Uncertainty Investigation for Personalised Lifelogging Physical Activity Intensity Pattern Assessment with Mobile Devices

Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual’s PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices

Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator

SAW Delay Line based Smart Sensing in Water Distribution System

Wireless Passive Surface Acoustic Wave (SAW) sensors have attracted great attention in numerous applications. They are powered by interrogating Radio Frequency (RF) pulses rather than batteries. In this study, a wireless passive SAW temperature and pressure delay line sensor was adapted in a designed framework which can control the ambient liquid temperature and pressure and characterised. The experimental results meet the theoretical analysis that the related phase delay of the response signal depends linearly on the temperature (pressure) when the pressure (temperature) keeps constant

Internet of Things Enabled Technologies for Behaviour Analytics in Elderly Person Care: A Survey

The advances in sensor technology over recent years has provided new ways for researchers to monitor the elderly in uncontrolled environments. 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 natural setting, which can lead to more useful data. This is important for those that may be suffering from mental illness as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However there are various challenges that need to be addressed ranging from issues with sensors to the involvement of human factors. It is vital that these challenges are taken into consideration along with the major behavioural symptoms that can appear in an Elderly Person. For a person suffering with Dementia, the application of sensor technologies can improve the quality of life of the person and also monitor the progress of the disease through behavioural analysis. This paper will consider the behaviours that can be associated with dementia and how these behaviours can be monitored through sensor technology. We will also provide an insight into some sensors and algorithms gathered through survey in order to provide advantages and disadvantages of these technologies as well as to present any challenges that may face future research

A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Due to the many beneficial effects on physical and mental health and strong association with many fitness and rehabilitation programs, physical activity (PA) recognition has been considered as a key paradigm for internet of things (IoT) healthcare. Traditional PA recognition techniques focus on repeated aerobic exercises or stationary PA. As a crucial indicator in human health, it covers a range of bodily movement from aerobics to anaerobic that may all bring health benefits. However, existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we attempt to detect more gym physical activities (GPAs) in addition to traditional PA using acceleration, A two layer recognition framework is proposed that can classify aerobic, sedentary and free weight activities, count repetitions and sets for the free weight exercises, and in the meantime, measure quantities of repetitions and sets for free weight activities. In the first layer, a one-class SVM (OC-SVM) is applied to coarsely classify free weight and non-free weight activities. In the second layer, a neural network (NN) is utilized for aerobic and sedentary activities recognition; a hidden Markov model (HMM) is to provide a further classification in free weight activities. The performance of the framework was tested on 10 healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/ and compared with some typical classifiers. The results indicate the proposed framework has better performance in recognizing and measuring GPAs than other approaches. The potential of this framework can be potentially extended in supporting more types of PA recognition in complex applications

Effectively Measuring Respiratory Flow with Portable Pressure Data using Back Propagation Neural Network

Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in uncontrolled environments is also a problem. For portable breathing equipment, due to the volume limit, the pressure signals acquired by using the airway tube may be too weak and contain some noisy, leading to huge errors in respiratory flow measures. In this paper, a cost-effective portable pressure sensor based respiratory measure device is designed. This device has a new airway tube design, which enables the pressure drop efficiently after the air flowing through the airway tube. Also, a new back propagation (BP) neural network based algorithm is proposed to stablise the device calibration and remove pressure signal nosie. For improving the reability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of proposed BP neural network algorithm have been carried out. The results show that giving proper parameters setting, the proposed BP neural network algorithm is capable of efficiently improving the reliability of new designed respiratory device

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

Link quality aware channel allocation for multichannel body sensor networks.

Body Sensor Network (BSN) is a typical Internet-of-Things (IoT) application for personalized health care. It consists of economically powered, wireless and implanted medical monitoring sensor nodes, which are designed to continually collect the medical information of the target patients. Multichannel is often used in BSNs to reduce the spectrum competition of the tremendous sensor nodes and the problem of channel assignment has attracted much research attention. The health sensing data in BSNs is often required to be delivered to a sink node (or server) before a certain deadline for real time monitoring or health emergency alarm. Therefore, deadline is of significant importance for multichannel allocation and scheduling. The existing works, though designed to meet the deadline, often overlook the impact of the unreliable wireless links. As a result, the health sensing data can still be overdue because of the scheduled lossy links. Besides, potential collisions in the schedules also incur considerable delay in delivering the sensing data. In this paper, we propose a novel deadline- driven Link quality Aware Channel Assignment scheme (LACA), where link quality, deadlines and collisions are jointly considered. LACA prioritizes links with urgent deadlines and heavy collisions. Besides, LACA allows the exploition of the spare slots for retransmissions on lossy links, which can further reduce the retransmission delay. Extensive simulation experiments show that compared to the existing approaches, LACA can better utilize the wireless spectrum and achieve higher packet delivery ratio before the deadline.N/

Exploring the effectiveness of the design-led intervention in reducing screen time and enhancing exercise for children in the home environment

Considering the rapid rise in obesity, this thesis suggests the notion that obesity can be tackled beyond conventional ways. It is widely reported in the literature that the epidemic of obesity is only going to get worse in the next 10 years. A change is needed to allow people to have more control over living healthier through improving physical and social environments, as these have a strong influence in shaping lifestyles. Existing research is converging on the view that modifying lifestyle behaviours is a viable approach to tackling this global epidemic. Therefore, this thesis proposes a unique intervention approach, focused on instigating sustainable behaviour through a design-led intervention. This novel approach attempted to facilitate a change in participants' sedentary lifestyles and prompt daily exercise behaviours. Physical inactivity amongst children and young people has been identified as a serious public health concern in relation to increases in obesity, with figures on screen time reaching a worrisome peak. The current study demonstrated that making small changes to people's daily routine could result in habitualising healthier behaviours. This thesis further analysed some fundamental theories/models that have been applied in stimulating behaviour changes in different disciplines. Following a thorough literature review of a broad range of behavioural change studies, including reported case studies, a framework was developed. It proposed the stages of intended habitualisation of this new behaviour and informed the development of a research tool artefact which was employed for the main study of this thesis. To evaluate the effectiveness of this intervention, a longitudinal study called the Domestic User Study (DUS) was undertaken to discover the habitual changes over time in home environments. A total of 20 households participated in DUS and used the prototype (design-led intervention) for a total duration of three months. Their behaviour change data were captured through means of monitoring, pre/post interviews, and mid/post surveys. The evaluation explored three fundamental research questions: 1) Did the design-led intervention help in building exercise behaviour? 2) Has the user's TV watching behaviour changed as a result of the design-led intervention? 3) Has Knudgbox created habitualised sustainable behaviour? Some key constructs were emerged to form the theoretical framework of this thesis and were measured throughout the DUS. The results uncovered the key determinants that influenced participants' behaviour when exposed to the design-led intervention. After a comprehensive analysis, three main dimensions are presented that constitute the user habitualisation process: visual feedback, user empowerment, and regulation. The joint impact of these dimensions on sustainable behaviour suggests that, when incorporated, they can bring forth habitual changes in the target demographic and beyond. Overall, this study suggests that design-led interventions can have a positive effect on instigating exercise behaviour in the home environment. The recommended behaviour habitualisation model should be considered in the future implementation of design led approaches in order to improve the chances of success