Bayesian-based scenario generation method for human activities
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
A context-driven approach to scalable human activity simulation
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
| Hi-index | 0.00 |
Activity recognition research relies heavily on test data to verify the modeling technique and the performance of the activity recognition algorithm. But data from real deployments are expensive and time consuming to obtain. And even if cost is not an issue, regulatory limitations on the use of human subjects prohibit the collection of extensive datasets that can test all scenarios, under all circumstances. A powerful and verifiable simulation tool is needed to accelerate research on human activity recognition. We present Persim, an event driven simulator of human activities in pervasive spaces. Persim is capable of capturing elements of space, sensors, behaviors (activities), and their inter-relationships. We focus on presenting the five main use cases for Persim addressing dataset synthesis, reuse and extension of existing datasets, sharing of data and simulation projects, as well as data validation.