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Objective assessment of physical activity and sedentary time of older adults using ambient and wearable sensor technologies

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Abstract

This paper reports the description of a multi-sensor platform able to automatically assess the level of physical activity and sedentary time of older adults. The platform has a hierarchical network topology, compound by N detector nodes managing several ambient sensor nodes and one detector node that manages a wearable sensor node. The system provides also one coordinator node that receives high-level reports from detector nodes. The idea of using heterogeneous sensors is motivated by the fact that in this way we expands the number of end-users, as they may accept only a type of sensor technology. The objective assessment was conducted through two main algorithmic steps: (1) recognition of well-defined set of human activities, detected by a 3D vision sensor (ambient node) and a smart garment (wearable sensor node), and (2) estimation of a physiological measure, that is (MET)-minutes. Results obtained in terms of activity recognition (and subsequent physical activity/sedentary time assessment) showed that the integrated version of the platform performs better than each single sensor technology with an overall accuracy obtained using simultaneously data provided from both sensory technologies that is about 5% higher of single sub-system, thus confirming the advantage in using a coordinator node. Finally, an added value of this work is the capability of the platform in providing a sensing invariant interface (i.e., abstracted from any specific sensing technology), since the use of the activities enables the integration of a wide set of devices, providing that they are able to reproduce the same set of features.

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References

  • Augusto JC, Nakashima H, Aghajan H (2010) Ambient intelligence and smart environments: a state of the art. In: Handbook of ambient intelligence and smart environments. Springer, US, pp 3–31

    Chapter  Google Scholar 

  • Baloch S, Krim H, Kogan I, Zenkov D (2005) Rotation invariant topology coding of 2D and 3D objects using Morse theory. Proc ICIP 3:796–799

    Google Scholar 

  • Barer D, Nouri F (1989) Measurement of activities of daily living. Clin Rehabil 3(3):179–187

    Article  Google Scholar 

  • Barnes J, Behrens TK, Benden ME, Biddle S, Bond D, Brassard P, Colley R (2012) Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab 37(3):540–542

    Article  Google Scholar 

  • Bassett DR Jr (2000) Validity and reliability issues in objective monitoring of physical activity. Res Q Exerc Sport 71(sup2):30–36

    Article  Google Scholar 

  • Bassett DR Jr (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35(8):1396

    Article  Google Scholar 

  • Bassett DR Jr, Wyatt HR, Thompson H, Peters JC, Hill JO (2010) Pedometer-measured physical activity and health behaviors in US adults. Med Sci Sports Exerc 42(10):1819

    Article  Google Scholar 

  • Buccolieri F, Distante C, Leone A (2005) Human posture recognition using active contours and radial basis function neural network. Proc AVSS:213–218

  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  • Chastin SF, Buck C, Freiberger E, Murphy M, Brug J, Cardon G, Oppert JM (2015) Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study. Int J Behav Nutr Phys Act 12(1):127

    Article  Google Scholar 

  • Claridge EA, McPhee PG, Timmons BW, Martin GK, MacDonald MJ, Gorter JW (2015) Quantification of physical activity and sedentary time in adults with cerebral palsy. Med Sci Sports Exerc 47(8):1719–1726

    Article  Google Scholar 

  • Dahlgren G, Carlsson D, Moorhead A, Häger-Ross C, McDonough SM (2010) Test–retest reliability of step counts with the ActivPAL™ device in common daily activities. Gait Posture 32(3):386–390

    Article  Google Scholar 

  • Debnath R, Takahide N, Takahashi H (2004) A decision based one-against-one method for multi-class support vector machine. Pattern Anal Applic 7(2):164–175

    Article  MathSciNet  Google Scholar 

  • Diraco G, Leone A, Siciliano P (2011) Geodesic-based human posture analysis by using a single 3D TOF camera. Proc ISIE:1329–1334

  • Dogra S, Stathokostas L (2012) Sedentary behavior and physical activity are independent predictors of successful aging in middle-aged and older adults. J Aging Res

  • Dwyer TJ, Alison JA, McKeough ZJ, Elkins MR, Bye PTP (2009) Evaluation of the SenseWear activity monitor during exercise in cystic fibrosis and in health. Respir Med 103(10):1511–1517

    Article  Google Scholar 

  • Gillick MR, Serrell NA, Gillick LS (1982) Adverse consequences of hospitalization in the elderly. Soc Sci Med 16(10):1033–1038

    Article  Google Scholar 

  • Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, Lancet Physical Activity Series Working Group (2012) Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 380(9838), 247–257

    Article  Google Scholar 

  • Harrington DM, Welk GJ, Donnelly AE (2011) Validation of MET estimates and step measurement using the ActivPAL physical activity logger. J Sports Sci 29(6):627–633

    Article  Google Scholar 

  • Hart TL, Ainsworth BE, Tudor-Locke C (2011) Objective and subjective measures of sedentary behavior and physical activity. Med Sci Sports Exerc 43(3):449–456

    Article  Google Scholar 

  • He Y, Li Y (2013) Physical activity recognition utilizing the built-in kinematic sensors of a smartphone. Int J Distrib Sens Netw 9(4):481580

    Article  Google Scholar 

  • He W, Goodkind D, Kowal P (2016) An aging world: 2015. US Census Bureau, pp 1–165

  • Katzmarzyk PT (2010) Physical activity, sedentary behavior, and health: paradigm paralysis or paradigm shift? Diabetes 59(11):2717–2725

    Article  Google Scholar 

  • Kellokumpu V, Pietikäinen M, Heikkilä J (2005) Human activity recognition using sequences of postures. MVA (pp 570–573)

  • Lee M, Kim J, Jee SH, Yoo SK (2010). Review of daily physical activity monitoring system based on single triaxial accelerometer and portable data measurement unit. In: Machine learning and systems engineering. Springer, The Netherlands, pp 569–580

    Chapter  Google Scholar 

  • MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th Berkeley symposium on mathematical statistics and probability, Berkeley, University of California Press, 1:281–297

  • Martin Ginis KA, Latimer AE (2008) Physical activity recall assessment for people with spinal cord injury (PARA-SCI): Administration and Scoring Manual. McMaster University, Hamilton

  • Paffenbarger R, Wing A, Hyde R (1978) Paffenbarger physical activity questionnaire. Am J Epidemiol 108:161–175

    Article  Google Scholar 

  • Pitta F, Troosters T, Probst VS, Spruit MA, Decramer M, Gosselink R (2006) Quantifying physical activity in daily life with questionnaires and motion sensors in COPD. Eur Respir J 27(5):1040–1055

    Article  Google Scholar 

  • Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  • Reeb G (1946) Sur les points singuliers d’une forme de Pfaff completement integrable on d’une fonction numerique. Comptes Rendus Acad Sci 222:847–849

    Google Scholar 

  • Rescio G, Leone A, Siciliano P (2013) Supervised expert system for wearable MEMS accelerometer-based fall detector. J Sens

  • Thorp AA, Owen N, Neuhaus M, Dunstan DW (2011) Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996–2011. Am J Prev Med 41(2):207–215

    Article  Google Scholar 

  • Unick JL, Lang W, Tate DF, Bond DS, Espeland MA, Wing RR (2017) Objective estimates of physical activity and sedentary time among young adults. J Obes

  • Verroust A, Lazarus F (2000) Extracting skeletal curves from 3D scattered data. Vis Comput 16(1):15–25

    Article  Google Scholar 

  • Virone G, Alwan M, Dalal S, Kell SW, Turner B, Stankovic JA, Felder R (2008) Behavioral patterns of older adults in assisted living. IEEE Trans Inf Technol Biomed 12(3):387–398

    Article  Google Scholar 

  • World Medical Association (2001) World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bull World Health Organ 79(4):373

    Google Scholar 

  • Xiao Y, Siebert P, Werghi N (2004) Topological segmentation of discrete human body shapes in various postures based on geodesic distance. Proc ICPR 3:131–135

    Google Scholar 

Download references

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Authors and Affiliations

  1. National Research Council of Italy, Institute for Microelectronics and Microsystems, Via Monteroni c/o Campus Universitario Ecotekne-Palazzina A3, 73100, Lecce, Italy

    Andrea Caroppo, Alessandro Leone & Pietro Siciliano

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  1. Andrea Caroppo
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  2. Alessandro Leone
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  3. Pietro Siciliano
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Correspondence to Andrea Caroppo.

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Caroppo, A., Leone, A. & Siciliano, P. Objective assessment of physical activity and sedentary time of older adults using ambient and wearable sensor technologies. J Ambient Intell Human Comput 15, 1961–1973 (2024). https://doi.org/10.1007/s12652-017-0610-5

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  • DOI: https://doi.org/10.1007/s12652-017-0610-5

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