Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

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