A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer's Disease Using EEG Signal.

Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper-based recurrent neural system (SbRNS) model for predicting Alzheimer's disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.

Effectiveness of a multimodal intervention in promoting physical activity among sedentary elderly population in socially and economically constrained settings - A quasi-experimental study.

Introduction: Aging is becoming a major challenge for policymakers. Regular exercise helps keep elderly people mobile, enhances physical and mental abilities, and to some extent delays the effects of chronic illnesses. Objectives: To evaluate the effectiveness of a multimodal intervention to increase physical activity levels among sedentary elderly living in socially and economically constrained settings. Materials and Methods: A quasi-experimental study was conducted in selected old age homes in Puducherry, South India in 2022 for 3 months. Individuals aged ≥60 years, both genders residing in selected old-age homes were included through convenience sampling. The sample size was 36 subjects per arm [three arms namely E1, E2 (intervention arms), and C (control arm)]. Baseline data collection on physical activity was collected using a semi-structured questionnaire in all three arms. The intervention arms (E1 and E2) received a multimodal intervention to promote physical activity. In addition, E1 arms were instructed to perform exercises with an "exercise partner" and to maintain a daily log. At the end of 8 weeks, follow-up data collection was done using the same questionnaire in all three arms. Data entry was done by MS Excel 2010 and analysis using SPSS version 21. Results: The mean (SD) of the days of physical activity per week and time of physical activity per day before and after the intervention among E1 and E2 were compared using paired t-tests. The difference between pre- and post-intervention was found to be statistically significant, that is, P value <0.05 in both the groups, thereby proving the effectiveness of the intervention. The difference between the three groups was found to be statistically significant, that is, P value <0.05. Conclusion: This multimodal intervention is found to be effective in increasing the physical activity of the participants in the interventional arms. Furthermore, having an exercise partner was found to be beneficial in ensuring motivation and compliance to carry out physical activity among the elderly living in socially and economically constrained settings.

LabVIEW based monitoring and rehabilitation module for freezing of gait in Parkinson's disease.

Freezing of gait (FOG) is an episodic motor symptom that occurs in almost half of the patients having Parkinson's Disease (PD). Levodopa and other dopaminergic drugs, although quite effective for tremors and bradykinesia (dyskinesia) are not so effective in case of postural instability and freezing of gait. This establishes a need for a non-pharmacological intervention. It has been well established in research that PD patients who experience FOG respond positively to sensory cueing. In this paper, a complete modular system has been developed that has both: (i) biofeedback in the form of vibrational cueing, synchronised with gait (hardware), as well as (ii) real-time monitoring, and graph generation for offline analysis (software). To realise this, a force sensor fitted shoe is constructed to analyse gait and provide vibrations in sync with phases of gait, for rehabilitation therapy. The assembly is monitored on LabVIEW, where (i) a 5-level force heat map and a 3D pressure map qualitatively shows the force experienced by force sensing resistors (FSR), (ii) an indicator for each vibrational motor shows its on/off state, and (iii) a live voltage vs. time graph (separate for each foot) is generated.