A Smartphone Based Real Time Ac5vity Monitoring System

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1 A Smartphone Based Real Time Ac5vity Monitoring System By: Shumei Zhang, Paul McCullagh, Jing Zhang, Tiezhong Yu Presented by: Jane Henderson A Smartphone Based-Real Time Daily Ac5vity Monitoring System 1

2 Outline Problem and goals of the system Background Methodology Experiments Results Takeaways Discussion A Smartphone Based-Real Time Daily Ac5vity Monitoring System 2

3 FALLS Leading cause of injury Major global health problem - par5cularly for elderly 3% who fall will not receive assistance for 20 minutes A Smartphone Based-Real Time Daily Ac5vity Monitoring System 3

4 How can we solve this? Automa5c monitoring of daily ac5vi5es Context aware applica5ons Pervasive compu5ng A Smartphone Based-Real Time Daily Ac5vity Monitoring System 4

5 Proposed Solu5on SMART PHONE BASED ACTIVITY MONITORING SYSTEM To classify mo5on and mo5onless daily ac5vi5es and dis5nguish falls in various situa5ons A Smartphone Based-Real Time Daily Ac5vity Monitoring System 5

6 Background How to classify human ac5vi5es of daily living? Wearable Sensors Feature Extrac5on Classifica5on of these features A Smartphone Based-Real Time Daily Ac5vity Monitoring System 6

7 How to sense falls? Larger accelera5on change compared to normal daily ac5vi5es Methods using only accelerometers? Combine accelerometers with other sensors? A Smartphone Based-Real Time Daily Ac5vity Monitoring System 7

8 Proposed System Smartphone based fall detec5on system Hierarchal rule-based algorithm Rule-based backward reasoning algorithm A Smartphone Based-Real Time Daily Ac5vity Monitoring System 8

9 Methodology HTC Wildfire S A510e phone A Smartphone Based-Real Time Daily Ac5vity Monitoring System 9

10 Methodology: Data Collec5on 2 Raw Data Sets Sampling frequency 5 Hz 80 Hz Can miss high-frequency values for mo5on ac5vi5es A Smartphone Based-Real Time Daily Ac5vity Monitoring System 10

11 Data Sensing Accelera5on Accelerometer 3D Accelera5on 3D Orienta5on A Smartphone Based-Real Time Daily Ac5vity Monitoring System 11

12 Methodology: Posture Classifica5on High level context based on: (t, id, A x, A y, A z, ΔA, θ X, θ y, θ Z ) t is the 5me stamp id is the calculated sample number ΔA is the calculated accelera5on change 2 types of ac5vi5es: mo5onless and mo5on A Smartphone Based-Real Time Daily Ac5vity Monitoring System 12

13 Methodology: Mo5onless Postures th1 = 0.4m/s 2 (determined empirically using collected mo5onless data) A Smartphone Based-Real Time Daily Ac5vity Monitoring System 13

14 Methodology: Mo5onless Postures A Smartphone Based-Real Time Daily Ac5vity Monitoring System 14

15 Methodology: Mo5on Postures th2 = 3.5m/s 2 (determined empirically using collected mo5on data) A Smartphone Based-Real Time Daily Ac5vity Monitoring System 15

16 Experiments Indoor (real home environment) Real-5me Six healthy people (5 male, 1 female, years) Simulated: Various falls Normal daily ac5vi5es A Smartphone Based-Real Time Daily Ac5vity Monitoring System 16

17 Experiments Results validated against notes by two independent observers Two algorithms used: PosTra (algorithm described in this paper) + posi5on AccThr A Smartphone Based-Real Time Daily Ac5vity Monitoring System 17

18 Data Sensing and the System Interface Analyzed Results (t, posture, loca5on, status) If certain fall: fall alert Else if possible fall: music alert will sound and a stop bulon will appear A Smartphone Based-Real Time Daily Ac5vity Monitoring System 18

19 Falls and Fall-Like Ac5vi5es Fall-lying (72) Fall-sitTilted (72) Normal lying (72) Bending (36) Jump and sit heavily A Smartphone Based-Real Time Daily Ac5vity Monitoring System 19

20 Results Normal and abnormal daily ac5vi5es classified using PosTra and AccThr 4 aspects: (1) True posi5ve (2) False nega5ve (3) True nega5ve (4) False posi5ve A Smartphone Based-Real Time Daily Ac5vity Monitoring System 20

21 Results: PosTra vs. AccThr A Smartphone Based-Real Time Daily Ac5vity Monitoring System 21

22 Results: Possible Fall Recogni5on PosTra will trigger possible fall when: Simng period of 5me < 2s before normal lying Bending > 70 Posture keeping sit-5lt on a chair aqer jumping A Smartphone Based-Real Time Daily Ac5vity Monitoring System 22

23 Results: Normal Lying Limita5ons A Smartphone Based-Real Time Daily Ac5vity Monitoring System 23

24 Takeaways The mo5on and mo5onless postures were classified using a hierarchal rule-based algorithm Trustworthy for daily ac5vity monitoring Fall detected was implemented by analyzing whether postures are normal or abnormal based on transi5on Music alert with a stop bulon if possible fall A Smartphone Based-Real Time Daily Ac5vity Monitoring System 24

25 Takeaways con5nued This approach can: Correctly detect various falls efficiently Real-5me within a smart phone Avoid false posi5ves and false nega5ves Situa5ons accounted for: Fall quickly onto ground Fall slowly onto bed Falls ending in lying or sit-5lted Normal lying A Smartphone Based-Real Time Daily Ac5vity Monitoring System 25

26 Discussion A Smartphone Based-Real Time Daily Ac5vity Monitoring System 26

27 References Zhang, S., McCullagh, P., Nugent, C., Zheng, H., Black, N.: An ontological framework for ac5vity monitoring and reminder reasoning in an assisted environment. J. Ambient Intell. Humaniz. Comput. 4(2), (2013) Zhang, S.; McCullagh, P.; Zhang, J.; Yu, T. A Smartphone Based Real-Time Daily Ac5vity Monitoring System. Clust. Comput. 17, (2014) Zhang, S., McCullagh, P., Nugent, C., Zheng, H.: A theore5c algorithm for fall and mo5onless detec5on. In: 3rd IEEE Interna5onal Conference on Pervasive Compu5ng Technologies for Healthcare, pp. 1 6 (2009) Image References hlp:// hlp://i.stack.imgur.com/gbzqg.png A Smartphone Based-Real Time Daily Ac5vity Monitoring System 27

28 Addi5onal Readings Casilari, Eduardo, Rafael Luque, and María-José Morón. "Analysis of android device-based solu5ons for fall detec5on." Sensors 15.8 (2015): Fraś, Mariusz, and Mikołaj Bednarz. "Simple Rule-Based Human Ac5vity Detec5on with Use of Mobile Phone Sensors." Informa.on Systems Architecture and Technology: Proceedings of 37th Interna.onal Conference on Informa.on Systems Architecture and Technology ISAT 2016 Part II. Springer Interna5onal Publishing, Luque, Rafael, et al. "Comparison and characteriza5on of android-based fall detec5on systems." Sensors (2014): Yu, Lei, et al. "A Compressed Sensing-Based Wearable Sensor Network for Quan5ta5ve Assessment of Stroke Pa5ents." Sensors 16.2 (2016): 202. Yu, Lei, et al. "A remote quan5ta5ve Fugl-Meyer assessment framework for stroke pa5ents based on wearable sensor networks." Computer methods and programs in biomedicine 128 (2016): Zhang, Shumei, Paul McCullagh, and Vic Callaghan. "An efficient feature selec5on method for ac5vity classifica5on." Intelligent Environments (IE), 2014 Interna.onal Conference on. IEEE, A Smartphone Based-Real Time Daily Ac5vity Monitoring System 28

29 Strengths and Weaknesses Strengths Weaknesses Provide a prac5cal solu5on Thorough explana5on of 3D coordinate system Thorough explana5on of calcula5ons of mo5onless and mo5on ac5vi5es Did not account for security/privacy concerns Poor transi5on between methodology and experiments Many gramma5cal mistakes made understanding difficult Limita5on of simula5ng falls A Smartphone Based-Real Time Daily Ac5vity Monitoring System 29

30 Future Work More ac5vity postures and fall situa5ons such as moving up/down stairs, cycling, driving and running Try higher sampling rates Implement a similar study for smart watches/ other wearable technology Implement real world case study A Smartphone Based-Real Time Daily Ac5vity Monitoring System 30

31 Discussion Ques5ons How can we get accelerometer data from actual falls, without simula5on? What are the ethical implica5ons from using this technology? Do you think this is a viable solu5on for the global health problem of falling? Do you think another wearable technology (i.e. smart watches) could provide more accurate readings for falls? A Smartphone Based-Real Time Daily Ac5vity Monitoring System 31

About the Course. Reading List. Assignments and Examina5on

About the Course. Reading List. Assignments and Examina5on Uppsala University Department of Linguis5cs and Philology About the Course Introduc5on to machine learning Focus on methods used in NLP Decision trees and nearest neighbor methods Linear models for classifica5on