In the last decade, unobtrusive (device-free and non-privacy invasive) recognition of activities of daily living for an individual in a smart home has been studied by many researchers. However, the unobtrusive recognition of multi-resident activities in a smart home is hardly studied. We propose a novel RGB activity image-based DCNN classifier for the unobtrusive recognition of the multi-resident activities (BedtoToilet, Bed, Breakfast, Lunch, Leavehome, Laundry, Dinner, Nightwandering, R2work, and R1medicine) using Cairo open data set provided by the CASAS Project. The open data set is collected by environmental sensors (PIR and temperature sensors) in Cairo testbed, while an adult couple with a dog was living for 55 days. The data set is preprocessed with activity segmentation, sliding window, and RGB activity image conversion steps. The experimental results demonstrate that our classifier has the highest total accuracy of 95.2% among the previously developed machine learning classifiers that employed the same data set. Moreover, the proposed RGB activity image was proven to be helpful for increasing the recognition rate. Therefore, we conclude that the proposed DCNN classifier is a useful tool for the unobtrusive recognition of the multi-resident activity in a home.
- activity recognition
- convolutional neural networks
- deep learning
- wandering detection
ASJC Scopus subject areas
- Electrical and Electronic Engineering