Investigating telemonitoring technologies for the detection of activities and the application of BLE in smart homes for elderly independent living

Abstract

Due to the rise in the elderly population and the prevalence of chronic diseases, healthcare organizations around the world are faced with an economic burden which will continue to grow. For this reason there is an urgent demand to reduce the intake of elders in hospitals and nursing homes by allowing them to live independently for greater lengths of time. In response to this demand, researchers are strongly focusing on 'telemonitoring', which is the use of information technology (IT) to monitor the health status of an individual from a remote location (e.g. their home). The first aim of this thesis was to investigate how telemonitoring technologies can detect elderly activities for health assessment purposes. To assess a patient's health status holistically, a wide variety of factors needed be considered by practitioners. Currently many telemonitoring technologies in research have addressed these factors/assessments by monitoring elderly activities. However based on the literature reviews, researchers have not been able to develop a comprehensive understanding of how these technologies support each assessment. Therefore the first contribution in chapter 3 of this thesis addresses this gap. A literature review was conducted where 215 telemonitoring technologies were identified from 82 papers, published between 2000 and 2016. Six assessments which involve monitoring of activities were identified as (1) mobility, (2) nutrition, (3) safety, (4) cognitive, (5) social, and (6) routine. All included technologies were categorized into six tables according to the assessment that they supported. From assessing the contents of these tables, it was found that a significant portion of ITs relate to mobility, nutritional, safety and routine assessments. Many of the studies were found to assess technologies inside of laboratory setting and still require improvement before they are suited for real world application. It also found that many of the technologies were not equipped with wireless communication. In recent years recently have been focusing been integrating wireless sensing technology into telemonitoring applications. Many of these wireless technologies are small, unobtrusive, and usually need to be powered by small batteries (e.g. coin cell) which have limited capacity. For this reason, researchers have had difficulties prolonging battery life to a duration that is practical. However the recent release of Bluetooth Low Energy (BLE) has the potential of resolving this issue due to its power saving qualities. The 2nd aim of this thesis is to assess the performance of Bluetooth Low Energy (BLE) in telemonitoring frameworks using advertising mode. Advertising mode is often used for device discovery purposes, however it can also be used to send context data without the need for device connection establishment. This method has received little investigation from researchers and yet it has the potential of offering advantages such as reduction in power consumption and manufacturing costs. Therefore in this thesis, the performance of BLE advertising mode was used within two telemonitoring applications. Firstly, a new device called 'BLUESOUND' is proposed. The device uses ultrasound sensing technology which can efficiently differentiate multiple residents in a home environment based on their height. The device consists of three sensing/communication modules: A Passive Infrared (PIR) occupancy module, an ultrasound array module and a BLE communication module. The PIR occupancy module is used to detect walking direction, while the ultrasound array measures the resident's height. The combination of these two technologies can also be used to detect a resident's velocity. BLE advertising mode is used to communicate acquired data to a smart phone gateway/database. A new embedded algorithm was able to increase the energy efficiency of the identification technology. Comprehensive modelling and experimentation was undertaken to assess the performance the BLUESOUND device. The BLUESOUND device was able to distinguish between multiple resident identities by measuring height accurately. Currently researchers have developed various wearable ECG monitors as there is a demand to detect the onset of cardiac disease earlier in the elderly population. However most of these devices have only lasted a couple of days on a coin cell battery which is not practical. Therefore the performance of BLE advertising mode was explored using a virtual BLE based ECG model in MATLAB. To further minimize power consumption, an ECG extraction technique (based on the 'So and Chan' algorithm [1]) was used in the model to extract some of the most significant points on the signal. Based on three simulation trails, ECG data was transferred to a scanning device with high accuracy (average of 99.62%). It was estimated that the virtual system is approximately 13 times more energy efficient compared to sending ECG stream data continuously when a connection is established.