Lifelog Agent for Human Activity Pattern Analysis on Health Avatar Platform

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Abstract

Objectives

To provide accurate personalized medical care, it is necessary to gather individual-related data or contextual information regarding the target person. Nowadays a large number of people possess smartphones, which enables sensors in the smartphones to be used for lifelogging. The objective of the study is to analyze human activity pattern by using lifelog agent cooperating with the Health Avatar platform.

Methods

Using the lifelog measured by accelerometer and gyroscope in a smartphone at a 50 Hz rate, the agent reveals how long the user walks, runs, sits, stands, and lies down, and this information is summarized by hours. The summaries are sent to the Health Avatar platform and finally are written in the Continuity of Care Record (CCR) format.

Results

The lifelog agent is successfully operated with the Health Avatar platform. In addition, we implement an application that displays the user's activity patterns in a graph and calculates the metabolic equivalent of task based calorie burned by hour or by day using the lifelog of the CCR form to show that the lifelog can be used as medical records.

Conclusions

The agent shows how lifelogs are analyzed and summarized to help activity recognition. We believe that our agent demonstrates a way of incorporating lifelogs into medical care and a way of exploiting lifelogs in a medical format.

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Most cited references 18

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Assessment of physical activity - a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation.

Janet M. Warren, Ulf Ekelund, Herve Besson … (2010)

Physical activity has a fundamental role in the prevention and treatment of chronic disease. The precise measurement of physical activity is key to many surveillance and epidemiological studies investigating trends and associations with disease. Public health initiatives aimed at increasing physical activity rely on the measurement of physical activity to monitor their effectiveness. Physical activity is multidimensional, and a complex behaviour to measure; its various domains are often misunderstood. Inappropriate or crude measures of physical activity have serious implications, and are likely to lead to misleading results and underestimate effect size. In this review, key definitions and theoretical aspects, which underpin the measurement of physical activity, are briefly discussed. Methodologies particularly suited for use in epidemiological research are reviewed, with particular reference to their validity, primary outcome measure and considerations when using each in the field. It is acknowledged that the choice of method may be a compromise between accuracy level and feasibility, but the ultimate choice of tool must suit the stated aim of the research. A framework is presented to guide researchers on the selection of the most suitable tool for use in a specific study.

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Recognition of dietary activity events using on-body sensors.

Oliver Amft, Gerhard Tröster (2008)

An imbalanced diet elevates health risks for many chronic diseases including obesity. Dietary monitoring could contribute vital information to lifestyle coaching and diet management, however, current monitoring solutions are not feasible for a long-term implementation. Towards automatic dietary monitoring, this work targets the continuous recognition of dietary activities using on-body sensors. An on-body sensing approach was chosen, based on three core activities during intake: arm movements, chewing and swallowing. In three independent evaluation studies the continuous recognition of activity events was investigated and the precision-recall performance analysed. An event recognition procedure was deployed, that addresses multiple challenges of continuous activity recognition, including the dynamic adaptability for variable-length activities and flexible deployment by supporting one to many independent classes. The approach uses a sensitive activity event search followed by a selective refinement of the detection using different information fusion schemes. The method is simple and modular in design and implementation. The recognition procedure was successfully adapted to the investigated dietary activities. Four intake gesture categories from arm movements and two food groups from chewing cycle sounds were detected and identified with a recall of 80-90% and a precision of 50- 64%. The detection of individual swallows resulted in 68% recall and 20% precision. Sample-accurate recognition rates were 79% for movements, 86% for chewing and 70% for swallowing. Body movements and chewing sounds can be accurately identified using on-body sensors, demonstrating the feasibility of on-body dietary monitoring. Further investigations are needed to improve the swallowing spotting performance.

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Monitoring of posture allocations and activities by a shoe-based wearable sensor.

Yves Schutz, Alexey Sazonov, George D Fulk … (2011)

Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) without significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.

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Author and article information

Journal

Journal ID (nlm-ta): Healthc Inform Res

Journal ID (iso-abbrev): Healthc Inform Res

Journal ID (publisher-id): HIR

Title: Healthcare Informatics Research

Publisher: Korean Society of Medical Informatics

ISSN (Print): 2093-3681

ISSN (Electronic): 2093-369X

Publication date (Print): January 2014

Publication date (Electronic): 31 January 2014

Volume: 20

Issue: 1

Pages: 69-75

Affiliations

[1 ]Human Computing Research Section, SW · Content Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, Korea.

[2 ]Seoul National University Biomedical Informatics, Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea.

Author notes

Corresponding Author: Changseok Bae, PhD. Human Computing Research Section, SW · Content Research Laboratory, Electronics and Telecommunications Research Institute, 218, Gajeong-ro, Yuseong-gu, Daejeon 305-700, Korea. Tel: +82-42-860-3816, Fax: +82-42-860-6645, csbae@ 123456etri.re.kr

Article

DOI: 10.4258/hir.2014.20.1.69

PMC ID: 3950268

SO-VID: 0d632ea5-fcb6-40a5-9712-718c6148e554

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 15 November 2013

Date revision received : 22 January 2014

Date accepted : 23 January 2014

Funding

Funded by: National Research Foundation of Korea (NRF)

Award ID: 2010-0028631

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Lifelog Agent for Human Activity Pattern Analysis on Health Avatar Platform

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Abstract

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Most cited references 18

Assessment of physical activity - a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation.

Recognition of dietary activity events using on-body sensors.

Monitoring of posture allocations and activities by a shoe-based wearable sensor.

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