Skipper

The social IoMT-based activity-monitoring system comprises several devices with different datasets. It faces challenges like a collection of a global activity dataset which comprises a myriad of activities. In this article, we propose a federated Siamese network-based data-independent group activity segregator—Skipper—which aims to identify anomalies in an activity-monitoring social IoMT system. The novelty of this work is that Skipper does not require any dataset before its deployment, which removes the need for any prior training of the model for activity monitoring. As a proof of concept, we select activities pertaining to school environments to identify low-performing students in a classroom, who would require teachers’ close attention to ensure balanced growth and proper health. Skipper monitors the students independently for their motion signatures through a wearable device that consists of an accelerometer. A federated Siamese network calculates indices that signify the degree of similarity among the students’ activities. Skipper identifies the students who do not perform the same activity. With real-world implementations, we observe that Skipper requires network rates of 10 Kb/s, making it suitable for low bandwidth networks while we achieve just 20% CPU and 10 MB memory utilization on constrained edge devices. Further, with an increasing number of students up to 100, the time delay for final results is limited to 80 s. Hence, Skipper is a fast, easy, and accurate solution for recognizing outliers in IoMT social systems

Computer network · Computer security · Computer Science · Context-Aware Activity Recognition Systems · IoT and Edge/Fog Computing · Opportunistic and Delay-Tolerant Networks

• Real-Time Cloud-Based Patient-Centric Monitoring Using Computational Health Systems

  Open Access•Chinmay Chakraborty, Amit Kishor•IEEE Transactions on Computational…•2022

  Add to list