DBP3: Physical Ac(vity & Sedentary Behavior Classifica(on using Mo(on Sensors & Smartphone App Development
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1 integra(ng Data for Analysis, Anonymiza(on, and SHaring DBP: Physical Ac(vity & Sedentary Behavior Classifica(on using Mo(on Sensors & Smartphone App Development Greg Norman, PhD
2 Research Team Greg Norman, PhD Sanjoy Dasgupta, PhD Wanmin Wu, PhD Ernesto Ramirez, MS Carlyn Peterson, MS Center for Popula.on and Wireless Health Systems at Calit2
3 Physical Ac(vity Assessment and Feedback Tool PURPOSE To create an interven(on system for physical ac(vity and sedentary behavior by using mobile smart phone technology and machine learning. Machine Learning Behavior classifica<on () Learn Pa?erns of Behavior Behavior Change Theory Feedback Behavior Change 0min of ac(vity today! Keep up the good work! Phone as Body Sensor Real- <me feedback
4 Project Goals Accurate ac(vity tracking Track ac(vity all day Feedback in real- (me Device not added burden to user Feedback informa(on appealing to user 10/2/1
5 Why Is Physical Ac(vity Important? Regular physical ac(vity can improve the health and quality of life of Americans of all ages, regardless of the presence of a chronic disease or disability.1, 2 Among adults and older adults, physical ac(vity can lower the risk of: Early death Coronary heart disease Stroke High blood pressure Type 2 diabetes Breast and colon cancer Falls Depression 1US Department of Health and Human Services (HHS), Office of Disease Preven(on and Health Promo(on Physical ac(vity guidelines for Americans. Washington: HHS; US Department of Health and Human Services (HHS), Office of Disease Preven(on and Health Promo(on. Physical ac(vity guidelines advisory commi`ee report, Washington: HHS, 2008.
6 Cross- sec(onal/longitudinal studies: High television viewing (2- + hrs/day) is detrimentally associated with health, independent of leisure- (me PA. CVD risk 1 Overweight 2, Diabetes Metabolic Syndrome,5 Abnormal glucose metabolism 6 and other biomarkers of cardio- metabolic health 7 Cancer 8,9 Mortality 10,11 Why Is Sedentary Time Important? 1 Jakes et al., E J Clin Nu 200; 2 Ching et al., AJPH 1996; Hu et al., JAMA 200 Dunstan et al., Diabetologia 2005 ; 5 Bertrais et al., Obesity Research 2005; 6 Dunstan et al., Diabetes Care 200; 7 Healy et al., MSSE 2008 ; 8 Patel et al., A J Epi 2006; 9 Howard et al., Cancer Causes and Control 2008, 10 Dunstan et al. (under review), 11 Katzmarzyk et al. (2009)
7 The Problem How Adults overall daily behavior pa`erns are typically distributed between physical ac(vity and sedentary (me moderate to vigorous activities: 0.7 hours/day light-intensity activities: 6.5 hours/ day sedentary time: 9. sedentary time: 9. hours/ hours/day day Healy, G.N., Wijndaele, K., Dunstan, D.W., Shaw, J.E., Salmon, J, Zimmet, P.Z. and Owen, N. (2008). Objec(vely- measured sedentary (me, physical ac(vity and metabolic risk: the AusDiab study. Diabetes Care, 1, 69-71
8 Feedback Loop Simple tool for changing behavior 1. Give people real (me informa(on about their ac(ons 2. Give them opportunity to change those ac(ons Ac(on informa(on reac(on Ac<on/reac<on Informa<on
9 Development Direc(ons Build own hardware Build own applica(ons Hack exis(ng hardware Use commercial product (open API) Combina(on of above InvenSense MPU Module + 10/2/1 9
10 Project Summary Phase 1a Develop ios ac(vity data collector, test pa`ern recogni(on classifica(on, share annotated dataset. Phase 1b Free- living data collec(on using Fitbit and Ac(graph. Validate Fitbit and sedentary (me classifica(on. Phase 2a Develop Android ac(vity data collector, test classifica(on accuracy, build Android app with on- board classifier and feedback (SenSed). Phase 2b Conduct user study of SenSed app.
11 Things we also wanted to do Add ac(ve learning component to SenSed. Further develop goal sepng component of SenSed. Con(nue itera(ve user tes(ng and development.
12 Phase 1a Tested poten(al and limita(ons of the iphone plaqorm and Fitbit. Built ipod Touch app to collect annotated data on ac(vi(es (walking, jogging, ascending / descending stairs, sipng and standing). Recruited 17 adult par(cipants (7 male, 10 female). Laboratory based tes(ng and outdoor tes(ng, devices worn in various loca(ons. Wore Ac(graph, Fitbit, Omron pedometer. 10/2/1 12
13 Data Collector Apps Version 1 Version 2 10/2/1 1
14 EPARC Lab Data Collec(on Ac(vi(es Ac<vity Descrip<on Speed Time Very Slow Walk Treadmill 1.5 mph min Slow Walking Treadmill.0 mph min Brisk Walking Treadmill.5 mph min Jog/Run Treadmill 6 mph min Walk/Sit/Walk Transi<on Test UCSD Track/Stadium 15 steps, sit for 2 minutes, 15 steps NA - 15 steps min Ac<vity Descrip<on Speed Time Stair Climbing Ascend Self selected - Slow 2 min Stair Descending Descend Self selected - Slow 2 min Stair Climbing Ascend Self selected Brisk 2 min Stair Descending Descend Self selected - Brisk 2 min Step Test Step up/down Self selected - Slow 2 min Step Test Step up/down Self selected - Brisk 2 min Very Slow Walk One lap around track Self selected.25 mile Slow Walking One lap around track Self selected.25 mile Brisk Walking One lap around track Self selected.25 mile Jog/Run One lap around track Self selected.25 mile
15 Ac<vi<es Accuracies (iphone touch data) knn Classifica<on Accuracy C1 = Slow walking 9.1% C2 = Normal Walking 92.0% C = Brisk Walking 90.1% C = Jogging 91.7% C5 = Sipng 100.0% C6 = Normal upstairs 69.8% C7 = Normal downstairs 79.% C8 = Brisk upstairs 70.% C9 = Brisk downstairs 52.% Weighted Average 90.2% Wu, W., Dasgupta, S., Ramirez, E., Peterson, C., Norman, G.J. (2012). Classifica(on accuracies of physical ac(vi(es using smartphone mo(on sensors. Journal of Medical Internet Research. 1(5), e10.
16 Confusion Matrix knn Classified as à C1 C2 C C C5 C6 C7 C8 C9 C1=Slow Walking C2=Normal Walking C=Brisk Walking C=Jogging C5=Si`ng C6=Normal Upstairs C7=Normal Downstairs C8=Brisk Upstairs C9=Brisk Downstairs
17 Phase 1b Fitbit & Ac(graph Free living data collec(on using Fitbit, accelerometer and ac(vity/wear log. 12 adult par(cipants (6 male, 6 female) 1 days Created annotated dataset with over 12, minute interval records of three data sources. Raw 0Hz - axis accelerometer data available Ramirez, E.R., Peterson, C., Wu, W., & Norman, G.J. (2012). Accuracy of the Fitbit Pedometer for self- paced and prescribed physical ac(vity. Medicine and Science in Sports and Exercise, (5S), S65. 10/2/1 17
18 Phase 1b - Fitbit & Ac(graph Steps/ 15 minute interval Minimum Median Maximum Mean Standard Devia<on Fitbit Ac(graph
19 Phase 1b - Median Steps/15 min by Posture Fitbit Ac(graph Laying down Reclining Sipng Standing
20 Phase 1b - Median Steps/15 min by MET Category Fitbit Ac(graph Sedentary Light Fairly Ac(ve Very Ac(ve < 2 METs 2- METs.5- METs 5-7 METs
21 Phase 2a Android Collect raw data (rota(on, accelera(on, orienta(on) using the accelerometer and gyroscope features. Build and test classifier of physical ac(vi(es and ac(vity intensi(es. Develop the SenSed applica(on with on- board classifier and to provide feedback to the user. 10/2/1 21
22 Phase 2a Ac(vi(es & Loca(ons SAMSUNG GALAXY player.0 Sipng Desk Hand, interac(ng Front pant pocket Driving/sipng in moving vehicle Seat Front pant pocket Standing Front pant pocket Back pant pocket Jogging Armband Walking Front pant pocket Back pant pocket Hand, not interac(ng Ascending Stairs (walk) Front pant pocket Back pant pocket Descending Stairs (walk) Front pant pocket Back pant pocket 10/2/1 22
23 Accuracies (Android data) Ac<vi<es J8 Classifica<on Accuracy C1 = Walking 96% C2 = Sipng 97.6% C = Jogging 100% C = Standing 98.2% C5 = Upstairs 89.8% C6 = Downstairs 99.9%
24 Confusion Matrix (Android Data) Classified as à C1 C2 C C C5 C6 C1 = Walking C2 = Sipng C = Jogging C = Standing C5 = Upstairs C6 = Downstairs
25 SenSed Classifica(on and Feedback Norman, GJ, Wu, W, Ramirez, ER, Dasgupta, S, and Peterson, C. Wireless Technology for Health Behavior Change Measurement & Interven(on. th Annual Interna(onal Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, CA, August 0, 2012.
26 Phase 2b: SenSed User Study 9 par(cipants.» 6 women, men» Average age = 0 (2-59)» Average BMI = 25.5 (19-6)» 2/ own smartphone» /9 have used pedometer or app to track ac(vity days using the device.
27 Day 1: Perceived Accuracy/ Usability Very Accurate Not at all Accurate Very Much Not at all Average.11 Si`ng 2.78 Standing.56 Walking. Easy to Use.22 Understand Without a manual
28 Day : User Ra(ngs Posi<ve Nega<ve Average 1.78 Ba?ery Life. Usability.11 Stability
29 Day : Percep(ons Very useful How useful do you think it is to receive prompts and/ or goal sepng coaching on smartphones for behavior change? Not at all Very likely How likely would you use a smartphone app to track and improve physical ac(vity? Not at all Average.67 Useful.22 Likely
30 User Feedback Posi<ve comments: I took a walking break on day to test out the walking por(on. I feel like it did a good job with that. This version is good for recording daily ac(vity, but it is not very encouraging for weight loss. All the features are very useful (MET goal, intensity breakdown, and ac(vity breakdown). I like that it shows the percentage of ac(vity intensity breakdown.
31 User Feedback - Some ques<oned the accuracy of the device I don't think it is accurate. I've been on my feet most of day. I think it is way off on the si_ng por(on. I think it was having a li`le trouble dis(nguishing between walking and standing. It could have been due to the orienta(on of the device. It seem to have recorded standing (me for actual si_ng (me
32 User Feedback - Users some<mes not sure if the device was recording. The device did not record the first 2- hours of the morning when I was primarily walking. the device seemed to shut down oben and I could not tell if it actually was recording anything. - Some found the MET value as a goal confusing. More informa(on on what a MET is and the ability to set a MET goal for the day I don't understand how the MET thing works.
33 User Feedback - some reports of ba?ery issues with the device. I wish the ba`ery would last so I could keep it on me. It would show low ba`ery very quickly aber only a few hours,. - some users wanted more op<ons for ways to wear the device. I haven't been able to wear the device jogging because of a lack of place to put it so I wasn't able to collect jogging data.
34 - Users wanted more features: User Feedback it would be nice to get a "congratula(ons" as you achieve your goals. other op(ons, for example, other ac(vi(es yoga, tennis I think the promp(ng feature would be good. Being able to adjust the MET se_ng based on personal goals would be beneficial. Would like to have an introduc(on page explaining the meter goals. I would like it to not record me sleeping as si_ng. I woke up in the morning and had 500+ hours of si_ng recorded. promp(ng for break from si_ng would be great Charts that showed trends over (me
35 Conclusions from User Study Would need to do several more itera(ons of development and user tes(ng. People like the idea of using a smartphone app as a tracker, but some did not know where to put it for some ac(vi(es. Ba`ery life of the device is a limi(ng factor for tracking apps.
36 Moves App Summary Bubbles and Storyline Automa(c recording (steps, distance, (me) Accelerometer & GPS Ac(vity recogni(on Calorie counter Map or route Daily storyline with loca(ons Price = Free
37 Conclusions Lessons Learned Developing an interven(on app is different than developing a website or workbook interven(on. Keeping up with technology developments very challenging in research environment. With a more mature technology could have been more integrated with other idash efforts of privacy and data sharing.
38 Thank you! Ques(ons? 10/2/1 Supported by the NIH Grant U5 HL10860 to the University of California, San Diego
39 Fitbit+ Interven(on System
40 Taiwan Collabora(on Sensors VectorNac VN- 100 is working. D Accelerometer + D Gyro + D Compass Expensive (approx. 500 USD ea.) We ll also get InvenSense MPU- 6050! D Accelerometer + D Gyro Matching D Compass Cheap (approx. 500 TED ea.) Chips en route. VectorNav VN- 100 Module Radios We ll get ANT chips. Nordic nrf2ap2 SoC Popular among body sensors First choice ZigBee working. We won t work on BlueTooth un(l BT is ready. Consider off- line data recording using SD disks in most applica(ons. InvenSense MPU Module
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