In response to the COVID-19 pandemic and the need for increased research, this study aimed to develop a real-time learning system to provide infection control for residential special care contexts and in doing so, explored different crowdsourcing technologies, spatial usages, and data processing methods within the scope of smart health-care systems and environments. Experiments were conducted in the selected special care indoor environment, which was fitted with sensors and Internet of Things devices, from which generated data were used to train Convolutional Neural Networks, Long-Short Term Memory, and Binary Layered Long-Short Term Memory neural networks. Sequential neural networks were multi-layered and configured in tandem and from these, the real-time updating learning system was developed. The system monitors the user activity and environmental data and predicts critical cases to send alerts to caregivers. Findings showed that stacking neural networks over one another increases the efficiency in updating the training data of real-time learning system. Overall, the study concludes that the developed real-time learning system is lightweight, fast, and efficient for infection control and special care at the private scale and can be multiplied at multiple nodes of larger networks of smart health services and environments.