Identification of children s activity type using accelerometer, GPS and HR data in neural network models

Transcription

1 Identification of children s activity type using accelerometer, GPS and HR data in neural network models Sanne de Vries (1) and Francisca Galindo-Garre (2) (1) TNO, Department of Life Style, Leiden, the Netherlands; (2) VU University Medical centre, Department of Epidemiology and Biostatistics, Amsterdam, the Netherlands

2 Background Physical activity is beneficial for children s health Many questions about the exact dose-response relationship between PA and health Accurate methods to assess children s PA are vitally important

3 Assessing physical activity ICDAM 2012

4 Accelerometers Accelerometers provide objective information on: - Frequency of PA - Intensity of PA - Duration of PA Accelerometers can provide raw data at 30 Hz up to 40 days Raw data is summarized into: - Counts per minute - Time spent in sedentary, light intensity, moderate intensity and vigorous intensity activities This does not value the richness of accelerometer data

5 07:00:00 07:48:00 08:36:00 09:24:00 10:12:00 11:00:00 11:48:00 12:36:00 13:24:00 14:12:00 15:00:00 15:48:00 16:36:00 17:24:00 18:12:00 19:00:00 19:48:00 counts/ min 07:00:00 07:47:45 08:35:30 09:23:15 10:11:00 10:58:45 11:46:30 12:34:15 13:22:00 14:09:45 14:57:30 15:45:15 16:33:00 17:20:45 18:08:30 18:56:15 19:44:00 counts/ min ICDAM 2012 Accelerometers Accelerometers do not provide information on: type of PA tijd Difference between non-wearing time, sedentary time and sleeping? Difference between cycling and walking? tijd

6 Sophisticated statistical techniques Pattern-recognition based or machine-learning approaches for analysing accelerometer data: - decision trees (Bonomi et al., 2009) - artificial neural network models (Staudenmayer et al., 2009; De Vries et al., 2011) - Hidden Markov models (Pober et al., 2006) Successful in classifying a number of controlled activities in children, adults and elderly (63%-99% correctly classified) Based on a limited number of laboratory activities Based on single hip-worn accelerometer data

7 ANN model Input variables: 10th, 25th, 75th, and 90th percentiles, absolute deviation, coefficient of variability, lag-one autocorrelation computed over 10 s intervals Output variables: x activity types Models evaluated by leave-one-subject-out cross-validation

8 Previous study in adults De Vries et al., 2011 adults N = 49; Actigraph GT1M on hip and ankle, 1 s epochs; Activities: sitting, standing, using the stairs, walking, cycling Results: - Hip ANN model performed better than ankle model (80.4% vs. 77.7%) - Combined model performed best (83.0%) - Using the stairs was often misclassified as standing still - Brisk walking and cycling were often misclassified as regular walking and cycling

9 Previous study in children De Vries et al., 2011 children N = 58; Actigraph GT1M and GT3X on hip and ankle, 1 s epochs; Activities: sitting, standing, walking, running, rope skipping, playing soccer, and cycling Results: - Hip models performed better than ankle models (1D: 72% vs. 57%; 3D: 77% vs. 68%) - 3D models performed better than 1D models - Standing was often misclassified as sitting

10 Present study in children Purpose: to examine whether the accuracy of previously developed model in children improved by including more information about intensity of activities. Additional data: Global Positioning Systems (GPS) speed data and heart rate data

11 Methods Sample: 58 healthy children (31 boys, 27 girls; age range: 9-12 years old) Measurement instruments 3-axial ActiGraph accelerometer (ActiGraph, Pensicola, FL) on the hip (counts/s) GPS device (GPS Qstarz BT-Q1000x) (meter/s) Heart rate measurement device (Polar T61, S610i) (5 seconds epoch) Physical activities performed (about 20 min): sitting, standing, walking, running, rope skipping, playing soccer, and cycling

12 ANN model Four models: Model based on accelerometer data (M1): 10th, 25th, 75th, and 90th percentiles, absolute deviation, coefficient of variability, lag-one autocorrelation for each axis Model based on accelerometer and GPS data (M2): Inputs model 1 + mean GPS and absolute deviation GPS Model based on accelerometer and HR data (M3): Inputs model 1 + mean heart rate Combined model (M4)

13 Model based on accelerometer data Percentage correctly classified activities per physical activity and total: M1 (%) Standing 87 Walking 92 Running 89 Regular cycling 67 Brisk cycling 33 Rope skipping 91 Playing soccer 87 Total 82

14 Model based on accelerometer and GPS data Percentage correctly classified activities per physical activity and total. M1 (%) M2 (%) Standing Walking Running Regular cycling Brisk cycling Rope skipping Playing soccer Total 82 89

15 Model based on accelerometer and heart rate data Percentage correctly classified activities per physical activity and total. M1 (%) M2 (%) M3 (%) Standing Walking Running Regular cycling Brisk cycling Rope skipping Playing soccer Total

16 Combined model Percentage correctly classified activities per physical activity and total. M1 (%) M2 (%) M3 (%) M4 (%) Standing Walking Running Regular cycling Brisk cycling Rope skipping Playing soccer Total

17 Conclusion and discussion All 4 ANN models performed well (> 80% correctly classified) when classifying standing, walking, running, rope skipping and playing soccer in children Including GPS and/or HR data improved accelerometer-based ANN model by 2-8% Adding GPS data seemed especially valuable for distinguishing between regular and brisk cycling Discussion: Does the added value of including HR data and GPS data into accelerometer-based ANN models outweigh the additional cost and burden for the researcher and the subject? Future studies: using other signal features (peak HR), using shorter time segments (< 10 s), using raw accelerometer data (100 Hz), using other sensors (inclinometer)

18 THANK YOU VERY MUCH FOR YOUR ATTENTION!! Vries SI de, Galindo Garre F, Engbers LH, Hildebrandt VH, Buuren S van. (2011a). Evaluation of Neural Networks to Identify Types of Activity Using Accelerometers. Medicine & Science in Sports & Exercise, Vol.43, No.1, pp Vries SI de, Engels, M, Galindo Garre F. (2011b). Identification of children s activity type with accelerometer-based neural networks. Medicine & Science in Sports & Exercise, Vol.43, No.10, pp Galindo Garre F, Vries S I de (2012). Evaluation of neural networks to identify types of activity among children using accelerometers, global positioning systems and heart rate monitors. Chapter 13. In: ZASLAV KR (Ed.). An International Perspective on Topics in Sports Medicine and Sports Injury. ISBN: , InTech, 2012: