MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition

Human Activity Recognition is a longstanding problem in AI with applications in a broad range of areas: from healthcare, sports and fitness, security, and human computer interaction to robotics. The performance of HAR in real-world settings is strongly dependent on the type and quality of the input signal that can be acquired. Given an unobstructed, high-quality camera view of a scene, computer vision systems, in particular in conjunction with foundational models (e.g., CLIP), can today fairly reliably distinguish complex activities. On the other hand, recognition using modalities such as wearable sensors (which are often more broadly available, e.g, in mobile phones and smartwatches) is a more difficult problem, as the signals often contain less information and labeled training data is more difficult to acquire. In this work, we show how we can improve HAR performance across different modalities using multimodal contrastive pretraining. Our approach MuJo (Multimodal Joint Feature Space Learning), learns a multimodal joint feature space with video, language, pose, and IMU sensor data. The proposed approach combines contrastive and multitask learning methods and analyzes different multitasking strategies for learning a compact shared representation. A large dataset with parallel video, language, pose, and sensor data points is also introduced to support the research, along with an analysis of the robustness of the multimodal joint space for modal-incomplete and low-resource data. On the MM-Fit dataset, our model achieves an impressive Macro F1-Score of up to 0.992 with only 2% of the train data and 0.999 when using all available training data for classification tasks. Moreover, in the scenario where the MM-Fit dataset is unseen, we demonstrate a generalization performance of up to 0.638.