Can smartwatches replace smartphones for posture tracking?

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Validity of consumer-based physical activity monitors and calibration of smartphone for prediction of physical activity energy expenditure

Accelerometry-based activity monitors have become the standard objective method for assessing physical activity (PA) in field-based research [1]. They are small, non-invasive, easy-to use, and provide an objective indicator of physical activity over extended periods of time. The main advantage from a research perspective is they provide an objective indicator of physical activity behavior, thereby avoiding common sources of error in subjective measurement (e.g., self-report). Because of their storage data capacity, it is possible to monitor behavior over extended periods of time and easy to download the information to a computer for processing. Numerous studies have been published on the reliability and validity of various accelerometry-based physical activity monitors. They have become widely accepted in the field. Over the years, advances in technology have contributed to dramatic improvements in the sophistication of accelerometry-based monitors. Most monitors today now use 3-dimensional accelerometers with higher sampling rates to provide more detailed information. The use of solid-state construction in most devices has also improved reliability and durability of this class of activity monitor. There have also been many advances in methodologies to process accelerometer data, including more standardization in protocols, better handling of missing data, and the application of complex, pattern recognition techniques to distinguish among various types of movement. These advances have collectively helped advance the science (and practice) of physical activity monitoring, but there are numerous challenges that remain to improve the utility\u27s accuracy of accelerometry-based physical activity monitors. One of the most challenging problems has been equating output from different accelerometry-based devices. In theory, accelerometry-based devices all measure the same thing (body acceleration). However, there is considerable variability in sensor properties, filtering, and scaling across different monitoring devices. This has made it impossible to directly compare data from competing instruments. While accelerometers internally measure acceleration in g-forces, most commercially-available devices report data using dimensionless units referred to as counts. Considerable research has been completed to calibrate the various devices against criterion measures, but presently it is not possible to directly equate output from one device to another in a systematic method. In recent years, many new, competing technologies, including built-in accelerometer Smartphones, have been released into the market. These have further compounded the challenge of comparing accelerometry-based devices. In many cases, these devices are released into the marketplace with little or no evidence of their validity. These accelerometry-based physical activity monitors have been used almost exclusively for research, but advances in technology have led to an explosion of new consumer-based activity monitors designed for use by individuals interested in fitness, health, and weight control. Examples include the BodyMedia FIT, Fitbit, Basis B1, Jawbone Up, NikeFuel band, DirectLife, PAM, and Smartphone applications. The development of these consumer-based monitors and applications has been driven, in large part, by the increased availability of low cost accelerometer technology that came about with the incorporation of accelerometers in the Wii. Refinement of other technologies (e.g., Bluetooth) and increased sophistication of websites and personalized social media applications also spurred the movement. These new accelerometry-based monitors provide consumers with the ability to estimate PA and energy expenditure (EE), and track data over time on a personalized web interface. Other technologies have also been adapted to capitalize on consumer interest in health and wellness. Pedometers developed originally to measure steps have been calibrated to estimate EE and to store data over time. Geographical positioning system (GPS) monitors, developed primarily for use in navigation, are now marketed to athletes and recreation enthusiasts to monitor speed and EE from activity. Heart rate monitors, originally marketed to athletes, have also been modified and marketed to appeal to more recreational athletes interested in health and weight control. These devices typically provide an easy-to-use web-interface to enable consumers to monitors PA and EE over time. The increased availability of monitoring technology provides consumers with options for self-monitoring, but these tools may also have applications for applied field-based research or intervention applications designed to promote PA in the population. To date, there is little or no information available to substantiate the validity of these consumer-based activity monitors and accelerometry-based mobile phone applications to assess PA and EE under free-living conditions. It is important to formally evaluate the validity of these various devices so information can be shared with researchers, fitness professionals, and consumers. The series of papers presented in this dissertation will provide a better understanding of the validity of consumer-based physical activity monitors and also evaluate the potential of estimating energy expenditure using built-in technology in Smartphones. The first study (Chapter 3) specifically evaluated the utility of various consumer-based, physical activity, monitoring tools against indirect calorimetry. A unique aspect of this study is that the consumer-based monitors were also directly compared with results from an Actigraph monitor, the most commonly used research-grade monitor used in the field. The second study (Chapter 4) explored the feasibility and utility of using embedded sensors in smart phones for objective activity monitoring. While consumer-monitors are designed to be convenient and easy to use, it is still somewhat burdensome for individuals to have to wear or carry another device. The embedded sensors in current Smartphones (e.g. accelerometers and gyroscope) may have similar (or better) utility than current research or consumer monitors. However, before this can be done, methods need to be developed to compile and use the raw sensor data. Machine learning techniques are widely used in pattern recognition technology and they have been increasingly used in accelerometry-based monitors to detect underlying patterns in the data. In this second study, machine learning techniques are tested to determine the most optimal way to classify physical activity patterns using Smartphone data. Once this is done it will be possible to develop prediction equations that can convert the raw data into estimates of energy expenditure and/or quantify levels of physical activity (see image below). The two studies will advance research on physical activity monitoring techniques and specifically determine the feasibility of utilizing embedded sensors in Smartphones to capture physical activity data under free living conditions. A comprehensive literature review is provided in the next section to summarize the progression of research in this area and to explain the methods for the present study

Continuous physical activity recording - Consumer-based activity trackers in epidemiological studies

Physical activity is an important modifiable lifestyle factor that can improve general health and reduce the risk of disease. Currently, collecting data on physical activity in epidemiological studies are generally limited to long-term but self-reported and inaccurate physical activity questionnaires and/or using short-term but objective and more accurate accelerometers. Consumer-based activity trackers are designed for long-term objective data collection and can therefore potentially be used to close this gap. The objective of this dissertation was therefore to explore and develop new methods for collecting data on physical activity in epidemiological studies using consumer-based activity trackers. The four included papers apply different methods to explore the objective from multiple angles. Results includes an overview of how activity tracker sensor support has changed over time, recommendations when choosing an activity tracker model for future physical activity research, recommendations for increasing activity tracker wear time among participants in clinical studies, as well as knowledge about activity tracker validity and physical activity trends during the Norwegian COVID-19 lockdown in 2020. Finally, the dissertation describes a system for automatic and continuous data collection using consumer-based activity trackers from multiple providers. We show the usability of this system by accessing and analysing historic activity tracker data from participants who wore a tracker before-, during-, and after the COVID-19 lockdown period. The proposed system can be a valuable addition to existing methods for physical activity assessment by contributing to closing the above-mentioned method gap

Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer

The main goal of this paper consists on the adaption and validation of a method for the measurement of the energy expenditure during physical activities. Sensors available in a mobile device, e.g., a smartphone, a smartwatch, or others, allow the capture of several signals, which may be used to the estimation of the energy expenditure. The adaption consists in the comparison between the units of the data acquired by a tri-axial accelerometer and a mobile device accelerometer. The tests were performed by healthy people with ages between 12 and 50 years old that performed several activities, such as standing, gym (walking), climbing stairs, walking, jumping, running, playing tennis, and squatting, with a mobile device on the waist. The validation of the method showed that the energy expenditure is underestimated and super estimated in some cases, but with reliable results. The creation of a validated method for the measurement of energy expenditure during physical activities capable for the implementation in a mobile application is an important issue for increase the acceptance of the mobile applications in the market. As verified the results obtained are around 124.6 kcal/h, for walking activity, and 149.7 kcal/h, for running activity.This work was supported by FCT project PEst-OE/EEI/L A0008/2013 (Este trabalho foi suportado pelo projecto FCT PEst-OE/EEI/LA0008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Custom-designed motion-based games for older adults: a review of literature in human-computer interaction

Many older adults, particularly persons living in senior residences and care homes, lead sedentary lifestyles, which reduces their life expectancy. Motion-based video games encourage physical activity and might be an opportunity for these adults to remain active and engaged; however, research efforts in the field have frequently focused on younger audiences and little is known about the requirements and benefits of motion-based games for elderly players. In this paper, we present an overview of motion-based video games and other interactive technologies for older adults. First, we summarize existing approaches towards the definition of motion-based video games – often referred to as exergames – and suggest a categorization of motion-based applications into active video games, exergames, and augmented sports. Second, we use this scheme to classify case studies addressing design efforts particularly directed towards older adults. Third, we analyze these case studies with a focus on potential target audiences, benefits, challenges in their deployment, and future design opportunities to investigate whether motion-based video games can be applied to encourage physical activity among older adults. In this context, special attention is paid to evaluation routines and their implications regarding the deployment of such games in the daily lives of older adults. The results show that many case studies examine isolated aspects of motion-based game design for older adults, and despite the broad range of issues in motion-based interaction for older adults covered by the sum of all research projects, there appears to be a disconnect between laboratory-based research and the deployment of motion-based video games in the daily lives of senior citizens. Our literature review suggests that despite research results suggesting various benefits of motion-based play for older adults, most work in the field of game design for senior citizens has focused on the implementation of accessible user interfaces, and that little is known about the long-term deployment of video games for this audience, which is a crucial step if these games are to be implemented in activity programs of senior residences, care homes, or in therapy