As a consequence of demographic shifts, the proportion of the older population is growing at an accelerated pace, resulting in a notable decline in cognitive and motor functions. This study examines the potential of wearables to monitor activities of daily living (ADL) and identify changes in behavior, thereby enabling early intervention to maintain the independence of the person. Eight dynamic ADLs were analyzed using data collected from eight subjects who were wearing a sensor belt that includes an accelerator and a gyroscope. The data were preprocessed and employed to train and evaluate two distinct types of classifiers: a deep learning and several machine learning approaches. Two data splits were considered: a subject-dependent model, which utilized data from all subjects for training and testing, and a Leave-One-Subject-Out Cross-Validation (LOSO-CV) subject-independent model, which excluded one subject from the training set for validation. The subject-dependent approach yielded high accuracies of 99.5% and 99.8% for the classification network and the best support vector machine, respectively. The LOSO-CV yielded accuracies of 77.8% for the convolutional neural network and 77.6% for the best support vector machine. While the classification network demonstrated marginally superior results, the support vector machine required significantly less training time, suggesting its potential suitability for practical applications.