Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.

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Background: Pain is a common symptom in Parkinson's disease (PD) patients. Scales to rate pain in PD are marred by several flaws, either not being available in other languages or not specific for PD. Objectives: To assess the frequency of pain among bilingual Indian PD patients using “King's Parkinson's disease pain scale” (KPPS) and to validate it. Methods: We randomly administered KPPS in Hindi/English to all consecutive bilingual persons with PD. The results were appropriately analyzed. Results: A total of 119 PD patients were enrolled with a mean age of 64.34 (± 9.57) years. Median Hoehn and Yahr stage was 2 (42.85%). Pain was present in 62 (52.1%) PD patients. The most common type was musculoskeletal (74.19%). The mean total KPPS score was 16.02 ± 10.57. KPPS score was significantly higher in women and correlated positively with unified Parkinson's disease rating scale (UPDRS) part 2 and 4 scores (r = 0.27 and r = 0.25). Risk factors for pain were female gender, higher H and Y stage, total UPDRS score, and individual UPDRS part 3 and 4 scores. Difficulty falling asleep ( P = 0.01), frequent awakenings ( P = 0.01), diminished smell sensation ( P = 0.003), diminished speech volume ( P = 0.02), gait freezing ( P = 0.03), and falls ( P = 0.001) correlated with the presence of pain. The interclass correlation coefficient between the Hindi and English versions of KPPS was 0.835, while Bland–Altman analysis showed 96.7% agreement suggesting excellent correlation and validation. Conclusions: KPPS is an easy tool for characterization, scoring, and follow-up of pain in PD patients. The Hindi version has good agreement with the original English version.

Background: In recent studies, Cross Project Defect Prediction (CPDP) has proven to be feasible in software defect prediction. When both the source as well as the target projects have the same metric sets, it is termed as a homogeneous CPDP. Current CPDP strategies are difficult to implement through projects with a variety of different metric sets. Aside from that, training data often has a problem with class imbalance. The number of defective/bug-ridden and non-defective/clean instances of the source class is usually unbalanced. To address this issue, we propose a heterogeneous cross-project defect prediction framework that can predict defects across projects with different metric sets. Methods: To construct a prediction framework between projects with heterogeneous metric sets, our heterogeneous cross project defect prediction approach uses metric selection, metric matching, class imbalance (CIB) learning followed by ensemble modelling. For our study, we have considered six open-source object-oriented projects. Results: The proposed model resolved the class imbalance issue and records the highest recall value of 7.5 with f-score value as 7.4 in comparison with other baseline models. The highest AUC (area under curve) value of 0.86 has also been recorded. K fold cross validation was performed to evaluate the training accuracy of the model. The proposed optimized model was validated using the Wilcoxon signed rank test (WSR) with a significance level of 5% (i.e., P-value=0.05). Conclusions: Our empirical research on these six projects shows that predictions based on our methodology outperform or are statistically comparable to Within-Project Defect Prediction (WPDP) and other heterogeneous CPDP baseline models.

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