Ubiquitous Person Detection and Identification in Smart Living Environments
The recent advances in ubiquitous computing and the Internet of Things induce the awareness of smart environments and enhance the interaction between the system and the users. This enables energy savings, improvements in human comfort and assistance, and many other convenience services. However, it requires abilities to detect, count and identify current occupying invidiuals within the smart environment. Person detection and identification with devices like cameras is a well-addressed topic in literature. However, this vision-based sensing is not socially acceptable in a home setting. Person detection based on contact sensors, such as wearable devices, relies too much on correct behavior of its users, and can be regarded as inconvenient especially for older adults, as it requires a constant contact with the users. This works aims at using ambient sensors that can be installed in existing indoor environments to detect and identify individuals in smart environments. Ambient sensors can mitigate disadvantages of other sensing methods: (a) ambient sensors can be seamlessly integrated into homes, (b) they can sense without direct interaction from their users, (c) they are more socially acceptable than video surveillance. These benefits make it realistic to capture ambient sensor information constantly, which could make it possible to detect and identify people with context-aware environments. In order to achieve person detection and identification, three different tasks are investigated: Single Human Occupancy Detection, Multiple Human Occupancy Detection, and Human Identification. This thesis investigates the use of different machine learning methods for aforementioned tasks, including neural networks, SVM, kNN, Discriminant Analysis and CART, trains and evaluates on three different databases of ambient sensor measurements, and compares with the current methods proposed in literature. A bidirectional recurrent model that uses GRU cells is proposed to extract patterns from time series data. On a data set specifically intended for occupancy detection, the state-of-the-art is outperformed with neural network models, achieving up to 99,44% accuracy. Another utilized database is a composition of sensor data collected from 30 different apartments, annotated with daily life activities. These activity annotations are useful enough to gain knowledge for all three detection/identification tasks. While not enough information is provided to fully explore multiple person detection and identification, it is shown that (a) a system can predict whether the environment is not occupied, or that one person, or multiple people are present, and (b) ambient sensor measurement patterns are sufficient to distinguish two similar apartments that have one resident each, so indirectly two persons can be identified.
Darmstadt, TU, Bachelor Thesis, 2019