Affiliation:
1. Lab-STICC, IMT Atlantique, 29280 Brest, France
2. Delta Dore Company, Le Vieux Chêne, 35270 Bonnemain, France
Abstract
Recently, deep learning (DL) approaches have been extensively employed to recognize human activities in smart buildings, which greatly broaden the scope of applications in this field. Convolutional neural networks (CNN), well known for feature extraction and activity classification, have been applied for estimating human activities. However, most CNN-based techniques usually focus on divided sequences associated to activities, since many real-world employments require information about human activities in real time. In this work, an online human activity recognition (HAR) framework on streaming sensor is proposed. The methodology incorporates real-time dynamic segmentation, stigmergy-based encoding, and classification with a CNN2D. Dynamic segmentation decides if two succeeding events belong to the same activity segment or not. Then, because a CNN2D requires a multi-dimensional format in input, stigmergic track encoding is adopted to build encoded features in a multi-dimensional format. It adopts the directed weighted network (DWN) that takes into account the human spatio-temporal tracks with a requirement of overlapping activities. It represents a matrix that describes an activity segment. Once the DWN for each activity segment is determined, a CNN2D with a DWN in input is adopted to classify activities. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference38 articles.
1. Towards adoption of building energy simulation and optimization for passive building design: A survey and a review;Tian;Energy Build.,2018
2. Phan, H. (2022). Modélisation de l’Activité des Ménages dans le Bâtiment Résidentiel Instrumenté. [Ph.D. Thesis, University of Bordeaux].
3. Alhamoud, A., Xu, P., Englert, F., Reinhardt, A., Scholl, P., Boehnstedt, D., and Steinmetz, R. Extracting human behavior patterns from appliance-level power consumption data. Proceedings of the European Conference on Wireless Sensor Networks.
4. Occupancy and occupant activity drivers of energy consumption in residential buildings;Akbari;Energy Build.,2021
5. Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes;Bouchabou;Electronics,2021
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献