shown promising results in treating faulty running and walking patterns. The closed-chain nature of gait during the stance phase may influence kinematic changes in the adjacent joints of the trained leg. In addition, the coupled nature of the lower extremity motion of gait suggests that changes in one leg may transfer to the other. This study aimed to assess the intra- and inter-limb transfer of kinematic changes following gait retraining to reduce knee extension in individuals with hyperextension walking patterns. MethodsSeventeen women with knee hyperextension gait patterns participated in six treadmill retraining sessions. All participants received verbal and real-time visual kinematic feedback in the form of knowledge of results. This intervention study took place at the Gait Analysis Laboratory at the University of Iowa. Mean peak sagittal-plane lower extremity joint kinematics during overground walking at pretraining, post-training, and 1- and 8-month follow-ups were calculated for analysis and comparisons. FindingsThe post-training changes in ankle range of motion returned to baseline values by the 8-month follow-up. There was a significant transfer effect of kinematic changes to the untrained knee following gait retraining. InterpretationTraining one knee did not result in long-term compensatory kinematic changes in the other joints. In addition, the improvements in knee extension range of motion were transferred to the untrained knee and retained at the 8-month follow-up. This study supports the use of gait retraining as an effective clinical intervention.

Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography

The joint moment is a key measurement in locomotion analysis. Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. However, even for similar gait motions, intersubject variance presents a significant challenge in maintaining reliable prediction performance. The optimal deep learning models for ankle moment prediction during dynamic gait motions remain underexplored for both intrasubject and intersubject usage. This study evaluates the feasibility of different deep-learning models for estimating ankle moments using sEMG data to find an optimal intrasubject model against the inverse dynamic approach. We verified and compared the performance of 1302 intrasubject models per subject on 597 steps from seven subjects using various architectures and feature sets. The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. They were then transferred to realize intersubject ankle moment estimation.

A Motion Planner Based on Mask-D3QN of Quadruped Robot Motion for Steam Generator.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierarchical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

Partial weight-bearing and range of motion limitation significantly reduce the loads at medial meniscus posterior root repair sutures in a cadaveric biomechanical model.

The aim of this study was to investigate the influence of medial meniscus posterior root avulsion (MMPRA) before and after surgical treatment on the biomechanics of the knee joint, including suture repair forces during daily and crutch-assisted gait movements. MMPRA were investigated in eight human cadaver knee joint specimens by a dynamic knee joint simulator with daily (normal gait, gait with additional rotational movement, standing up, sitting down) and rehabilitation-associated movements (crutch-assisted gait with limited flexion range of motion [30°] and 30% [toe-touch weight-bearing, TTWB] and 50% of body weight [partial weight-bearing, PWB]) with simulated physiologic muscle forces. Each specimen was tested in intact, torn and repaired (transtibial suture) state. The biomechanical parameters were: medial mean contact pressure and area, knee joint kinematics, medial displacement of the posterior meniscus horn and loading on the anchoring suture. Significant reduction of the contact area due to the avulsion was observed in all movements except for PWB and sitting down. MMPRA repair significantly increased the contact areas during all movements, bringing them to levels statistically indistinguishable from the initial state. MMPRA resulted in a medial displacement up to 12.8 mm (sitting down) and could be reattached with a residual displacement ranging from 0.7 mm (PWB) to 5.7 mm (standing up), all significantly (p < 0.001) reduced compared to the torn state. The mean peak anchoring suture load increased from TTWB (77 N), PWB (91 N) to normal gait (194 N), gait rotation (207 N), sitting (201 N; p < 0.01) and to standing up (232 N; p = 0.03). Surgical treatment of MMPRA allows restoration of physiological knee joint biomechanics. Crutch-assisted movements reduce the loading of the repair suture, thus likewise the risk for failure. From a biomechanical point of view, crutch-assisted movements are recommended for the early rehabilitation phase after MMPRA repair. Level V.

Electrohydraulic musculoskeletal robotic leg for agile, adaptive, yet energy-efficient locomotion

Robotic locomotion in unstructured terrain demands an agile, adaptive, and energy-efficient architecture. To traverse such terrains, legged robots use rigid electromagnetic motors and sensorized drivetrains to adapt to the environment actively. These systems struggle to compete with animals that excel through their agile and effortless motion in natural environments. We propose a bio-inspired musculoskeletal leg architecture driven by antagonistic pairs of electrohydraulic artificial muscles. Our leg is mounted on a boom arm and can adaptively hop on varying terrain in an energy-efficient yet agile manner. It can also detect obstacles through capacitive self-sensing. The leg performs powerful and agile gait motions beyond 5 Hz and high jumps up to 40 % of the leg height. Our leg’s tunable stiffness and inherent adaptability allow it to hop over grass, sand, gravel, pebbles, and large rocks using only open-loop force control. The electrohydraulic leg features a low cost of transport (0.73), and while squatting, it consumes only a fraction of the energy (1.2 %) compared to its conventional electromagnetic counterpart. Its agile, adaptive, and energy-efficient properties would open a roadmap toward a new class of musculoskeletal robots for versatile locomotion and operation in unstructured natural environments.

Parkinsonian gait modelling from an anomaly deep representation

AbstractParkinson’s Disease (PD) is associated with gait movement disorders, such as bradykinesia, stiffness, tremors and postural instability. Hence, a kinematic gait analysis for PD characterization is key to support diagnosis and to carry out an effective treatment planning. Nowadays, automatic classification and characterization strategies are based on deep learning representations, following supervised rules, and assuming large and stratified data. Nonetheless, such requirements are far from real clinical scenarios. Additionally, supervised rules may introduce bias into architectures from expert’s annotations. This work introduces a self-supervised generative representation to learn gait-motion-related patterns, under the pretext task of video reconstruction. Following an anomaly detection framework, the proposed architecture can avoid inter-class variance, learning hidden and complex kinematics locomotion relationships. In this study, the proposed model was trained and validated with an owner dataset (14 Parkinson and 23 control). Also, an external public dataset (16 Parkinson, 30 control, and 50 Knee-arthritis) was used only for testing, measuring the generalization capability of the method. During training, the method learns from control subjects, while Parkinson subjects are detected as anomaly samples. From owner dataset, the proposed approach achieves a ROC-AUC of 95% in classification task. Regarding the external dataset, the architecture evidence generalization capabilities, achieving a 75% of ROC-AUC (shapeness and homoscedasticity of 66.7%), without any additional training. The proposed model has remarkable performance in detecting gait parkinsonian patterns, recorded in markerless videos, even competitive results with classes non-observed during training.

Modulating Perceived Authority and Warmth of Mobile Social Robots Through Bodily Openness and Vertical Movement in Gait

Modulating Perceived Authority and Warmth of Mobile Social Robots Through Bodily Openness and Vertical Movement in Gait

Relationship between foot and ankle tendinopathies and dyslipidemia: a literature review

Objectives: Dyslipidemia is a multifactorial condition related to genetic factors, dietary patterns, sedentary habits, and socioeconomic conditions. Foot and ankle tendinopathies are a common problem in orthopedic consultations and can represent up to 30%, highlighting some risk factors, such as metabolic disorders. Methods: This study is an integrative review of the literature addressing the correlation between foot and ankle tendinopathy and dyslipidemia. The keywords used were: “tendinopathy,” “foot and ankle,” and “dyslipidemia,” with the operators “AND” and “OR” for the search. The following inclusion criteria were established: case reports, cohort studies, case and control studies, clinical trials, and biomechanical studies published between 2013 and 2024, indexed in the following databases: Pubmed (Medline), Scielo, Lilacs, and Scopus, published in English, Spanish, and Portuguese.Results: Several mechanisms have been proposed to explain the correlation between tendinopathies and dyslipidemia, although there is still no absolute clarity; it was shown that the Achilles tendon is the main tendon affected in the foot and ankle, especially by xanthomas, which lead to an increase in the area size, causing pain, edema, difficulty in movement and changes in gait. Conclusion: It is believed that females with dyslipidemia, elderly patients, and individuals with a body mass index below 18.5kg/m2 are more prone to tendinopathies. Level of evidence II; Diagnostic studies.

Bio-Inspired Multimodal Motion Gait Control of Snake Robots with Environmental Adaptability Based on ROS

Bio-Inspired Multimodal Motion Gait Control of Snake Robots with Environmental Adaptability Based on ROS

Self-Powered Wearable Pressure Sensor System for Smart Healthcare Applications

Wearable sensors mark a groundbreaking advancement in the realm of health monitoring technology. However, traditional wearable devices lack comprehensive solutions for tracking sports exercise behaviors and providing feedback on rehabilitation for those undergoing home-based recovery. To bridge this gap, a self-powered triboelectric nanogenerator (TENG)-based wearable sensing system has been developed with in-house monitoring capabilities. The wearable sensor system is composed of triboelectric pressure sensors with an electro-conductive silicone sheet and patterned Ecoflex film as the contact layers. Highly sensitive TENG sensors have been successfully embedded into a bicycle saddle and human shoe insole for real-time pressure monitoring. Experimental results demonstrate that biomechanical energy, originating from both the pelvic-saddle interactions on bicycle and human gait movements, has been successfully converted into electrical energy. The generated sensor voltage remains stable under frequency variations and external environmental conditions, such as temperature and humidity variations. Utilizing distinctive information contained in individual body mass imbalances and gait characteristics, a smart healthcare monitoring system is envisioned to extract essential biomechanical insights via artificial intelligence, facilitating a pivotal role in in-house monitoring and tracking of sports rehabilitation exercises. The as-designed low-cost, highly scalable self-powered wearable sensor system offers an accessible, user-friendly point-of-care solution for gait detection and rehabilitation monitoring for individuals with various physical impairments, with considerable potential for commercialization. Figure 1

Deep motion estimation through adversarial learning for gait recognition

Gait recognition is a form of identity verification that can be performed over long distances without requiring the subject’s cooperation, making it particularly valuable for applications such as access control, surveillance, and criminal investigation. The essence of gait lies in the motion dynamics of a walking individual. Accurate gait-motion estimation is crucial for high-performance gait recognition. In this paper, we introduce two main designs for gait motion estimation. Firstly, we propose a fully convolutional neural network named W-Net for silhouette segmentation from video sequences. Secondly, we present an adversarial learning-based algorithm for robust gait motion estimation. Together, these designs contribute to a high-performance system for gait recognition and user authentication. In the experiment, two datasets, i.e., OU-IRIS and our own dataset, are used for performance evaluation. Experimental results show that, the W-Net achieves an accuracy of 89.46% in silhouette segmentation, and the proposed user-authentication method achieves over 99.6% and 93.8% accuracy on the two datasets, respectively.

A Study on the Design of Knee Exoskeleton Rehabilitation Based on the RFPBS Model.

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior-Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction.

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

The problem of predicting Freeze of Gait (FoG) on Parkinson diseased patients has been well studied. There exists number of approaches in predicting FoG, which uses sensory features, EEG data and so on. However, the methods suffer to achieve higher performance. To handle this issue, an efficient Time Orient Acceleration Gait pattern based FoG prediction model (TOAGP-FoG) is presented in this paper. The model designed to attach accelerometer sensors at different ankle and joints of the body. The sensor signals are recorded at different gait movement in long term. The sensory signals are passed to the central data server which tracks the movement signals. With the time variant signals stored by the model, the method generates Acceleration Gait Pattern with number of features. At each movement, the method analyses the patterns to compute FOG Risk Support (FoGRS) towards various gait movement. The FoG Risk Support is measured according to the movement forces produced by the patient for various gait movement in different time stamp and computes minimum gait force to be produced. Based on the FoGRS value, the method performs FoG prediction. The proposed method improves the performance of FoG prediction with higher accuracy. Other notable aspects of the suggested model include comparable performance, resiliency, real-time prediction capabilities, FOG-specific integration of data, and advanced deep learning methodologies for accurate prediction. The Special Features of TOAGP-FoG include the Multi-Sensor Configuration, Temporal Analysis, Adaptive Thresholding, Dynamic FoG Risk Support (FoGRS), and Enriched Feature Extraction. The TOAGP-FoG model offers an important breakthrough in the predictive modelling of Parkinson's disease since it integrates several features such as temporal flexibility, dynamic FoGRS computation, adaptive thresholding, enriched feature extraction, and multi-sensor configurations.

Self-weight utilization harvester oriented to low-frequency gait for human health monitoring and assistive training

The mechanical energy generated during human daily activities holds the potential as a viable energy source for health monitoring and assistive devices. However, the vibrational energy generated from human walking is difficult to harvest due to the characteristics of low-frequency, especially considering the exemption of the additional walking burden. This work introduces an energy harvesting system incorporating an innovative spiral-spring and flywheel combination based on self-weight utilization strategy, alongside an excitation matching methodology to achieve efficient energy conversion. By utilizing a smart plucking structure with a piezoelectric cantilever beam to match the excitation displacement and force with the locomotion in the leg-stretching phase. The system can obtain a power output of 38.07mW, and 58.62mW at walking speeds of 1.0 m/s, and 3.0 m/s without imposing additional burden on the human by self-weight utilization strategy. Moreover, it can provide assistive force for gait movement due to the unique plucking structure realizing asymmetric loading style. The system showcases its potential as a lightweight, cost-effective, and ergonomic solution for the needs of adults requiring assistive training and health monitoring.

Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

BackgroundIn post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.MethodsTen participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson’s correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.ResultsParietal–frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.ConclusionsIn this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

Gait Recognition Under Different Clothing Conditions Via Deterministic Learning

In real-world scenarios, the clothing difference constitutes one of the most common covariate factors that can affect the performance of gait recognition systems. This paper proposes a gait recognition method which is invariant to maximum number of clothing conditions. First, four kinds of timevarying silhouette features are selected to capture the spatiotemporal characteristics of gait motion. Second, frame-to-frame gait dynamics underlying different individuals ʼ gait features is effectively modeled by radial basis function ( RBF ) neural networks through deterministic learning. Gait patterns are represented as the gait dynamics underlying time-varying gait features. This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions. In order to eliminate the effect of clothing differences, the training patterns under different clothing conditions further constitute a uniform training dataset, containing all kinds of gait dynamics under different clothing conditions. A rapid recognition scheme is presented on published gait databases. Extensive experiments demonstrate the efficacy of the proposed method.

Effect of manual manipulation on mechanical gait parameters.

A variety of manual manipulation techniques are utilized in clinical practice to alleviate pain and improve musculoskeletal function. Many manual practitioners analyze gait patterns and asymmetries in their assessment of the patient, and an increasing number of gait motion capture studies are taking place with recent improvements in motion capture technology. This study is the first systematic review of whether these manual modalities have been shown to produce an objectively measurable change in gait mechanics. This study was designed to perform a systematic review of the literature to assess the impact of manual medicine modalities on biomechanical parameters of gait. A master search term composed of keywords and Medical Subject Headings (MeSH) search terms from an initial scan of relevant articles was utilized to search six databases. We screened the titles and abstracts of the resulting papers for relevance and then assessed their quality with the Cochrane Risk of Bias Tool. Clinical trials that featured both a manual manipulation intervention and multiple mechanical gait parameters were included. Case reports and other studies that only measured gait speed or other subjective measures of mobility were excluded. We included 20 studies in our final analysis. They utilize manipulation techniques primarily from osteopathic, chiropractic, massage, and physiotherapy backgrounds. The conditions studied primarily included problems with the back, knee, and ankle, as well as healthy patients and Parkinson's patients. Control groups were highly variable, if not absent. Most studies measured their gait parameters utilizing either multicamera motion capture systems or force platforms. Twelve of 20 papers included in the final analysis demonstrated a significant effect of manipulation on gait variables, many of which included either step length, walking speed, or sagittal range of motion (ROM) in joints of the lower extremity. However, the results and study design are too heterogeneous to draw robust conclusions from these studies as a whole. While there are initial indications that certain modalities may yield a change in certain gait parameters, the quality of evidence is low and there is insufficient evidence to conclude that manual therapies induce changes in biomechanical gait parameters. Studies are heterogeneous with respect to the populations studied and the interventions performed. Comparators were variable or absent across the studies, as were the outcome variables measured. More could be learned in the future with consistent methodology around blinding and sham treatment, and if the gait parameters measured were standardized and of a more robust clinical significance.

Gait-Based Human Activity Recognition Using Efficient Sensor Fusion and Deep Learning Approach

Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. A novel feature selection approach is then proposed in order to select a subset of discriminant features, construct an online activity recognizer with better generalization ability, and reduce the smartphone power consumption. Experimental results on a publicly available data set show that the fusion of both accelerometer and gyroscope data contributes to obtain better recognition performance than that of using single source data, and that the proposed feature selector outperforms three other comparative approaches in terms of four performance measures. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. The aim of paper is to explore real life applications like contactless employee recognition system using gait analysis which uses sensor data as base to identify employees based on their gait movement. This requires understanding the dimensions of sensor data and its application exploring other potential real-life applications and optimizing the methodology are also one of the core objectives.