With the proliferation of wearable devices and ubiquitous computing, context-aware music recommendation systems are evolving to deliver more personalized experiences. Traditional methods relying on explicit feedback, such as listening history and song ratings, struggle to adapt to users' dynamic contextual states, limiting their effectiveness. In this paper, we introduce Wear2Rec, a privacy-preserving, IoT-driven music recommendation system that leverages passive physiological, psychological, and environmental data from wearable devices to enhance personalization. At its core, Wear2Rec employs an innovative dual-expert-dual-task network architecture that separately extracts context and music features, minimizing cross-modal interference. Unlike conventional models, it simultaneously optimizes both music recommendation and mood improvement prediction, ensuring both relevant music suggestions and an emotionally supportive listening experience. Experimental results show its superiority, achieving 0.8411 AUC for music recommendation and 0.5928 MAE for mood prediction, outperforming traditional models by integrating emotional adaptation. Wear2Rec represents a significant step forward in human-centric, real-time recommendation systems, setting new standards for personalized music experiences in IoT-driven ubiquitous computing environments.

Measuring visitor engagement in informal learning environments presents critical challenges for optimizing educational experiences and spatial design. While existing research predominantly focuses on formal settings, systematic analysis of multidimensional engagement in complex environments like museums remains underdeveloped. This study introduces the first integrated head-mounted display (HMD)-based framework combining meso-scale spatial analysis through behavioral engagement heatmaps with micro-level temporal engagement modeling via headset pose and eye-tracking data. Our edge-optimized long short-term memory (LSTM) model achieves real-time engagement measurement with a mean squared error (MSE) of 0.145 using four physiologically grounded features from a two-stage user study (N=20 for feature analysis, N=15 for modeling). The framework synthesizes planar trajectory heatmaps and panoramic fixation distributions to enable both real-time adaptive support and post-hoc exhibition design insights. Results demonstrate HMDs' potential as precision measurement tools, establishing methodological foundations for intelligent heritage environments that dynamically respond to engagement states through integrated meso-micro analytics.

With the help of machine learning, models are trained to recognize human activity based on radio frequency signals, which are widely used in human-computer interaction, healthcare, etc. Cross-domain human activity recognition (HAR) aims to adapt a model trained in a specific source domain (including environment and user) for another target domain. Most existing cross-domain recognition methods are proposed under an ideal closed-set assumption, which means the training set and the testing set contain the same categories of human activities. However, when a model is applied in practice for HAR, it often encounters new categories of activities which are not contained in the training set. Under such open-set condition, the traditional closed-set cross-domain recognition model usually incorrectly identifies the new activity as a known activity, which decline the recognition accuracy. In this article, a model is proposed for open-set cross-domain HAR. The model is established based on generative adversarial network, and a unique generation module is designed to generate confusing samples whose features are similar to known classes. Thanks to such design, the proposed model can autonomously select appropriate unlabeled samples under the open-set condition to improve the open-set recognition ability of the model in the target domain. Extensive experiments are conducted based on four real data sets, one is collected by ourselves and the other three are public. The results show that the proposed model outperforms other state-of-the-art methods in the open-set and the cross-domain contexts.