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Smart Sensing Technologies for Personalised Coaching

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 October 2017) | Viewed by 81933

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Special Issue Editors

Dr. Oresti Banos

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Guest Editor

Research Center for Information and Communication Technologies, University of Granada, 18014 Granada, Spain
Interests: human-aware computing; behaviour and context modeling; intelligent coaching systems; smart ubiquitous sensing; digital health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hermie Hermens

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Guest Editor

Head of Telemedicine Group, Roessingh Research and Development, 7500 AH Enschede, The Netherlands
Interests: telemedicine; remote monitoring and treatment; smart artificial coaching

Prof. Dr. Christopher Nugent

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Guest Editor

School of Computing, Ulster University, 2-24 York Street, Belfast BT15 1AP, UK
Interests: ambient assisted living; smart homes; smart environments; activity recognition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hector Pomares

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Guest Editor

Department of Computer Architecture and Computer Technology, Higher Technical School of Information Technology and Telecommunications Engineering, CITIC-UGR, University of Granada, 18011 Granada, Spain
Interests: time series; forecasting; complex systems; HPC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lifestyle choices can have a tremendous impact on people’s health and wellness. Avoiding unhealthy habits is, nowadays, a priority, and to achieve this goal, ground-breaking mechanisms are required to automatically and autonomously identify and eventually change people’s behaviours. An increasing number of smart ubiquitous sensing technologies are being developed all over the world to coach people on healthier, as well as more responsible behaviours, providing them timely and ubiquitously with personalised information and support. This Special Issue aims at bringing together the latest experiences, findings and developments on the smart sensing, modelling and understanding of human behaviour for the provision of personalised coaching and support services.

We invite novel, innovative and exciting contributions including sensors used for behavior monitoring and coaching, validity and reliability of sensing modality, and modeling of behavior and coaching strategies. This Special Issue is further interested in:

wearable, mobile and ubiquitous health sensing systems,
mobile social networks,
behavioural grouping and participatory sensing,
context-awareness and semantic modelling,
physical and virtual coaching systems,
benchmarking, datasets and simulation tools that have been applied to study behavior and/or support coaching.

Dr. Oresti Banos
Prof. Dr. Hermie Hermens
Prof. Dr. Christopher Nugen
Prof. Dr. Hector Pomares
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

behavior modelling
activity recognition
emotion recognition
location tracking
social sensing
wearable, mobile and ubiquitous computing
intelligent coaching
virtual agents

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Published Papers (12 papers)

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Editorial

Jump to: Research

4 pages, 165 KiB

Open AccessEditorial

Smart Sensing Technologies for Personalised e-Coaching

by Oresti Banos, Hermie Hermens, Christopher Nugent and Hector Pomares

Sensors 2018, 18(6), 1751; https://doi.org/10.3390/s18061751 - 29 May 2018

Cited by 4 | Viewed by 5381

Abstract

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people to have healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support, but also gives us an incentive to set goals and do more. This paper outlines some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

Research

Jump to: Editorial

16 pages, 1889 KiB

Open AccessArticle

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

by Muhammad Fahim, Thar Baker, Asad Masood Khattak, Babar Shah, Saiqa Aleem and Francis Chow

Sensors 2018, 18(3), 874; https://doi.org/10.3390/s18030874 - 15 Mar 2018

Cited by 18 | Viewed by 5410

Abstract

Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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15 pages, 2306 KiB

Open AccessArticle

Personalized Physical Activity Coaching: A Machine Learning Approach

by Talko B. Dijkhuis, Frank J. Blaauw, Miriam W. Van Ittersum, Hugo Velthuijsen and Marco Aiello

Sensors 2018, 18(2), 623; https://doi.org/10.3390/s18020623 - 19 Feb 2018

Cited by 54 | Viewed by 9960

Abstract

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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27 pages, 3300 KiB

Open AccessArticle

Design and Evaluation of a Pervasive Coaching and Gamification Platform for Young Diabetes Patients

by Randy Klaassen, Kim C. M. Bul, Rieks Op den Akker, Gert Jan Van der Burg, Pamela M. Kato and Pierpaolo Di Bitonto

Sensors 2018, 18(2), 402; https://doi.org/10.3390/s18020402 - 30 Jan 2018

Cited by 57 | Viewed by 9794

Abstract

Self monitoring, personal goal-setting and coaching, education and social support are strategies to help patients with chronic conditions in their daily care. Various tools have been developed, e.g., mobile digital coaching systems connected with wearable sensors, serious games and patient web portals to personal health records, that aim to support patients with chronic conditions and their caregivers in realizing the ideal of self-management. We describe a platform that integrates these tools to support young patients in diabetes self-management through educational game playing, monitoring and motivational feedback. We describe the design of the platform referring to principles from healthcare, persuasive system design and serious game design. The virtual coach is a game guide that can also provide personalized feedback about the user’s daily care related activities which have value for making progress in the game world. User evaluations with patients under pediatric supervision revealed that the use of mobile technology in combination with web-based elements is feasible but some assumptions made about how users would connect to the platform were not satisfied in reality, resulting in less than optimal user experiences. We discuss challenges with suggestions for further development of integrated pervasive coaching and gamification platforms in medical practice. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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21 pages, 3760 KiB

Open AccessArticle

Smart Device-Based Notifications to Promote Healthy Behavior Related to Childhood Obesity and Overweight

by Gustavo López, Iván González, Elitania Jimenez-Garcia, Jesús Fontecha, Jose A. Brenes, Luis A. Guerrero and José Bravo

Sensors 2018, 18(1), 271; https://doi.org/10.3390/s18010271 - 18 Jan 2018

Cited by 4 | Viewed by 6923

Abstract

Obesity is one of the most serious public health challenges of the 21st century and it is a threat to the life of people according to World Health Organization. In this scenario, family environment is important to establish healthy habits which help to reduce levels of obesity and control overweight in children. However, little efforts have been focused on helping parents to promote and have healthy lifestyles. In this paper, we present two smart device-based notification prototypes to promote healthy behavior with the aim of avoiding childhood overweight and obesity. The first prototype helps parents to follow a healthy snack routine, based on a nutritionist suggestion. Using a fridge magnet, parents receive graphical reminders of which snacks they and their children should consume. The second prototype provides a graphical reminder that prevents parents from forgetting the required equipment to practice sports. Prototypes were evaluated by nine nutritionists from three countries (Costa Rica, Mexico and Spain). Evaluations were based on anticipation of use and the ergonomics of human–system interaction according to the ISO 9241-210. Results show that the system is considered useful. Even though they might not be willing to use the system, they would recommend it to their patients. Based on the ISO 9241-210 the best ranked features were the system’s comprehensibility, the perceived effectiveness and clarity. The worst ranked features were the system’s suitability for learning and its discriminability. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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21 pages, 6602 KiB

Open AccessArticle

Increasing the Intensity over Time of an Electric-Assist Bike Based on the User and Route: The Bike Becomes the Gym

by Daniel H. De La Iglesia, Juan F. De Paz, Gabriel Villarrubia González, Alberto L. Barriuso, Javier Bajo and Juan M. Corchado

Sensors 2018, 18(1), 220; https://doi.org/10.3390/s18010220 - 14 Jan 2018

Cited by 19 | Viewed by 6743

Abstract

Nowadays, many citizens have busy days that make finding time for physical activity difficult. Thus, it is important to provide citizens with tools that allow them to introduce physical activity into their lives as part of the day’s routine. This article proposes an app for an electric pedal-assist-system (PAS) bicycle that increases the pedaling intensity so the bicyclist can achieve higher and higher levels of physical activity. The app includes personalized assist levels that have been adapted to the user’s strength/ability and a profile of the route, segmented according to its slopes. Additionally, a social component motivates interaction and competition between users based on a scoring system that shows the level of their performances. To test the training module, a case study in three different European countries lasted four months and included nine people who traveled 551 routes. The electric PAS bicycle with the app that increases intensity of physical activity shows promise for increasing levels of physical activity as a regular part of the day. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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20 pages, 4561 KiB

Open AccessArticle

The Feasibility and Usability of RunningCoach: A Remote Coaching System for Long-Distance Runners

by Daniel Aranki, Gao Xian Peh, Gregorij Kurillo and Ruzena Bajcsy

Sensors 2018, 18(1), 175; https://doi.org/10.3390/s18010175 - 10 Jan 2018

Cited by 14 | Viewed by 5994

Abstract

Studies have shown that about half of the injuries sustained during long-distance running involve the knee. Cadence (steps per minute) has been identified as a factor that is strongly associated with these running-related injuries, making it a worthwhile candidate for further study. As such, it is critical for long-distance runners to minimize their risk of injury by running at an appropriate running cadence. In this paper, we present the results of a study on the feasibility and usability of RunningCoach, a mobile health (mHealth) system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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6432 KiB

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Real-Time Monitoring in Home-Based Cardiac Rehabilitation Using Wrist-Worn Heart Rate Devices

by Javier Medina Quero, María Rosa Fernández Olmo, María Dolores Peláez Aguilera and Macarena Espinilla Estévez

Sensors 2017, 17(12), 2892; https://doi.org/10.3390/s17122892 - 12 Dec 2017

Cited by 24 | Viewed by 6319

Abstract

Cardiac rehabilitation is a key program which significantly reduces the mortality in at-risk patients with ischemic heart disease; however, there is a lack of accessibility to these programs in health centers. To resolve this issue, home-based programs for cardiac rehabilitation have arisen as a potential solution. In this work, we present an approach based on a new generation of wrist-worn devices which have improved the quality of heart rate sensors and applications. Real-time monitoring of rehabilitation sessions based on high-quality clinical guidelines is embedded in a wearable application. For this, a fuzzy temporal linguistic approach models the clinical protocol. An evaluation based on cases is developed by a cardiac rehabilitation team. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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18652 KiB

Open AccessArticle

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities

by Kee-Hoon Kim and Sung-Bae Cho

Sensors 2017, 17(12), 2877; https://doi.org/10.3390/s17122877 - 11 Dec 2017

Cited by 15 | Viewed by 4700

Abstract

Recently, recognizing a user’s daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user’s obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the “Five W’s”, and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54–14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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5162 KiB

Open AccessArticle

Creating Affording Situations: Coaching through Animate Objects

by Chris Baber, Ahmad Khattab, Martin Russell, Joachim Hermsdörfer and Alan Wing

Sensors 2017, 17(10), 2308; https://doi.org/10.3390/s17102308 - 11 Oct 2017

Cited by 8 | Viewed by 4397

Abstract

We explore the ways in which animate objects can be used to cue actions as part of coaching in Activities of Daily Living (ADL). In this case, changing the appearance or behavior of a physical object is intended to cue actions which are appropriate for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. We present initial design prototypes and simple user trials which explore the impact of different cues on activity. It is shown that raising the handle of a jug, for example, not only cues the act of picking up the jug but also encourages use of the hand adjacent to the handle; that combinations of lights (on the objects) and auditory cues influence activity through reducing uncertainty; and that cueing can challenge pre-learned action sequences. We interpret these results in terms of the idea that the animate objects can be used to create affording situations, and discuss implications of this work to support relearning of ADL following brain damage or injury, such as might arise following a stroke. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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2818 KiB

Open AccessArticle

Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity

by Michel C. A. Klein, Adnan Manzoor and Julia S. Mollee

Sensors 2017, 17(6), 1436; https://doi.org/10.3390/s17061436 - 19 Jun 2017

Cited by 34 | Viewed by 8277

Abstract

Lack of physical activity is an increasingly important health risk. Modern mobile technology, such as smartphones and digital measurement devices, provides new opportunities to tackle physical inactivity. This paper describes the design of a system that aims to encourage young adults to be more physically active. The system monitors the user’s behavior, uses social comparison and provides tailored and personalized feedback based on intelligent reasoning mechanisms. As the name suggests, social processes play an important role in the Active2Gether system. The design choices and functioning of the system are described in detail. Based on the experiences with the development and deployment of the system, a number of lessons learnt are provided and suggestions are proposed for improvements in future developments. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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2350 KiB

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Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons

by Avgoustinos Filippoupolitis, William Oliff, Babak Takand and George Loukas

Sensors 2017, 17(6), 1230; https://doi.org/10.3390/s17061230 - 27 May 2017

Cited by 31 | Viewed by 6394

Abstract

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)

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