Paper by Warren Liang et al: “In the age of ubiquitous computing, the convergence of wearable technologies and social sentiment analysis has opened new frontiers in both consumer engagement and patient care. These technologies generate continuous, high-frequency, multimodal data streams that are increasingly being leveraged by artificial intelligence (AI) systems for predictive analytics and adaptive interventions. This article explores a unified, integrated framework that combines physiological data from wearables and behavioral insights from social media sentiment to drive proactive engagement strategies. By embedding AI-driven systems into these intersecting data domains, healthcare organizations, consumer brands, and public institutions can offer hyper-personalized experiences, predictive health alerts, emotional wellness interventions, and behaviorally aligned communication.
This paper critically evaluates how machine learning models, natural language processing, and real-time stream analytics can synthesize structured and unstructured data for longitudinal engagement, while also exploring the ethical, privacy, and infrastructural implications of such integration. Through cross-sectoral analysis across healthcare, retail, and public health, we illustrate scalable architectures and case studies where real-world deployment of such systems has yielded measurable improvements in satisfaction, retention, and health outcomes. Ultimately, the synthesis of wearable telemetry and social context data through AI systems represents a new paradigm in engagement science — moving from passive data collection to anticipatory, context-aware engagement ecosystems…(More)”.
