Employing entropy measures to identify visitors in multi-occupancy environments

Abstract

Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

“…The further approach for the prediction in real time [43] utilizes other methods for own prediction such as HMM [38] , SVD [44] for HAR and ADL with the possibility of using other sensors, e.g. (motion detectors [PIR] and door entry sensors) [42] and using new methods [44] to additive noise canceling in the predicted course of the signal. Moreover, as suggested in [46] , the classification can be further improved by projecting the training instances into the low-dimensional singular subspace; the SVM can train the classification model on it while not violating the privacy requirements for the training data.…”

Human Activity Mining in Multi-Occupancy Contexts Based on Nearby Interaction Under a Fuzzy Approach

“…By analysing PIR sensor data and employing entropy measurements, researchers have established a threshold that indicates the presence of a visitor based on occupancy data's standard deviation and entropy metrics such as Approximate Entropy, Sample Entropy, and Fuzzy Entropy. Many scientific contributions have adopted similar methodologies utilizing PIR sensors to derive occupancy-related insights within indoor environments [20][21][22][23].…”

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

“…Adoption of CSI not only ensures user privacy but also can aggregate multidimensional data associated with an occupant. Some studies have also reported usage of cellular signal for occupancy detection [12]. However, the prime limiting factor with this model is that a user will always require to carry their device in powered on condition.…”