Search

With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate split of testing and training datasets, causing these models to evaluate the same subjects that they were trained on, making them subject-dependent. This study comparatively discusses this validation approach with a universal approach, Leave-One-Subject-Out (LOSO) cross-validation which is not subject-dependent and ensures that an entirely new subject is used for evaluation in each fold, validated on four different machine learning models trained on windowed data and select hand-crafted features. The random forest model, with the highest accuracy of 76% when evaluated on LOSO, achieved an accuracy of 89% on k-fold cross-validation, demonstrating data leakage. Additionally, this experiment underscores the significance of hand-crafted features by contrasting their accuracy with that of raw sensor models. The feature models demonstrate a remarkable 30% higher accuracy, underscoring the importance of feature engineering in enhancing the robustness and precision of HAR systems. [ABSTRACT FROM AUTHOR]

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson's disease (PD) and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. The technique used in this study is to extract voiceprint from each voice samples by using mel frequency cepstral coefficients (MFCCs). The extracted MFCC were compressed by calculating their average value in order to extract the voiceprint from each voice recording. Subsequently, a classification method was performed using leave one subject out (LOSO) validation scheme along with support vector machines (SVMs). We also used an independent test to validate our results by using another database which contains 28 PD patients. Based on the research result, the best obtained classification accuracy using LOSO on the first dataset was 82.50 % using MLP kernel of SVM on sustained vowel /u/. And the maximum classification accuracy using the independent test was 100 % using sustained vowel /a/ with polynomial kernel of the SVM and with MLP kernel of the SVM. This result was also achieved using sustained vowel /o/ with polynomial kernel of the SVM. [ABSTRACT FROM AUTHOR]