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.

Anomaly detection in the Activities of Daily Living (ADL) of older adults is essential for healthcare management, to act to avoid prospective problems early and improve this group's quality of life. Once ADLs are recognised, the gathered information will be utilised to detect anomalies in comparison with routine activities. Existing methods provide some limited reliabilities in detecting the anomalous events in ADLs, particularly due to ignoring the changes in individuals' routine. Therefore, it is important to develop an appropriate method or algorithm that can efficiently detect anomalies in older adults' daily activities. The focus of this study is to distinguish and detect anomalies in ADLs based on data gathered from ambient sensors. In this paper, a novel method based on a Multi-scale Fuzzy Entropy measure to discriminate between normal and anomalous cases in ADLs with a high degree of accuracy is investigated. Experimental evaluation is conducted to detect anomalies in ADL data obtained from two different datasets (ADL and CASAS). The experimental results show that the proposed method can detect anomalous instances with a high degree of accuracy. Comparisons with other methods have also offered support to the proposed method.

Falls are considered as one of the greatest risks and a fundamental problem in health-care for older adults living alone at home. The number of older adults living alone in their own homes is increasing worldwide due to the high expense of health care services. Therefore, it is important to develop an accurate system with the ability to detect human falls during daily activities. The focus of this study is to distinguish and detect human falls in Activities of Daily Living (ADL) based on data acquired from an accelerometer device. In this paper, a novel method based on Fuzzy Entropy measure is investigated to detect and distinguish human fall from other activities with a high degree of accuracy. The proposed method is tested and evaluated based on a publicly available URFD dataset. The experimental results show that Fuzzy Entropy achieved a sensitivity and specificity of 100% and 97.8%, respectively. Comparisons with other methods have also provided further support to the proposed method.

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.

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.

Ambient sensor systems in an intelligent environment are often used to monitor and infer Activities of Daily Living (ADL). Existing research on the recognition of ADL from ambient sensors assumes that the intelligent environment is inhabited by a single-occupant (older adults). However, in the real environment there may be situations in which there is more than one occupant for a time. The focus of this study is to distinguish the number of people in the home environment by employing only PIR sensors in order to identify whether the environment is used by one person or more. In this paper, two different techniques, Fuzzy Entropy (FuzzyEn) and Indoor Mobility (IM), are investigated to distinguish the activities within a multi-occupancy environment. The model is tested and evaluated based on a set of data representing a multi-occupancy environment. The experiment results show that FuzzyEn and IM can detect and identify the existence of a visitor in a home environment with an accuracy of 100% and 98.7%, respectively.

Many smart house systems have been presented, but there are still some limitations in terms of the high cost, less functionality, difficulty of use that are not satisfactorily reliable and cannot be developed. Therefore, the aim of this research project was to design and develop a prototype for a smart house system that is low cost, has a user-friendly interface, and is scalable and reliable by using an integrated system of hardware and software. The hardware, such as Arduino Uno, servo motors, a temperature sensor, a motion sensor and a battery, was utilized to develop the prototype of a smart house system. The software included Arduino integrated development environment (IDE) to compile the code in hardware and using MIT App Inventor for Android mobile phones to interfacing a mobile handset with hardware. Moreover, In order to make the design of prototype more professional, Sweet Home 3D software was used.The system has been evaluated with the previous works and it was demonstrated to six experts to obtain some feedback on the prototype. The testing of the prototype demonstrated that the system was an integrated, practical and easy to use, and any new device could easily be installed into the system. The aim of this research project has been achieved successfully. However, the prototype requires further developments, which include the power supply reliability, use other wireless technologies such as Wi-Fi and ZigBee, and cross-platform apps to work with differently operated systems such as iOS for people use Apple devices by using MIT App Inventor.