Literature DB >> 26449155

Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age.

Scott J Strath1, Rohit J Kate, Kevin G Keenan, Whitney A Welch, Ann M Swartz.   

Abstract

To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18-39, 40-64, 65 +  years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of -0.03 to 0.01 METs, bias percent of -0.8 to 0.3%, and a rMSE range of 0.81-1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed.

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Year:  2015        PMID: 26449155      PMCID: PMC4790099          DOI: 10.1088/0967-3334/36/11/2335

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  29 in total

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Authors:  J E McLaughlin; G A King; E T Howley; D R Bassett; B E Ainsworth
Journal:  Int J Sports Med       Date:  2001-05       Impact factor: 3.118

2.  Estimation of energy expenditure using CSA accelerometers at hip and wrist sites.

Authors:  A M Swartz; S J Strath; D R Bassett; W L O'Brien; G A King; B E Ainsworth
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

3.  Validity of accelerometry for the assessment of moderate intensity physical activity in the field.

Authors:  D Hendelman; K Miller; C Baggett; E Debold; P Freedson
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

4.  Comparison of MTI accelerometer cut-points for predicting time spent in physical activity.

Authors:  S J Strath; D R Bassett; A M Swartz
Journal:  Int J Sports Med       Date:  2003-05       Impact factor: 3.118

5.  Measurement of human daily physical activity.

Authors:  Kuan Zhang; Patricia Werner; Ming Sun; F Xavier Pi-Sunyer; Carol N Boozer
Journal:  Obes Res       Date:  2003-01

6.  A timely meeting: objective measurement of physical activity.

Authors:  Richard P Troiano
Journal:  Med Sci Sports Exerc       Date:  2005-11       Impact factor: 5.411

Review 7.  Calibration of accelerometer output for adults.

Authors:  Charles E Matthew
Journal:  Med Sci Sports Exerc       Date:  2005-11       Impact factor: 5.411

8.  Calibration of the Computer Science and Applications, Inc. accelerometer.

Authors:  P S Freedson; E Melanson; J Sirard
Journal:  Med Sci Sports Exerc       Date:  1998-05       Impact factor: 5.411

9.  Predicting activity energy expenditure using the Actical activity monitor.

Authors:  Daniel P Heil
Journal:  Res Q Exerc Sport       Date:  2006-03       Impact factor: 2.500

10.  Control of simple arm movements in elderly humans.

Authors:  W G Darling; J D Cooke; S H Brown
Journal:  Neurobiol Aging       Date:  1989 Mar-Apr       Impact factor: 4.673

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  5 in total

1.  Ordinal Statistical Models of Physical Activity Levels from Accelerometer Data.

Authors:  Shafayet S Hossain; Drew M Lazar; Munni Begum
Journal:  Int J Exerc Sci       Date:  2021-04-01

Review 2.  Assessment of Physical Activity in Adults Using Wrist Accelerometers.

Authors:  Fangyu Liu; Amal A Wanigatunga; Jennifer A Schrack
Journal:  Epidemiol Rev       Date:  2022-01-14       Impact factor: 4.280

3.  Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models.

Authors:  Matthew N Ahmadi; Margaret E O'Neil; Emmah Baque; Roslyn N Boyd; Stewart G Trost
Journal:  Sensors (Basel)       Date:  2020-07-17       Impact factor: 3.576

4.  Feasibility of the Energy Expenditure Prediction for Athletes and Non-Athletes from Ankle-Mounted Accelerometer and Heart Rate Monitor.

Authors:  Chin-Shan Ho; Chun-Hao Chang; Yi-Ju Hsu; Yu-Tsai Tu; Fang Li; Wei-Lun Jhang; Chih-Wen Hsu; Chi-Chang Huang
Journal:  Sci Rep       Date:  2020-06-01       Impact factor: 4.379

Review 5.  A Survey of Human Gait-Based Artificial Intelligence Applications.

Authors:  Elsa J Harris; I-Hung Khoo; Emel Demircan
Journal:  Front Robot AI       Date:  2022-01-03
  5 in total

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