Keywords :
Human Activity Recognition, Daily-Life Activity Classification, Machine Learning, Pattern Recognition, Wearable Sensors, Inertial Sensors, Accelerometer, Gyroscope, IMU Signals.
Abstract :
[en] Human activity recognition (HAR) is utilized to automatically identify the daily-life activities of people for
the effective management of age-related health conditions. Classical machine learning (ML) algorithms are
used to design HAR systems, in a subject-specific or population-based configuration depending on the
application. In this study, the performance of 8 classical and ensemble-learning-based ML classifiers has been
studied for both HAR configurations. Inertial measurement unit (IMU) signals from 10 healthy participants,
corresponding to various static, dynamic, and transitional daily-life activities, were acquired. Random forest
(RF), ensemble adaptive boosting (EAB), ensemble subspace (ES), decision tree (DT), k-nearest neighbors
(KNN), linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network
(ANN) were used to classify these activities. The performance of the classifiers was measured in terms of
mean classification accuracy (MCA). The results showed that, for a subject-specific HAR system, ES
(97.78%) has achieved the highest MCA followed by RF (96.61%) and SVM (96.11%) while outperforming
the DT, KNN, and LDA (P-value < 0.05). For a population-based HAR system, SVM (95.18%) achieved the
highest MCA, however, no significant difference has been observed among the MCA of all the investigated
classifiers (P-value > 0.05). Also, the class-wise comparison reveals that SVM outperformed the other
investigated classifiers in terms of MCAs for each of the distinct activities. Based on the HAR configuration
incorporating diverse static, dynamic, and transitional daily-life activities, the findings may be used to develop
a customized HAR system for the effective management of movement disorders.
