56 research outputs found
Bioactive Conserve from Unconventionally Processed Cumin Seeds
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Ethics in human resource management: potential for burnout among healthcare workers in ART and community care centres.
No full textThis paper examines ethical dilemmas in providing care for people with HIV/AIDS. Healthcare providers in this sector are overworked, particularly in the high prevalence states. They are faced with the dual burden of the physical and the emotional risks of providing this care. The emotional risks result from their inability to control their work environment, while having to deal with the social and cultural dimensions of patients' experiences. The physical risk is addressed to some extent by post exposure prophylaxis. But the emotional risk is largely left to the individual and there is little by way of institutional responsibility for minimising this. The guidelines for training workers in care and support programmes do not include any detailed institutional mechanisms for reducing workplace stress. This aspect of the programme needs to be examined for its ethical justification. The omission of institutional mechanisms to reduce the emotional risks experienced by healthcare providers in the HIV/AIDS sector could be a function of lack of coordination across different stakeholders in programme development. This can be addressed in further formulations of the programme. Whatever the reasons may be for overlooking these needs, the ethics of this choice need to be carefully reviewed
Mitigating technical challenges in brain-computer interfaces for stroke rehabilitation
No full textBrain-computer interfaces (BCIs) provide a means of non-muscular communication by translating brain activity into the control of external devices. Motor imagery (MI) has attracted significant attention among various non-invasive BCI paradigms using electroencephalogram (EEG) for its potential in stroke rehabilitation. However, MI-based BCIs encounter challenges in real-time applications for stroke patients, primarily due to limited reliability and robustness. Additionally, the scarce availability of clinical data impedes the development of cross-subject models for MI detection in stroke patients. Furthermore, the current MI-BCIs do not adequately facilitate the restoration of distal hand functions, which are essential for enhancing the quality of life for individuals with motor impairments. This thesis proposes solutions to address these technical challenges in BCIs for stroke rehabilitation using deep learning (DL) methods. Furthermore, a novel experimental protocol is introduced to enable clinically relevant practical applications of BCIs in stroke patients.
The research begins with an extensive literature review focusing on the impact of EEG discrepancies on the performance of BCIs. The review delves into channel selection and transfer learning techniques that aim to enhance the resilience of EEG-BCIs. Recently, there has been a surge in studies investigating subject-independent models in the domain of MI-BCI. This trend is driven by the superior predictive capabilities of subject-independent models based on DL compared to subject-specific models. However, the literature review highlights a significant gap in the research, as most studies in this area have focused primarily on healthy subjects, with limited inclusion of stroke patients. Furthermore, the review encompasses relevant studies exploring MI decoding from the same limb.
With the goal of selecting the optimal set of EEG channels to enhance overall classification performance in DL-based MI-BCIs, the author proposes subject-independent channel selection using layer-wise relevance propagation (LRP) and neural network pruning. Traditional approaches to channel selection have focused predominantly on subject-specific optimization, whereas subject-independent methods are essential for the utilization of DL models trained on cross-subject data. The proposed methodology not only achieves a significant reduction in the number of channels but also maintains subject-independent classification accuracy, while ensuring interpretability in terms of underlying neural mechanisms.
Furthermore, in consideration of the limited availability of clinical data to train BCI algorithms, the research investigates the feasibility of employing DL models pre-trained on data from healthy individuals to detect MI in stroke patients, while also taking into account the inter-subject variability between the healthy and stroke populations. Through domain adaptation, the transfer learning approach demonstrates improved MI detection accuracy in stroke patients, surpassing subject-specific models. Interpretability analysis using transfer models determines channel relevance patterns that indicate contributions from the bilateral motor, frontal, and parietal regions of the cortex towards MI detection in stroke patients. Furthermore, the healthy-to-stroke transfer learning achieves comparable performance to stroke-to-stroke transfer learning, highlighting its potential to enhance the clinical use of BCI algorithms.
Finally, a novel BCI experiment utilizing a robotic exoskeleton for unilateral hand motor attempt (MA) tasks is introduced. The focus of stroke rehabilitation is often the recovery of distal hand function. A mere act of opening and closing the hand has the potential to bring about significant enhancements in the quality of life experienced by individuals who have suffered from stroke. In this research study, MA-EEG data collected from healthy subjects is employed to develop subject-specific and subject-independent DL models. The results highlight the importance of this experiment in driving advancements in stroke rehabilitation.
This thesis makes novel contributions to the field by optimizing EEG-BCIs for stroke rehabilitation through subject-independent channel selection, transfer learning from healthy to stroke populations, and a new BCI experiment for same-hand MA-EEG decoding. The findings pave the way for more reliable, applicable, and interpretable BCIs, enhancing their potential for clinical use and rehabilitation purposes.Doctor of Philosoph
Assessment of Nutritional Status and its Associated Factors among the Elderly Population: A Cross-sectional Study from Rural Area of Madurai, Tamil Nadu, India
No full textIntroduction: Elderly malnutrition is an iceberg phenomenon and remains undiagnosed most of the time. The elderly people are at risk of malnutrition due to physical, psychological, social, dietary and environmental risk factors. When malnutrition gets compounded with various co-morbidities, it turns into a vicious cycle.
Aim: To focus on the assessment of nutritional status and its associated factors among the elderly population above 60 years of age in a rural area of Madurai district, Tamil Nadu, India.
Materials and Methods: This was a community-based cross-sectional study done for a period of four months in the rural field practice area of Madurai Medical College, Madurai, Tamil Nadu, India. Following ethical clearance, study subjects were selected by using single stage area wise cluster sampling technique. Data was collected from 240 elderly individuals by face to face interview using semistructured questionnaire. Details regarding socio-demographic variables like age, gender, educational status, occupational status, economic dependency and place of residence were taken. Nestle’s Short Form Mini Nutritional Assessment (SF-MNA) screening tool was used to assess the nutritional status of the study participants. The association was assessed by Chi-square test. Significance level was considered at p-value of ≤0.05.
Results: Prevalence of malnutrition was seen among 33 (13.75%) individuals. A 105 (43.75%) were at the risk of malnutrition and 102 (42.5%) had satisfactory nutritional status. The comparison between well nourished, at risk of malnutrition and malnourished groups showed significant differences with respect to age (p-value=0.016), economic dependency (p-value=0.002), place of residence (p-value=0.004), Body Mass Index (BMI) (p-value=0.024), calf circumference (p-value=0.016) and presence of co-morbid illness (p-value=0.015).
Conclusion: The findings of the present study clearly indicate that malnutrition is a multifactorial condition associated with socio-demographic, somatic and functional status. A multidimensional approach is required to deal with these issues. Nutritional assessment and screening of elderly people should be done by opportunistic screening for early detection of malnutrition and to implement an appropriate nutritional intervention
Power optimization in mobile stations using heterogeneous small cells for green cellular networks
No full textEnergy Consumption Analysis of Multicast Routing Protocols in Wireless Ad Hoc Network Environment
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