Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity

International Journal of Behavioral Nutrition and Physical Activity

Charlie Foster⁴  Alexander Hamilton⁴  Hannah Badland⁵  Melody Oliver¹  Simon Marshall³  Jacqueline Kerr³  Paul Kelly⁴  Aiden R Doherty² 

[1] Centre for Physical Activity and Nutrition, Auckland University of Technology, Auckland, New Zealand;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland;SDU, University of California San Diego, La Jolla, USA;British Heart Foundation Health Promotion Research Group, Department of Public Health, University of Oxford, Oxford, UK;McCaughey VicHealth Centre for the Promotion of Mental Health and Community Wellbeing, the University of Melbourne, Parkville, Australia

关键词: Sedentary behaviour;    Physical activity;    Measurement;    Context;    Accelerometer episode;    SenseCam;   

Others  :  810821
DOI  :  10.1186/1479-5868-10-22

 received in 2012-06-21, accepted in 2013-01-18,  发布年份 2013

【 摘 要 】

Background

Accelerometers can identify certain physical activity behaviours, but not the context in which they take place. This study investigates the feasibility of wearable cameras to objectively categorise the behaviour type and context of participants’ accelerometer-identified episodes of activity.

Methods

Adults were given an Actical hip-mounted accelerometer and a SenseCam wearable camera (worn via lanyard). The onboard clocks on both devices were time-synchronised. Participants engaged in free-living activities for 3 days. Actical data were cleaned and episodes of sedentary, lifestyle-light, lifestyle-moderate, and moderate-to-vigorous physical activity (MVPA) were identified. Actical episodes were categorised according to their social and environmental context and Physical Activity (PA) compendium category as identified from time-matched SenseCam images.

Results

There were 212 days considered from 49 participants from whom SenseCam images and associated Actical data were captured. Using SenseCam images, behaviour type and context attributes were annotated for 386 (out of 3017) randomly selected episodes (such as walking/transportation, social/not-social, domestic/leisure). Across the episodes, 12 categories that aligned with the PA Compendium were identified, and 114 subcategory types were identified. Nineteen percent of episodes could not have their behaviour type and context categorized; 59% were outdoors versus 39% indoors; 33% of episodes were recorded as leisure time activities, with 33% transport, 18% domestic, and 15% occupational. 33% of the randomly selected episodes contained direct social interaction and 22% were in social situations where the participant wasn’t involved in direct engagement.

Conclusion

Wearable camera images offer an objective method to capture a spectrum of activity behaviour types and context across 81% of accelerometer-identified episodes of activity. Wearable cameras represent the best objective method currently available to categorise the social and environmental context of accelerometer-defined episodes of activity in free-living conditions.

【 授权许可】

   
2013 Doherty et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]WHO: Global Recommendations on Physical Activity for Health.. World Health Organisation; 2010.
• [2]Ireland N, Chief Medical Officers of England W Scotland: Start Active, Stay Active: A Report on Physical Activity for Health from the Four Home Countries. UK: Department of Health; 2011.
• [3]US Department of Health and Human Services: 2008 Physical Activity Guidelines for Americans: Be Active, Healthy, and Happy!. US: Department of Health and Human Services; 2008.
• [4]Sallis JF, Owen N, Fotheringham MJ: Behavioral epidemiology: a systematic framework to classify phases of research on health promotion and disease prevention. Ann Behav Med 2000, 22:294-298.
• [5]Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW: Correlates of physical activity: why are some people physically active and others not? Lancet 2012, 380(9838):258-271. [http://www.sciencedirect.com/science/article/pii/S0140673612607351 webcite]
• [6]Koorts H, Mattocks C, Ness AR, Deere K, Blair SN, Pate RR, Riddoch C: The association between the type, context, and levels of physical activity amongst adolescents. J Phys Act Health 2011, 8:1057-1065.
• [7]Burton NW, Khan A, Brown WJ: How, where and with whom? Physical activity context preferences of three adult groups at risk of inactivity. Br J Sports Med 2012, 46:1125-1131.
• [8]Kelly P, Doherty AR, Berry E, Hodges S, Batterham AM, charlie Foster: Can we use digital life-log images to investigate active and sedentary travel behaviour? Results from a pilot study. Int J Behav Nutr Phys Act 2011, 8:44. BioMed Central Full Text
• [9]Armstrong N, Welsman JR: The physical activity patterns of European youth with reference to methods of assessment. Sports Med 2006, 36:1067-1086.
• [10]Pate RR, O’Neill JR, Mitchell J: Measurement of physical activity in preschool children. Med Sci Sports Exerc 2010, 42:508-512.
• [11]Craig R, Mindell J, Hirani V: Health Survey for England - 2008: Physical Activity and Fitness. National Centre for Social Research; 2008.
• [12]Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M: Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008, 40:181-188.
• [13]Freedson PS, Lyden K, Kozey-Keadle S, Staudenmayer J: Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample. J Appl Physiol 2011, 111(6):1804-1812.
• [14]Cooper AR, Page AS, Wheeler BW, Griew P, Davis L, Hillsdon M, Jago R: Mapping the walk to school using accelerometry combined with a global positioning system. Am J Prev Med 2010, 38:178-183.
• [15]Oliver M, Badland H, Mavoa S, Duncan MJ, Duncan S: Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors. J Phys Act Health 2010, 7:102-108.
• [16]Maddison R, Jiang Y, Vander Hoorn S, Exeter D, Mhurchu CN, Dorey E: Describing patterns of physical activity in adolescents using global positioning systems and accelerometry. Pediatr Exerc Sci 2010, 22:392-407.
• [17]Krenn PJ, Titze S, Oja P, Jones A, Ogilvie D: Use of global positioning systems to study physical activity and the environment: a systematic review. Am J Prev Med 2011, 41(5):508-515.
• [18]Dunton GF, Liao Y, Intille SS, Spruijt-Metz D, Pentz M: Investigating children’s physical activity and sedentary behavior using ecological momentary assessment with mobile phones. Obesity (Silver Spring) 2011, 19(6):1205-1212.
• [19]Hodges S, Williams L, Berry E, Izadi S, Srinivasan J, Butler A, Smyth G, Kapur N, Wood K: SenseCam: A Retrospective Memory Aid. In UbiComp: 8th International Conference on Ubiquitous Computing, Volume 4602 of LNCS. Berlin, Heidelberg: Springer; 2006:177-193.
• [20]Hodges S, Berry E, Wood K: SenseCam: A wearable camera that stimulates and rehabilitates autobiographical memory. Memory 2011, 7(19):685-696.
• [21]Heil DP: Predicting activity energy expenditure using the Actical activity monitor. Res Q Exerc Sport 2011, 77:64-80.
• [22]Barclay T, Gray J, Slutz D: Microsoft TerraServer: a spatial data warehouse. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, SIGMOD ’00. New York: ACM; 2000:307-318. [http://doi.acm.org/10.1145/342009.335424 webcite]
• [23]Trost SG, McIver KL, Pate RR: Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc 2005, 37:S531-543.
• [24]Wong SL, Colley R, Connor Gorber S, Tremblay M: Actical accelerometer sedentary activity thresholds for adults. J Phys Act Health 2011, 8:587-591.
• [25]Masse LC, Fuemmeler BF, Anderson CB, Matthews CE, Trost SG, Catellier DJ, Treuth M: Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc 2005, 37:S544-S554.
• [26]Heil DP: Identification of accelerometer non-wear time and sedentary behavior. Res Q Exerc Sport 2010, 82:593-597.
• [27]Matthews CE: Calibration of accelerometer output for adults. Med Sci Sports Exerc 2005, 37:S512-522.
• [28]Colley RC, Tremblay MS: Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer. J Sports Sci 2011, 29:783-789.
• [29]Doherty AR, Moulin CJ, Smeaton AF: Automatically assisting human memory: A SenseCam browser. Memory 2011, 7(19):785-795.
• [30]Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Jr DRB, Tudor-Locke C, Greer J, Vezina J, Whitt-glover MC, Leon AS: 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc 2011, 43(8):1575-1581.
• [31]Taylor HL, Jacobs DR, Schucker B, Knudsen J, Leon AS, Debacker G: A questionnaire for the assessment of leisure time physical activities. J Chronic Dis 1978, 31:741-755.
• [32]Prosser J, Loxley A: ESRC National Centre for Research Methods Review Paper. Tech. rep., UK: National Centre for Research Methods; 2008
• [33]Smeaton AF, Over P, Kraaij W: Evaluation campaigns and TRECVid. In Proceedings of the 8th ACM international workshop on Multimedia information retrieval, MIR ’06. New York: ACM; 2006:321-330. [http://doi.acm.org/10.1145/1178677.1178722 webcite]
• [34]Doherty AR, Caprani N, Conaire CO, Kalnikaite V, Gurrin C, Smeaton AF, Connor NEO: Passively recognising human activities through lifelogging. Comput Human Behav 2011, 27(5):1948-1958.
• [35]Landis JR, Koch GC: The measurement of observer agreement for categorical data. Biometrics 1977, 33:159-174.
• [36]Smeulders AW, Worring M, Santini S, Gupta A, Jain R: Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 2000, 22:1349-1380.
• [37]Freedson P, Bowles HR, Troiano R, Haskell W: Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. Med Sci Sports Exerc 2012, 44(1 Suppl 1):1-4.
• [38]Harris TJ, Owen CG, Victor CR, Adams R, Ekelund U, Cook DG: A comparison of questionnaire, accelerometer, and pedometer: measures in older people. Med Sci Sports Exerc 2009, 41:1392-1402.