Faculty Publications
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Item Miners’ return to work following injuries in coal mines; Powrót do pracy górników poszkodowanych w wypadkach w kopalni w?gla(Nofer Institute of Occupational Medicine ul. sw. Teresy od Dzieciatka Jezus 8 Lodz 91-348, 2016) Bhattacherjee, A.; Kunar, B.M.Background: The occupational injuries in mines are common and result in severe socio-economical consequences. Earlier studies have revealed the role of multiple factors such as demographic factors, behavioral factors, health-related factors, working environment, and working conditions for mine injuries. However, there is a dearth of information about the role of some of these factors in delayed return to work (RTW) following a miner’s injury. These factors may likely include personal characteristics of injured persons and his or her family, the injured person’s social and economic status, and job characteristics. This study was conducted to assess the role of some of these factors for the return to work following coal miners’ injuries. Material and Methods: A study was conducted for 109 injured workers from an underground coal mine in the years 2000-2009. A questionnaire, which was completed by the personnel interviews, included among others age, height, weight, seniority, alcohol consumption, sleeping duration, presence of diseases, job stress, job satisfaction, and injury type. The data was analyzed using the Kaplan-Meier estimates and the Cox proportional hazard model. Results: According to Kaplan-Meier estimate it was revealed that a lower number of dependents, longer sleep duration, no job stress, no disease, no alcohol addiction, and higher monthly income have a great impact on early return to work after injury. The Cox regression analysis revealed that the significant risk factors which influenced miners’ return to work included presence of disease, job satisfaction and injury type. Conclusions: The mine management should pay attention to significant risk factors for injuries in order to develop effective preventive measures. © 2016, Nofer Institute of Occupational Medicine. All rights reserved.Item Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease(Elsevier B.V., 2017) Singh, B.; Jana, S.K.; Ghosh, N.; Das, S.K.; Joshi, M.; Bhattacharyya, P.; Chaudhury, K.Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. 1H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and L-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising. © 2016 Elsevier B.V.Item Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population(Springer London, 2018) Areeckal, A.S.; Jayasheelan, N.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Summary: We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. Introduction: We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Methods: Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. Results: In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. Conclusion: An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis. © 2017, International Osteoporosis Foundation and National Osteoporosis Foundation.Item Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data(Elsevier Ltd, 2018) Areeckal, A.S.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. © 2018 Elsevier LtdItem Workplace Spirituality and Subjective Happiness Among High School Teachers: Gratitude As A Moderator(Elsevier Inc. usjcs@elsevier.com, 2019) Mahipalan, M.; Sheena, u.Background and Objective: Spirituality and well-being are two constructs related to optimal levels of human functioning. This study attempts to link the concept of spirituality at work and subjective happiness, which is a facet of well-being. The role of grateful disposition is also examined by incorporating gratitude as a moderator Method: Data were collected using a structured questionnaire from high school teachers working with government schools in the southern region of India. Hypothesised relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) Results: Results reveal significant relationships between workplace spirituality, subjective happiness, and gratitude. Gratitude was also found to be a significant moderator, which exercised a positive influence on the workplace spirituality-happiness relationship. Conclusion: The study has contributed to the evolving literature on workplace spirituality by elucidating its relationship with positive psychology. Workplace spirituality could play a significant role in building substantial happiness for individuals in the long run. © 2018 Elsevier Inc.Item Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses(Elsevier Ltd, 2019) Powar, O.S.; Chemmangat, K.Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60 ± 9%, 70 ± 8%, and 60 ± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method. © 2019 Elsevier LtdItem Evaluation of implant properties, safety profile and clinical efficacy of patient-specific acrylic prosthesis in cranioplasty using 3D binderjet printed cranium model: A pilot study(Churchill Livingstone, 2021) Basu, B.; Bhaskar, N.; Barui, S.; Sharma, V.; Das, S.; Govindarajan, N.; Hegde, P.; Perikal, P.J.; Antharasanahalli Shivakumar, M.; Khanapure, K.; Jagannatha, A.There exists a significant demand to develop patient-specific prosthesis in reconstruction of cranial vaults after decompressive craniectomy. we report here, the outcomes of an unicentric pilot study on acrylic cranial prosthesis fabricated using a 3D printed cranium model with its clinically relevant mechanical properties. Methods: The semi-crystalline polymethyl methacrylate (PMMA) implants, shaped to the cranial defects of 3D printed cranium model, were implanted in 10 patients (mean age, 40.8 ± 14.8 years). A binderjet 3D printer was used to create patient-specific mould and PMMA was casted to fabricate prosthesis which was analyzed for microstructure and properties. Patients were followed up for allergy, infection and cosmesis for a period of 6 months. Results: As-cast PMMA flap exhibited hardness of 15.8 ± 0.24Hv, tensile strength of 30.7 ± 3.9 MPa and elastic modulus of 1.5 ± 0.1 GPa. 3D microstructure of the semi-crystalline acrylic implant revealed 2.5–15 µm spherical isolated pores. The mean area of the calvarial defect in craniectomy patients was 94.7 ± 17.4 cm2. We achieved a cranial index of symmetry (CIS -%) of 94.5 ± 3.9, while the average post-operative Glasgow outcome scale (GOS) score recorded was 4.2 ± 0.9. Conclusions: 3D printing based patient-specific design and fabrication of acrylic cranioplasty implant is safe and achieves acceptable cosmetic and clinical outcomes in patients with decompressive craniectomy. Our study ensured clinically acceptable structural and mechanical properties of implanted PMMA, suggesting that a low cost 3D printer based PMMA flap is an affordable option for cranioplasty in resource constrained settings. © 2021 Elsevier LtdItem Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images(Institute of Electrical and Electronics Engineers Inc., 2021) Thomas, E.; Pawan, S.J.; Kumar, S.; Horo, A.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis. © 2013 IEEE.Item Perspective analysis of assistive robots for elderly in India(Taylor and Francis Ltd., 2024) Hegde, P.; Gadag, A.; Sontakke, S.; Kumar, M.; Kholia, A.; Patel, J.; Khan, A.; Jahnavi, E.; Nabala, R.; Thotappa, D.Purpose: Assistive technology for elderly are advancing, and this study aimed to analyse the Indian perspective on utilising assistive robot technology for aiding elderly individuals. Materials and Methods: A population-based survey was undertaken to collect data from three perspectives: Relatives of the elderly, Healthcare professionals and Elderly individuals. The survey gathered 389 responses. The responses are statistically analysed, and data is visualised with different plots for better understanding. Results: It is observed that the older people rate with less conviction on the use of technology when compared to the relatives and healthcare professionals. Out of the three target groups, the elderly individuals had the most correlating attributes to purchasing the robot. Also, healthcare personnel, relatives, and older people gave 82%, 63% and 55% affirmatives to the question on purchasing the robot, respectively. And the cost of the robot is preferred to be under 6 lakh rupees. Conclusions: Though the younger generation has more orientation towards technology, older people are skeptical about handling computer gadgets or robots. However, there are significant expectations and concerns expressed by three target groups such as conversational, navigational, reminder features, security and malfunction concerns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.Item A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis(Institute of Physics, 2024) Kumar Koppolu, P.; Chemmangat, K.Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.