scholarly journals Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 416 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Home Environment ◽  
Daily Living ◽  
Approximate Entropy ◽  
Entropy Measures ◽  
Different Types ◽  
Novel Method ◽  
High Degree ◽  
Movement Activities

Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.

scholarly journals Employing entropy measures to identify visitors in multi-occupancy environments

2020 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Home Environment ◽  
Daily Living ◽  
Wearable Sensors ◽  
Permutation Entropy ◽  
Care Workers ◽  
Entropy Measures ◽  
Novel Method ◽  
High Degree ◽  
Motion Detectors ◽  
To Receive

AbstractHuman 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.

scholarly journals An Entropy-Based Approach for Anomaly Detection in Activities of Daily Living in the Presence of a Visitor

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 845 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Anomaly Detection ◽  
Activities Of Daily Living ◽  
Home Environment ◽  
Health Care Workers ◽  
Daily Living ◽  
Healthcare Management ◽  
Entropy Measure ◽  
Care Workers ◽  
Entropy Measures ◽  
Measure Analysis

This paper presents anomaly detection in activities of daily living based on entropy measures. It is shown that the proposed approach will identify anomalies when there are visitors representing a multi-occupant environment. Residents often receive visits from family members or health care workers. Therefore, the residents’ activity is expected to be different when there is a visitor, which could be considered as an abnormal activity pattern. Identifying anomalies is essential for healthcare management, as this will enable action to avoid prospective problems early and to improve and support residents’ ability to live safely and independently in their own homes. Entropy measure analysis is an established method to detect disorder or irregularities in many applications: however, this has rarely been applied in the context of activities of daily living. An experimental evaluation is conducted to detect anomalies obtained from a real home environment. Experimental results are presented to demonstrate the effectiveness of the entropy measures employed in detecting anomalies in the resident’s activity and identifying visiting times in the same environment.

scholarly journals UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1267 ◽  
Author(s):  
Macarena Espinilla ◽  
Javier Medina ◽  
Chris Nugent
Keyword(s):  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Proximity Data ◽  
Location Data ◽  
Smart Watch ◽  
Real World Applications ◽  
Tools And Techniques

Many real-world applications, which are focused on addressing the needs of a human, require information pertaining to the activities being performed. The UCAmI Cup is an event held within the context of the International Conference on Ubiquitous Computing and Ambient Intelligence, where delegates are given the opportunity to use their tools and techniques to analyse a previously unseen human activity recognition dataset and to compare their results with others working in the same domain. In this paper, the human activity recognition dataset used relates to activities of daily living generated in the UJAmI Smart Lab, University of Jaén. The dataset chosen for the first edition of the UCAmI Cup represents 246 activities performed over a period of ten days carried out by a single inhabitant. The dataset includes four data sources: (i) event streams from 30 binary sensors, (ii) intelligent floor location data, (iii) proximity data between a smart watch worn by the inhabitant and 15 Bluetooth Low Energy beacons and (iv) acceleration of the smart watch. In this first edition of the UCAmI Cup, 26 participants from 10 different countries contacted the organizers to obtain the dataset.‬‬‬‬‬

scholarly journals A DSL-Based Approach for Detecting Activities of Daily Living by Means of the AGGIR Variables

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5674
Author(s):  
José Manuel Negrete Ramírez ◽  
Philippe Roose ◽  
Marc Dalmau ◽  
Yudith Cardinale ◽  
Edgar Silva
Keyword(s):  
Activities Of Daily Living ◽  
Home Environment ◽  
Daily Living ◽  
Smart Home ◽  
Daily Activities ◽  
Domain Specific Language ◽  
Analysis Tool ◽  
Disabled People ◽  
Domain Specific ◽  
Event Information

In this paper, we propose a framework for studying the AGGIR (Autonomie Gérontologique et Groupe Iso Ressources—Autonomy Gerontology Iso-Resources Groups) grid model, with the aim of assessing the level of independence of elderly people in accordance with their capabilities of performing daily activities as well as interacting with their environments. In order to model the Activities of Daily Living (ADL), we extend a previously proposed Domain Specific Language (DSL), by defining new operators to deal with constraints related to time and location of activities and event recognition. The proposed framework aims at providing an analysis tool regarding the performance of elderly/disabled people within a home environment by means of data recovered from sensors using a smart-home simulator environment. We perform an evaluation of our framework in several scenarios, considering five of the AGGIR variables (i.e., feeding, dressing, toileting, elimination, and transfers) as well as health-care devices for tracking the occurrence of elderly activities. The results demonstrate the accuracy of the proposed framework for managing the tracked records correctly and, thus, generate the appropriate event information related to the ADL.

scholarly journals Predicting Activities of Daily Living with Spatio-Temporal Information

2020 ◽  
Vol 12 (12) ◽  
pp. 214
Author(s):  
Sook-Ling Chua ◽  
Lee Kien Foo ◽  
Hans W. Guesgen
Keyword(s):  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Daily Living ◽  
Independent Living ◽  
Smart Home ◽  
Contextual Information ◽  
Smart Homes ◽  
The Elderly ◽  
Temporal Information ◽  
User Activities

The smart home has begun playing an important role in supporting independent living by monitoring the activities of daily living, typically for the elderly who live alone. Activity recognition in smart homes has been studied by many researchers with much effort spent on modeling user activities to predict behaviors. Most people, when performing their daily activities, interact with multiple objects both in space and through time. The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This paper shows the importance of spatial and temporal information for reasoning in smart homes and demonstrates how such information is represented for activity recognition. Evaluation was conducted on three publicly available smart-home datasets. Our method achieved an average recognition accuracy of more than 81% when predicting user activities given the spatial and temporal information.

scholarly journals A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment

Informatics ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 6 ◽  
Author(s):  
Isibor Ihianle ◽  
Usman Naeem ◽  
Syed Islam ◽  
Abdel-Rahman Tawil
Keyword(s):  
Activities Of Daily Living ◽  
Home Environment ◽  
Daily Living ◽  
Hybrid Approach ◽  
Object Use
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