Browsing by Author "Das, B."

Now showing 1 - 4 of 4

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.
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.
Tunable dual color emission from the opposite faces of silicon nanoparticle embedded gel-glass
(Elsevier B.V., 2023) Das, B.; Hossain, S.M.; Mohanraj, G.T.; Chowdhury, S.R.; Siddique, A.B.; Rahman, M.R.; Ray, M.
A luminescent silicon nanoparticle embedded gel-glass, prepared by room temperature hydrolysis and reduction of aminosilane, exhibits intriguing dual photoluminescence (PL) from opposite faces of the glass. The face, which is excited with UV, exhibits excitation energy dependent blue-green emission. As the excitation energy is varied from 350 nm to 450 nm the PL peaks shift from 435 nm to 506 nm. The opposite surface, on the other hand emits nearly excitation independent green light – the PL peak shifts by ∼17 nm as the excitation energy is varied from 350 nm to 450 nm. The luminescent properties provide interesting insights into the light emission mechanism from nanostructured silicon. Spectral filtering by reabsorption and photon reabsorption-reemission in a size distributed nanoparticle system having different optical gaps play a combined role in the observed dual emission. We show that the dual emission can be tuned by simply varying the thickness of the glass. Such dual emission renders the luminescent glass amenable for several applications as a novel solid state display material. © 2023 Elsevier B.V.