Surface acoustic arrays to analyze human activities in smart environments
Smart Environments should be able to understand a user's need without explicit interaction. In order to do that, one step is to build a system that is able to recognize and track some common activities of the user. This way, we can provide a system that provides various services for controlling installed appliances and offering help for every day activities. Applying these services in the users' environment should make his life more comfortable, easier, and safer. In this paper, we will introduce an embedded sensor system using surface acoustic arrays to analyze human activities in a smart environment. We divided basic activity groups ranging from walking, cupboard closing to falling, including their extended sub-activity groups. We expanded walking into walking barefoot, with shoes and with high heels and further extended closing cupboard with three cupboards locating on different positions. We further investigated the usage of single pickup or a combination of 4 pickups with their effect on the recognition precision. We achieved an overall precision of 97.23% with 10-fold cross validation using support vector machine (SVM) for all sub-activity group combined. Even using one pickup only, we can achieve an overall precision of more than 93%, but we can further increase the precision by using a combination of pickups up to 97.23%.