scholarly journals Self-Organizing Wearable Device Platform for Assisting and Reminding Humans in Real Time

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yu Jin Park ◽  
Ki Eun Seong ◽  
Seol Young Jeong ◽  
Soon Ju Kang
Keyword(s):  
Power Consumption ◽  
Daily Activity ◽  
Independent Living ◽  
Older Persons ◽  
Good Health ◽  
Wearable Device ◽  
High Price ◽  
Activity Data ◽  
Smart Watch ◽  
Self Organizing

Most older persons would prefer “aging in my place,” that is, to remain in good health and live independently in their own home as long as possible. For assisting the independent living of older people, the ability to gather and analyze a user’s daily activity data would constitute a significant technical advance, enhancing their quality of life. However, the general approach based on centralized server has several problems such as the usage complexity, the high price of deployment and expansion, and the difficulty in identifying an individual person. To address these problems, we propose a wearable device platform for the life assistance of older persons that automatically records and analyzes their daily activity without intentional human intervention or a centralized server (i.e., cloud server). The proposed platform contains self-organizing protocols, Delay-Tolerant Messaging system, knowledge-based analysis and alerting for daily activities, and a hardware platform that provides low power consumption. We implemented a prototype smart watch, called Personal Activity Assisting and Reminding (PAAR), as a testbed for the proposed platform, and evaluated the power consumption and the service time of example scenarios.

scholarly journals How Much Walking Should be Advocated for Good Health in Adolescent Girls?

2017 ◽  
Vol 14 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Mhairi MacDonald ◽  
Samantha G. Fawkner ◽  
Ailsa Niven
Keyword(s):  
Adolescent Girls ◽  
Daily Activity ◽  
Good Health ◽  
Moderate Intensity ◽  
Daily Step ◽  
Activity Time ◽  
Activity Data ◽  
Healthy Adolescent ◽  
Health Related ◽  
Moderate Intensity Activity

Background:It is currently not known how much walking should be advocated for good health in adolescent girls. The aim of this study was therefore to recommend health referenced standards for step defined physical activity relating to appropriate health criterion/indicators in a group of adolescent girls.Method:Two hundred and thirty adolescent girls aged between 12 to 15 years volunteered to take part in the study. Each participant undertook measurements (BMI, waist circumference, % body fat, and blood pressure) to define health status. Activity data were collected by pedometer and used to assess daily step counts and accumulated daily activity time over 7 consecutive days.Results:Individuals classified as ‘healthy’ did not take significantly more steps·day–1 nor spend more time in moderate intensity activity than individuals classified as at health risk or with poor health profiles.Conclusion:‘Healthy’ adolescent girls do not walk significantly more in term of steps·day–1 or time spent in activity than girls classified as ‘unhealthy.’ This could suggest that adolescent girls may not walk enough to stratify health and health related outcomes and as a result the data could not be used to inform an appropriate step guideline for this population.

scholarly journals Collecting public RGB-D datasets for human daily activity recognition

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141770907 ◽  
Author(s):  
Hanbo Wu ◽  
Xin Ma ◽  
Zhimeng Zhang ◽  
Haibo Wang ◽  
Yibin Li
Keyword(s):  
Activity Recognition ◽  
Daily Activity ◽  
Visual Cues ◽  
Large Scale ◽  
Hot Spot ◽  
Feature Representation ◽  
Data Sets ◽  
Activity Data ◽  
Data Set ◽  
Depth Motion Maps

Human daily activity recognition has been a hot spot in the field of computer vision for many decades. Despite best efforts, activity recognition in naturally uncontrolled settings remains a challenging problem. Recently, by being able to perceive depth and visual cues simultaneously, RGB-D cameras greatly boost the performance of activity recognition. However, due to some practical difficulties, the publicly available RGB-D data sets are not sufficiently large for benchmarking when considering the diversity of their activities, subjects, and background. This severely affects the applicability of complicated learning-based recognition approaches. To address the issue, this article provides a large-scale RGB-D activity data set by merging five public RGB-D data sets that differ from each other on many aspects such as length of actions, nationality of subjects, or camera angles. This data set comprises 4528 samples depicting 7 action categories (up to 46 subcategories) performed by 74 subjects. To verify the challengeness of the data set, three feature representation methods are evaluated, which are depth motion maps, spatiotemporal depth cuboid similarity feature, and curvature space scale. Results show that the merged large-scale data set is more realistic and challenging and therefore more suitable for benchmarking.

scholarly journals Tracking and Separation of Smart Home Residents through Ambient Activity Sensors

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 29 ◽  
Author(s):  
Sebastian Matthias Müller ◽  
Andreas Hein
Keyword(s):  
Independent Living ◽  
Tracking System ◽  
Error Rates ◽  
Learning Approaches ◽  
Clustering Methods ◽  
Activity Data ◽  
Related Data ◽  
Health Related ◽  
Increasing Demand ◽  
Set Up

To enable independent living for people in need of care and to accommodate the increasing demand of ambulant care due to demographic changes, a multitude of systems and applications that monitor activities and health-related data based on ambient sensors commonly found in smart homes have been developed. When such a system is used in a multi-person household, some form of identification or separation of residents is required. Most of these systems require permanent participation in the form of body-worn sensors or a complicated supervised learning procedure which may take hours or days to set up. To resolve this, we study several unsupervised learning approaches for the separation of activity data of multiple residents recorded with ambient, binary sensors such as light barriers and contact switches. We show how various clustering methods on data from a tracking system can, under optimal conditions, separate the activity of two residents with low error rates (<2%, Rand Index of 0 . 959 ). We also show that imprecisions in the underlying tracking algorithm have a significant impact on the clustering performance and that most of these errors can be corrected by adding a single “identifying sensor area” into the environment. As a consequence, activity monitoring applications need to rely less on body-worn sensors, which may be forgotten or biometric sensors, which may be perceived as a violation of privacy.

scholarly journals “I Did Not Believe You Could Get Better”—Reversal of Diabetes Risk Through Dietary Changes in Older Persons with Prediabetes in Region Stockholm

Nutrients ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2658
Author(s):  
Linda Timm ◽  
Meena Daivadanam ◽  
Anton Lager ◽  
Birger Forsberg ◽  
Claes-Göran Östenson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Older Persons ◽  
Lifestyle Modification ◽  
Qualitative Content Analysis ◽  
Diabetes Risk ◽  
Main Theme ◽  
Lifestyle Modifications ◽  
Activity Data ◽  
Dietary Changes

Diabetes risk can be controlled and even reversed by making dietary changes. The aim of this study was to improve the understanding of how older persons with a high risk of developing Type 2 diabetes manage and relate to information about diabetes risk over a ten-year period. Fifteen qualitative interviews were conducted among participants from the Stockholm Diabetes Prevention Program (SDPP). The participants were asked to recall the health examinations conducted by the SDPP related to their prediabetes and to describe their experiences and potential changes related to diet and physical activity. Data were analyzed using qualitative content analysis. The main theme found was that T2D (type 2 diabetes) risk is not perceived as concrete enough to motivate lifestyle modifications, such as changing dietary patterns, without other external triggers. Diagnosis was recognized as a reason to modify diet, and social interactions were found to be important for managing behavior change. Diagnosis was also a contributing factor to lifestyle modification, while prognosis of risk was not associated with efforts to change habits. The results from this study suggest that the potential of reversing prediabetes needs to be highlighted and more clearly defined for older persons to serve as motivators for lifestyle modification.

Older persons' definitions of good health: implications for general practitioners

1989 ◽  
Vol 150 (8) ◽  
pp. 426-428 ◽  
Author(s):  
Deborah C. Saltman ◽  
Ian W. Webster ◽  
Gallia A. Therin
Keyword(s):  
General Practitioners ◽  
Older Persons ◽  
Good Health ◽  
Health Implications

Concurrent Validity of Daily Activity Data From Medtronic ICD/CRT Devices and the Actigraph GT3X Triaxial Accelerometer

2017 ◽  
Vol 28 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Michael J. Shoemaker ◽  
Kelly Cartwright ◽  
Kim Hanson ◽  
Deb Serba ◽  
Michael G. Dickinson ◽  
...  
Keyword(s):  
Daily Activity ◽  
Concurrent Validity ◽  
Triaxial Accelerometer ◽  
Activity Data

Remote COPD Severity and Exacerbation Detection Using Heart Rate and Activity Data Measured from a Wearable Device

2021 ◽  
Author(s):  
Abhishek Tiwari ◽  
Salaar Liaqat ◽  
Daniyal Liaqat ◽  
Moshe Gabel ◽  
Eyal de Lara ◽  
...  
Keyword(s):  
Heart Rate ◽  
Wearable Device ◽  
Activity Data
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