+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set



A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set



Sensors 17(3)



Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects' movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects' movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen's Kappa, corrected kappa, Krippendorff's alpha and Fleiss' kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 059386082

Download citation: RISBibTeXText

PMID: 28287449

DOI: 10.3390/s17030559


Related references

Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas 71(1): 13-19, 2012

Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults. Sensors 14(1): 443-457, 2014

Analyzing free-living physical activity of older adults in different environments using body-worn activity monitors. Journal of Aging and Physical Activity 18(2): 171-184, 2010

Evaluation of falls risk in community-dwelling older adults using body-worn sensors. Gerontology 58(5): 472-480, 2013

Estimation of minimum ground clearance (MGC) using body-worn inertial sensors. Journal of Biomechanics 44(6): 1083-1088, 2011

Using uncertain data from body-worn sensors to gain insight into type 1 diabetes. Journal of Biomedical Informatics 63: 259-268, 2016

Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors. Journal of Neuroengineering and Rehabilitation 12: 36, 2016

Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition 41(6): 2010-2024, 2008

Strategy quantification using body worn inertial sensors in a reactive agility task. Journal of Biomechanics 64: 219-225, 2017

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data. Sensors 19(4), 2019

Development of a standard fall data format for signals from body-worn sensors : the FARSEEING consensus. Zeitschrift für Gerontologie und Geriatrie 46(8): 720-726, 2014

Associated factors with physical activity and social activity in a sample of Brazilian older adults: data from the FIBRA Study. Revista Brasileira de Epidemiologia 22: E190022, 2019

Aging and physical activity data on which to base recommendations for exercise in older adults. Applied Physiology, Nutrition, and Metabolism 32 Suppl 2f: S75-S171, 2009

Dynamic relationship between physical activity and plantar temperatures using body worn sensor technology and thermography. Foot 18(4): 184-0, 2008

Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study. Sensors 16(12), 2018