Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer
biomechanists a wealth of data on healthy and pathological movement. To harness the …

Simple Summary This study represents a resourceful review article that can deliver
resources on neurological diseases and their implemented classification algorithms to …

This research studies a support vector machine (SVM)-based ensemble learning algorithm
for breast cancer diagnosis. Illness diagnosis plays a critical role in designating treatment …

Inertial measurement units (IMUs) have a long-lasting popularity in a variety of industrial
applications from navigation systems to guidance and robotics. Their use in clinical practice …

A new bearing vibration feature extraction method based on multiscale permutation entropy
(MPE) and improved support vector machine based binary tree (ISVM-BT) is put forward in …

Quantitative gait assessment is important in diagnosis and management of Parkinson's
disease (PD); however, gait characteristics of a cohort are dispersed by patient physical …

The increasing amount of data in biomechanics research has greatly increased the
importance of developing advanced multivariate analysis and machine learning techniques …

This paper presents a new rolling bearing fault diagnosis method based on local mean
decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and …

This paper investigates the application of different homogeneous ensemble learning
methods to perform multi-class classification of respiratory diseases. The case sample …

Gait is a biometric trait that can allow user authentication, though it is classified as a “soft”
one due to a certain lack in permanence and to sensibility to specific conditions. The earliest …