Subject variability in sensor-based activity recognition

Building classification models in activity recognition is based on the concept of exchangeability. While splitting the dataset into training and test sets, we assume that the training set is exchangeable with the test set and expect good classification performance. However, this assumption is invalid due to subject variability of the training and test sets due to age differences. This happens when the classification models are trained with adult dataset and tested it with elderly dataset. This study investigates the effects of subject variability on activity recognition using inertial sensor. Two different datasets—one locally collected from 15 elders and another public from 30 adults with eight types of activities—were used to evaluate the assessment techniques using ten-fold cross-validation. Three sets of experiments have been conducted: experiments on the public dataset only, experiments on the local dataset only, and experiments on public (as training) and local (as test) datasets using machine learning and deep learning classifiers including single classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbors), ensemble classifiers (Adaboost, Random Forest, and XGBoost), and Convolutional Neural Network. The experimental results show that there is a significant performance drop in activity recognition on different subjects with different age groups. It demonstrates that on average the drop in recognition accuracy is 9.75 and 12% for machine learning and deep learning models respectively. This confirms that subject variability concerning age is a valid problem that degrades the performance of activity recognition models.