Home Appliance Recognition Using Edge Intelligence

Ambient assisted living (AAL) environments represent a key concept for dealing with the inevitable problem of population-ageing. Until recently, the use of computational intensive techniques, like Machine Learning (ML) or Computer Vision (CV), were not suitable for IoT end-nodes due to their limited resources. However, recent advances in edge intelligence have somehow changed this landscape for smart environments. This paper presents an AAL scenario where the use of ML is tested in kitchen appliances recognition using CV. The goal is to help users on working with those appliances through Augmented Reality (AR) on a mobile device. Seven types of kitchen appliances were selected: blender, coffee machine, fridge, water kettle, microwave, stove and toaster. A dataset with nearly 4900 images was organized. Three different deep learning (DL) models from the literature were selected, each with a total number of parameters and architecture compatibles with their execution on mobile devices. The results obtained in the training of these models reveal precision in the test set above 95% for the model with better results. The combination of edge AI and ML opens the application of CV in smart homes and AAL without compromising mandatory requirements as the system privacy or latency.