Performance Evaluation of an Ambient Intelligence Testbed for Improving Quality of Life: Evaluation Using Clustering Approach

Abstract

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm. For evaluation we considered respiratory rate and heart rate metrics. The simulation results show that the k-means clustering algorithm has a good performance.