Obtaining data on active travel activities such as walking, jogging, and cycling are important for refining sustainable transportation systems (STS). In order to provide an accurate and privacy-preserving sensing solution, a deep learning (DL)-enhanced distributed acoustic sensing (DAS) system for recognizing active travel activities is proposed. By leveraging the ambient vibrations captured by DAS, this scheme infers motion patterns without relying on image-based or wearable devices, thereby addressing privacy concerns. We conduct real-world experiments in two geographically distinct locations and collect a comprehensive dataset to evaluate the performance of the proposed system. To address the generalization challenges posed by heterogeneous deployment environments, we propose two solutions based on network availability: 1) an Internet-of-Things (IoT) scheme based on federated learning (FL) is proposed, and it enables geographically different DAS nodes to be trained collaboratively to improve generality; 2) an off-line initialization approach enabled by meta-learning is proposed to develop high-generality initialization for DL models and to enable rapid model fine-tuning with limited data samples, facilitating generalization in newly established or isolated DAS nodes. Experimental results of the walking and cycling classification problem demonstrate the performance and generality of the DL-enhanced DAS system, paving the way for practical, large-scale DAS monitoring of active travel.

Knowledge and the use of knowledge is increasingly important for business. As the world moves towards economies based on professional expertise and knowledge workers there is increasing pressure to make best use of available resources. Healthcare is no exception, and as new research becomes increasingly available healthcare professionals are reliant on their experience to identify robust evidence on which to base treatment decisions. This paper describes a research project carried out within a healthcare organization to identify professional knowledge and disseminate this throughout the organization. The paper describes the creation and testing of a conceptual model to locate and share knowledge and the development of an e-health system based on this model to support healthcare professionals.