Implementation of non-intrusive appliances load monitoring (NIALM) on k-nearest neighbors (k-NN) classifier

Nonintrusive Appliance Load Monitoring (NIALM) is used to analyze individual’s house energy consumption by distinguishing variations in voltage and current of appliances in a household. The method identifies load consumption of each appliance from the aggregated home energy consumption. NIALM will also provide information of load consumptions of each appliance by indirectly detecting the abnormal changes of appliance usage. The proposed NIALM approach is based on features extraction from load consumptions measurements of electrical power signals in order to classify appliance’s state of operation. In this work, we have improved the identification accuracy and the detection of appliances based on their operational state by employing Machine Learning (ML) technique; namely k-nearest neighbor (k-NN) classification algorithm. The dataset used to perform this process is from the publicly available (PLAID) of power, voltage and current signals of appliances from several houses. This is used as benchmark data set. The PLAID dataset is collected and processed for each appliance and our classification results based on k-NN algorithm achieved high accuracy and is able to gain cost-effective solution. In addition, the result shows that k-NN classifier is a proven as an efficient method for NIALM techniques when compared with other proposed different ML options. Based on the used dataset, the average F-score measure obtained using the k-NN classifier is 90%. Possible reasons behind these findings are discussed and areas for further exploration are proposed.</p

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.Comment: Accepted for Publication in IEEE Access DOI: 10.1109/ACCESS.2020.303771

Investigation of Low-Cost Wearable Internet of Things Enabled Technology for Physical Activity Recognition in the Elderly

Technological advances in mobile sensing technologies has produced new opportunities for the monitoring of the elderly in uncontrolled environments by researchers. Sensors have become smaller, cheaper and can be worn on the body, potentially creating a network of sensors. Smart phones are also more common in the average household and can also provide some behavioural analysis due to the built-in sensors. As a result of this, researchers are able to monitor behaviours in a more naturalistic setting, which can lead to more contextually meaningful data. For those suffering with a mental illness, non-invasive and continuous monitoring can be achieved. Applying sensors to real world environments can aid in improving the quality of life of an elderly person with a mental illness and monitor their condition through behavioural analysis. In order to achieve this, selected classifiers must be able to accurately detect when an activity has taken place. In this thesis we aim to provide a framework for the investigation of activity recognition in the elderly using low-cost wearable sensors, which has resulted in the following contributions: 1. Classification of eighteen activities which were broken down into three disparate categories typical in a home setting: dynamic, sedentary and transitional. These were detected using two Shimmer3 IMU devices that we have located on the participants’ wrist and waist to create a low-cost, contextually deployable solution for elderly care monitoring. 2. Through the categorisation of performed Extracted time-domain and frequency-domain features from the Shimmer devices accelerometer and gyroscope were used as inputs, we achieved a high accuracy classification from a Convolutional Neural Network (CNN) model applied to the data set gained from participants recruited to the study through Join Dementia Research. The model was evaluated by variable adjustments to the model, tracking changes in its performance. Performance statistics were generated by the model for comparison and evaluation. Our results indicate that a low epoch of 200 using the ReLu activation function can display a high accuracy of 86% on the wrist data set and 85% on the waist data set, using only two low-cost wearable devices