Browsing by Author "Verma, K."

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A novel mechanism to support the sit-to-stand and squat-to-stand physical training for rehabilitation purposes
(Springer Nature, 2025) Suman, S.K.; Verma, K.
In patients with neurological impairment, muscles become stiff, and the joint range of motion (ROM) is restricted. Physical rehabilitation training is required if they cannot perform tasks like sit-to-stand motion. The existing devices support the trunk with limited degrees of freedom (DOF) and fixed shank pad support that arrests ankle, knee, and hip joint ROM and its associated muscle activations. The manual transfer increases falls and the risk of lower back injury. This study proposes a mechanism that supports the shank and trunk by moving assistance in joint ROM. Natural sit-to-stand (STS) and squat-to-stand motion experiments were conducted in the motion capture system with twenty healthy participants. For Inverse kinematic and motion event/phases analysis, Visual 3D biomechanics software was used. From inverse kinematics, trunk & shank angle, velocity, and hip joint position were calculated. A 3-DOF mechanism design consisting of 13 links and 17 joints is proposed based on the inverse kinematics analysis of squat-to-stand movements that accommodate the STS joint ROM. A CAD model of the mechanism is created and imported into Simscape Multibody Dynamic (MATLAB 2023b) software for simulation. Simulated angular velocity and displacement of the trunk and shank compared with experimental data. The proposed mechanism facilitates physical rehabilitation training and reduces the physical burden on caregivers, nurses, and physiotherapists. The 3-DOF assisting trunk support provides coordination with the lower limb to follow the natural path. It includes toileting facility transfer due to its motion up to squat position. Safe transfer from hospital beds to wheelchairs. © The Author(s) 2025.
Comparison of Stress Distribution of Graphene-Based Bioactive Material for Zirconia and Titanium by Applying Orthotropic Properties: A Finite Element Analysis
(Springer Science and Business Media Deutschland GmbH, 2024) Singh, R.K.; Verma, K.; Kumar, G.C.
This study employs finite element analysis to examine stress distribution at the boneâ€“implant interface in graphene-based dental implants. Four implant models, encompassing titanium and zirconia with and without graphene coating, are assessed under axial and oblique loading. Considering their anisotropic nature, bone tissues are simulated as orthotropic, while implants are treated as homogeneous and isotropic. The study utilizes one-way ANOVA and Kruskalâ€“Wallis tests for statistical analysis to compare stress distribution among implant groups. Results indicate superior von Mises stress distribution in graphene-based implants (A2 and A4) compared to the pure material group. The incorporation of graphene coating significantly reduces implant stresses under axial and oblique loads compared to titanium and zirconia. In conclusion, the study underscores the potential benefits of graphene-based implant models in optimizing stress distribution at the boneâ€“implant interface, emphasizing the importance of suitable implant models and biomaterial selection for enhanced dental implant performance. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Development of a Rack-and-Pinion Mechanism with Arduino-Based PID Control System for a Continuous Passive Motion Device in Knee Rehabilitation
(Institute of Electrical and Electronics Engineers Inc., 2024) Sholapurkar, S.U.; Chanda, S.; Verma, K.; Shivananda Nayaka, H.; Das, B.
This paper outlines the design and prototyping methodology for a simple, cost-effective device intended for Continuous Passive Motion (CPM) therapy, specifically for knee rehabilitation. The device is designed to assist patients in recovery by providing controlled, repetitive motion to the knee joint without requiring active participation. Its core mechanism utilizes a rack-and-pinion system, which converts rotational motion into linear motion, ensuring smooth and consistent lower limb movement. A PID controller has been implemented using an Arduino Mega microcontroller to provide precise control over the motion. This setup allows for accurate adjustments to both position and speed, aligning with the therapeutic requirements for effective rehabilitation. Key success factors and failure points are also highlighted, providing valuable insights for future improvements and development. Â© 2024 IEEE.
Effect of trunk angle on lower limb joint moment in different strategies of sit-to-stand-to-sit motion with healthy subjects
(Springer Science and Business Media Deutschland GmbH, 2025) Suman, S.K.; Verma, K.
Patients with lower limb impairments often face sit-to-stand-to-sit motion challenges. The patients utilize a greater trunk flexion angle at seat-off time to mitigate knee moment. Alternative methods of STSTS motion strategies are required to study and understand the various patterns to guide physical rehabilitation programs in clinical practice. Four different STSTS strategies—Natural, Full Flexion, Pelvis-spine alignment, and Frame-Assisted—were experimented with twenty healthy subjects in a 3D motion capture lab, and inverse kinematics and dynamics methods were used for motion analysis in Visual 3D. At seat-off time in full flexion, the maximum trunk flexion angle is 58.77(± 17.92) degrees, duration is 1.63 s, 27% of the cycle, which reduces knee moment by -0.466(± 0.2) N.m/kg, increased hip moment by 0.67(± 0.312) N.m/kg, and ankle moment by 0.225(± 0.09) N.m/kg for the compensation. The compensatory movement also occurred while sitting down. Frame-assisted STSTS motion reduced knee moments without increases in hip and ankle moments at the maximum of trunk flexion angle while standing and sitting, and its motion patterns are similar to pelvis-spine alignment and natural strategies. These findings provide valuable insights for physiotherapists to predict the current stage of the patient for clinical assessment and guide in the design and development of medical devices. © International Federation for Medical and Biological Engineering 2025.
Flatfoot Detection in an Indian Population: Validation of Morphological Indices Using a Diagnostic Device †
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Kalghatgi, K.; Verma, K.; Das, B.
Flatfoot, or pes planus, is a condition where the foot’s arch collapses, leading to complications such as pain, gait abnormalities, and an increased risk of injury. Accurate and early diagnosis is critical for effective treatment. Traditional diagnostic methods, including radiographic imaging, footprint analysis, and plantar pressure measurement, often require specialized equipment and are subjective. This study proposes a novel diagnostic device that captures 2D plantar foot images to calculate key morphological indices, including the Staheli Index, Clark’s Angle, and Chippaux–Smirak Index, for flatfoot detection. The device, designed with off-the-shelf components, includes a transparent toughened glass platform and LED illumination to capture images using web cameras. A Python-based application was developed for image acquisition, segmentation, and stitching. The device was tested on 55 participants aged 18–28, and the extracted morphological indices were validated against established thresholds for flatfoot diagnosis. The results showed that the Staheli Index, Chippaux–Smirak Index, and Clark’s Angle reliably detected flatfoot in participants. The study highlights the potential of this device for non-invasive, accurate, and rapid flatfoot diagnosis. Future advancements in deep learning could enhance its capabilities, making it a valuable tool for proactive healthcare in foot deformity detection. © 2025 by the authors.
Importance of Knee Angle and Trunk Lean in the Detection of an Abnormal Walking Pattern Using Machine Learning
(Springer Science and Business Media Deutschland GmbH, 2023) Pandit, P.; Thummar, D.; Verma, K.; Gangadharan, K.V.; Das, B.; Kamat, Y.
Human gait can be quantified using motion capture systems. Three-dimensional (3D) gait analysis is considered the gold standard for gait assessment. However, the process of three-dimensional analysis is cumbersome and time-consuming. It also requires complex software and a sophisticated environment. Hence, it is limited to a smaller section of the population. We, therefore, aim to develop a system that can predict abnormal walking patterns by analyzing trunk lean and knee angle information. A vision-based OpenPose algorithm was used to calculate individual trunk lean and knee angles. Web applications have been integrated with this algorithm so that any device can use it. A Miqus camera system of Qualisys 3D gait analysis system was used to validate the OpenPose algorithm. The validation method yielded an error of Â± 9Â° in knee angle and Â± 8Â° in trunk lean. The natural walking pattern of 100 healthy individuals was compared to simulated walking patterns in an unconstrained setting in order to develop a machine learning program. From the collected data, an RNN-based LSTM machine learning model was trained to distinguish between normal and abnormal walkings. LSTM-based models were able to distinguish between normal and abnormal gaits with an accuracy of 80%. This study shows that knee angle and trunk lean patterns collected during walking can be significant indicators of abnormal gait. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Investigation of lumbar and cervical lordosis variation in sit-to-stand-to-sit movement with different strategies in healthy subjects
(SAGE Publications Ltd, 2025) Suman, S.K.; Verma, K.
Background: Due to lower limb impairment, people use greater trunk flexion strategies and cannot maintain the alignment of the upper body, leading to loss of lordosis over time. Objective: A comprehensive study is needed to understand the heightened trunk flexion effect on lumbar and cervical lordosis and associated joint moments. Methods: The three sit-to-stand-to-sit strategies, Natural, Full trunk flexion, and Pelvis-spine alignment, were conducted in 3D motion capture. The hypothesis is that increasing the total lumbar and cervical lordosis depth will reduce the total lumbosacral and cervicothoracic joint moment. Using Visual 3D, inverse kinematics and dynamics for joint moments and angles of the head, trunk, and pelvis at five events/phases, and the corresponding lordosis depth was calculated. Results: Pelvis-spine alignment strategies show a strong positive correlation (r?=?0.70) between the total depth of lordosis and reducing the lumbosacral and cervicothoracic joint moment. The full flexion strategy mirrored the compensatory movement with a negative correlation (r?=??0.88) on the reduction of lordosis depth and compensated by increasing the cervical lordosis depth. Conclusions: These findings guide the correcting of spine disorders, the development of physical rehabilitation programs, the design of devices, and the correctness of posture to prevent low back pain and disease progression. © The Author(s) 2025
Maximizing Performance and Efficiency: An Algorithm Approach to Engine Sensor Optimization using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2024) Varma, V.; Verma, K.; Mehta, H.; Gangadharan, K.V.
This paper presents an algorithmic methodology developed to reduce the number of sensors required in automotive engines by leveraging machine learning techniques. The sensor data used in this technique was obtained from a standard engine, which exhibited redundancy in data. The high cost associated with sensors and their integration into engine systems necessitates an efficient approach to optimize sensor utilization while maintaining reliable engine performance. By utilizing advanced data analysis and predictive modeling, our algorithm aims to identify redundant or non-critical sensors, enabling a streamlined and cost-effective sensor configuration. We achieved this by developing a tailored dimensionality reduction algorithm based on functional dependency theory. This approach transforms data from a high-dimensional space into a lower-dimensional space, preserving essential features of the original data and ideally approximating its intrinsic dimension. Â© 2024 IEEE.
Optimizing dental implant design parameters through orthotropic properties of bone: a DOE-based approach
(Springer-Verlag Italia s.r.l., 2025) Singh, R.K.; Verma, K.; Kumar, G.C.; Doddamani, S.
Dental implant research has provided insights into the effects of thread design and occlusal loading rate on stress distribution within implants and adjacent bone structures. However, ongoing advancements in materials necessitate further investigation to optimize implant performance through a thorough understanding of design parameters. This study developed a comprehensive three-dimensional CAD model of dental implants, incorporating cortical and cancellous bone, crown, and various thread types (V type, buttress, and trapezoidal threads). Multiple thread design parameters (pitch, length, angle, and depth) were varied to analyze their impact on stress distribution. Taguchi's design of experiments, combined with finite element analysis, was employed to explore stress distribution around dental implants. The implant material used was Ti6Al7Nb alloy, comprising 90% titanium, 6% aluminium, and 7% niobium. Von Mises stresses were compared to identify the optimal design. Taguchi's analysis revealed that raising all parameters except pitch reduced implant stress. However, for trapezoidal and buttress designs, increasing pitch resulted in higher stress levels. A confirmation experiment, utilizing the developed regression equation, validated these findings. Comparative analysis between simulation and statistical results showed a close match across all cases; with an error rate of less than 10%. These findings underscore the reliability and accuracy of the research outcomes, emphasizing the significance of identified thread types and their impacts on implant stress. Further research in this area could lead to advancements in dental implant design, enhancing patient outcomes and implant longevity. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025.
Potential of Graphene-Functionalized Polymer Surfaces for Dental Applications: A Systematic review
(Taylor and Francis Ltd., 2025) Singh, R.K.; Verma, K.; Kumar, G.C.M.; Jalageri, M.B.
Graphene, a two-dimensional carbon nanomaterial, has garnered widespread attention across various fields due to its outstanding properties. In dental implantology, researchers are exploring the use of graphene-functionalized polymer surfaces to enhance both the osseointegration process and the long-term success of dental implants. This review consolidates evidence from in-vivo and in-vitro studies, highlighting grapheneâ€™s capacity to improve bone-to-implant contact, exhibit antibacterial properties, and enhance mechanical strength. This research investigates the effects of incorporating graphene derivatives into polymer materials on tissue response and compatibility. Among 123 search results, 14 articles meeting the predefined criteria were analyzed. The study primarily focuses on assessing the impact of GO and rGO on cellular function and stability in implants. Results indicate promising improvements in cellular function and stability with the use of GO-coated or composited implants. However, it is noted that interactions between Graphene derivatives and polymers may alter the inherent properties of the materials. Therefore, further rigorous research is deemed imperative to fully elucidate their potential in human applications. Such comprehensive understanding is essential for unlocking the extensive benefits associated with the utilization of Graphene derivatives in biomedical contexts. Â© 2024 Informa UK Limited, trading as Taylor & Francis Group.
Potential of Graphene-Functionalized Polymer Surfaces for Dental Applications: A Systematic review
(Taylor and Francis Ltd., 2025) Singh, R.K.; Verma, K.; Kumar, G.C.; Jalageri, M.B.
Graphene, a two-dimensional carbon nanomaterial, has garnered widespread attention across various fields due to its outstanding properties. In dental implantology, researchers are exploring the use of graphene-functionalized polymer surfaces to enhance both the osseointegration process and the long-term success of dental implants. This review consolidates evidence from in-vivo and in-vitro studies, highlighting graphene’s capacity to improve bone-to-implant contact, exhibit antibacterial properties, and enhance mechanical strength. This research investigates the effects of incorporating graphene derivatives into polymer materials on tissue response and compatibility. Among 123 search results, 14 articles meeting the predefined criteria were analyzed. The study primarily focuses on assessing the impact of GO and rGO on cellular function and stability in implants. Results indicate promising improvements in cellular function and stability with the use of GO-coated or composited implants. However, it is noted that interactions between Graphene derivatives and polymers may alter the inherent properties of the materials. Therefore, further rigorous research is deemed imperative to fully elucidate their potential in human applications. Such comprehensive understanding is essential for unlocking the extensive benefits associated with the utilization of Graphene derivatives in biomedical contexts. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach
(Gruppo Italiano Frattura, 2025) Singh, R.K.; Verma, K.; Kumar, G.C.
This research examines the mechanical characteristics of polymer composites (PMMA) that are reinforced with Hydroxyapatite (HAp), with a particular emphasis on the Elastic Modulus and Compressive Strength. The investigation employs a multifaceted approach that integrates experimental methods, micromechanical analysis, and machine learning techniques. Experimental assessments of Elastic Modulus and Compressive Strength were conducted at various HAp concentrations (5%, 15%, and 30%) and were compared with theoretical predictions derived from Representative Volume Element (RVE) and micromechanical frameworks, including Voigt and Reuss bounds. Various machine learning algorithms, such as Feedforward Neural Network (FFNN), Radial Basis Neural Network (RBNN), and Support Vector Machine (SVM), were used to predict the mechanical properties. The RBNN exhibited high accuracy (R² = 0.92; MAE = 0.05) for intermediate HAp levels (20-30%) but displayed instability at the extremes % of reinforcements values . The FFNN consistently provided lower estimates of the properties, whereas the SVM yielded robust and stable predictions that closely matched both experimental and theoretical results with the error of (2-5) % (Result value). This research highlights the effectiveness of integrating micromechanical modeling with machine learning to improve the prediction and comprehension of composite behavior, thereby offering valuable insights for the design and application of advanced materials. © 2025, Fracture Structural Integr. All rights reserved.