The field of human activity recognition is moving towards the development of more generalizable and adaptable models. Recent research has focused on creating foundation models that can be applied to a wide range of downstream tasks, such as gesture recognition, gait analysis, and sports monitoring. These models are designed to capture diverse signal characteristics and can be fine-tuned for specific tasks, enabling more accurate and reliable activity recognition. Notable papers in this area include: SlotFM, which introduces a motion foundation model with slot attention for diverse downstream tasks, achieving a 4.5% performance gain on average. FM-FoG, which presents a real-time foundation model-based wearable system for freezing-of-gait mitigation, achieving a 98.5% F1-score on previously unseen patients. ActiNet, which proposes a self-supervised deep learning approach for activity intensity classification using wrist-worn accelerometers, exceeding the performance of existing baseline models.