Advancing Quantified-Self Applications Utilizing Visual Data Analytics and the Internet of Things

Chousiadas, Dimitris; Menychtas, Andreas; Tsanakas, Panayiotis

doi:10.1007/978-3-319-92016-0_24

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…For instance, Ref. [ 11 ] helped participants to examine their daily behaviour by grouping their PA using a k-means algorithm to cluster the mean heartbeat and oxygen saturation values. Ref.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Díaz

Caillaud

Yacef

2022

Sensors

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Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant’s needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Díaz

Caillaud

Yacef

2022

Sensors

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“…QS has been applied to health in several practices, including personal informatics. There are several health tracking devices, including accelerometers, pedometers, smartwatches, wrist-worn devices, wearables biosensors, clothing and wearable textiles, and smartphone applications [30][31][32][33][34].…”

Section: Quantified Self For Healthmentioning

confidence: 99%

Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data

Gómez-Del-Río

González-González

Toledo-Delgado

et al. 2020

Sensors

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At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a considerable amount of tracked activity data. Consequently, our approach is based on the quantified-self model for recommending healthy activities. Gamification can also be used as a mechanism to enhance personalization, increasing user motivation. This paper describes the quantified-self model and its data sources, the activity recommender system, and the PROVITAO App user experience model. Furthermore, it presents the results of a gamified program applied for three years in children with obesity and the process of evaluating the quantified-self model with experts. Positive outcomes were obtained in children’s medical parameters and health habits.

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“…Living in the era of Internet of ings (IoT), Big Data, and AI analytics, telemedicine systems like wearable activity trackers, medical sensors [28], produce valuable health-related data that need to be consumed and analyzed by intelligent platforms using AI and ML technology. ese data are vital for classi cation, prediction, and recommendation engines [1].…”

Section: Introductionmentioning

confidence: 99%

The Health ChatBots in Telemedicine: Intelligent Dialog System for Remote Support

Vasileiou

2022

Journal of Healthcare Engineering

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The objective of the Health Bot is to analyze and elaborate on the implementation of an intelligent system that can support telemedicine services using natural language processing (NLP) technology. Health Bot is a robust, modularized, and user-friendly platform that aims to improve the patient’s interactions with the health care system. Using NLP and speech recognition algorithms, the platform can analyze and classify free text and voice input data to symptoms. The classified data are used in the machine learning (ML) training process of the artificial intelligence (AI) models in order to predict the probability of the patient suffering from a specific disease and get alerted in case of disorders. The Health Bot is acting as a medical virtual agent in order to collect any information needed in the sense of a medical interview and provide medical assessments, close appointments with the doctors, and monitor/record the patient’s health condition. In this paper, two models have been trained for the Covid-19 and heart disease cases with 98.3% and 82% accuracy, respectively.

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Advancing Quantified-Self Applications Utilizing Visual Data Analytics and the Internet of Things

Cited by 8 publications

References 17 publications

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data

The Health ChatBots in Telemedicine: Intelligent Dialog System for Remote Support

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