PRIORITIZING FALL RISK MANAGEMENT: IRRESPECTIVE OF PARKINSON’S DISEASE PROGRESSION DURING REHABILITATION

Background and Aims: It has been established that the hazard of falling in Parkinson's patients is a primary cause of loss of independence, and hence it is a significant component to the disease's burden. As a result, the goal of this study is to evaluate the relationship between level of disease progression and the risk of falling in order to understand the critical need for rehab intervention in Parkinson's disease. Methodology: A cross-sectional survey was conducted on 24 participants participated in study through convenience sampling technique from primary and tertiary care institutes/ hospitals. Tinetti balance and gait test was used to evaluate the risk of fall among patients while The Hoehn and Yahr Scale was used to track the progression of Parkinson's symptoms and disability. Results: Total 24 (n=24) participants with idiopathic Parkinson’s disease were enrolled in the study in which the majority of the participants belong to age group 59-69 years. The balance and gait of Parkinson’s patient is strongly negative correlated i.e. -8.33 with the severity of Parkinson’s disease with .000 level of significance which is strongly negative correlated with the severity of Parkinson’s disease (p<0.05). Conclusion: This demonstrates that the balance and gait of a Parkinson’s person is not related to the severity level Parkinson’s disease thus fall management in PD patients must be kept in priority during the rehab intervention for ADL independence and social participation.

Updating Techniques for Solving Linear Least Squares Problems With Equality Constraints

Linear least squares (LLS) problem is a computational problem of wide scale importance in the field of numerical linear algebra. Such problem often arises in real world applications subject to some equality constraints and is known as linear least squares problem with equality constraints (LSE). In this work, updating techniques based on QR factorization are considered as a solution tool for LSE problem. The LSE problem is first transformed into the weighted LLS problem using a suitable weighted factor w and then its solution is ap proximated by the updating techniques. The proposed updating algorithms are mainly based on Householder QR factorization. The original problem is partition into a small subproblem and then its QR factorization is com puted. The removed block of columns and rows are appended respectively and then updated the QR factorization to get the solution of the original problem. The proposed algorithms contain repeatedly updating of the QR factorization to approximate the solution of LSE problem. Moreover, the solution of an equivalent formulation of LSE problem possessing the saddle point structure is computed using updating of the QR factorization. The proposed algorithms are implemented in MATLAB and the error analysis has been carried out to study their accuracy and stability