1. www.monash.edu.au Mobile Data Mining for Intelligent Healthcare Support By: Pari Delir Haghighi, Arkady Zaslavsky, Shonali Krishnaswamy, Mohamed Medhat Gaber Center for Distributed Systems and Software Engineering Monash University, Australia

2. www.monash.edu.au 2 An Overview Introduction The State-of-the-Art Situation-Aware Adaptive Processing (SAAP) of Data Streams Fuzzy Situation Inference (FSI) Adaptation Engine (AE) Implementation Evaluation Future Work Conclusion

3. www.monash.edu.au 3 Introduction Mobile healthcare services: provide a convenient, safe and constant way of monitoring of vital signs development of mobile healthcare applications encouraged by –innovations in mobile communications –low-cost of wireless biosensors the issues: –maintaining continuity of running applications on mobile devices –enabling real-time analysis of data and decision making

4. www.monash.edu.au 4 The State-of-the-Art (1) recent works in mobile healthcare –mostly focused on using, enhancing or combining existing technologies >projects: EPI-MEDICS [RFN05],MobiHealth [MWH07] –limited use of context-awareness –lack of resource-aware data analysis techniques a need for a general approach: –performing smart and cost-efficient analysis of data in real-time –providing a general model for representation of real-world and health-related situations

5. www.monash.edu.au 5 The State-of-the-Art (2) Ubiquitous Data Stream Mining (UDM) –real-time analysis of data streams on-board small/mobile devices >techniques and algorithms for resource-aware data stream mining [GKZ05] However, to perform smart and intelligent analysis of data on mobile devices – imperative to factor in contextual information

6. www.monash.edu.au 6 Situation-aware Adaptive Processing (SAAP) of Data Streams SAAP: 1.incorporates situation-awareness into data stream mining 2.performing situation-aware adaptation of data streaming parameters according to occurring situations and available resources 3.situation-awareness achieved by Fuzzy Situation Inference (FSI) model –FSI combines fuzzy logic principles with the Context Spaces (CS) model >a general context modeling and reasoning approach for pervasive computing environments

7. www.monash.edu.au 7 The Framework of SAAP

8. www.monash.edu.au 8 SAAP Fuzzy Situation Inference (FSI) Engine Adaptation Engine (AE) –Resource-aware strategies –Situation-aware strategies –Hybrid strategies

9. www.monash.edu.au 9 Fuzzy Situation Inference (FSI) FSI inspired by the Context Spaces (CS) Model [PAD04] The CS model advantages: >deals with uncertainty associated with sensors’ inaccuracies disadvantages: >does not deal with other aspect of uncertainty related to human concepts and real-world situations FSI integrates fuzzy logic principles into the CS model FSI –enables representation of vague situations –reflects minor and delta changes in the inference results

10. www.monash.edu.au 10 FSI: Situation Modeling linguistic variables: e.g. heart rate terms/Fuzzy sets: e.g. low, normal, fast membership functions to map input data into fuzzy sets A FSI Rule defines a situation –consists of multiple conditions joined with the AND operator >each condition can be a disjunction of conditions e.g. if Room-Temperature is ‘hot’ and Heart-Rate is ‘fast’ and ( Age is ‘middle-aged’ or ‘old) then situation is ’heat stroke’ ’

11. www.monash.edu.au 11 Reasoning technique 1 Heuristics: weight and contribution CS FSI Reasoning technique 2 Heuristics: sensors’ inaccuracy CS FSI Reasoning technique 3 and 4 Heuristics: Symmetric and Asymmetric context attributes, partial and complete containment CS FSI CS FSI where and Reasoning Techniques (1,2, 3) where and

12. www.monash.edu.au 12 SAAP Fuzzy Situation Inference (FSI) Engine Adaptation Engine (AE) –Resource-aware strategies –Situation-aware strategies –Hybrid strategies

13. www.monash.edu.au 13 Adaptation Engine (AE)

14. www.monash.edu.au 14 The Controller CasesAdaptation Strategy 1 – R at safe level and S at safe LevelSituation-aware 2 – R at safe level and S at medium levelSituation-aware 3 – R at safe level and S at critical levelSituation-aware 4 – R at medium level and S at safe levelResource-aware 5 – R at medium level and S at medium levelHybrid 6 – R at medium level and S at critical levelHybrid 7 – R at critical level and S at safe level 8 – R at critical level and S at medium level 9 – R at critical level and S at critical level Other strategies e.g. migration

15. www.monash.edu.au 15 Lightweight data stream mining algorithms –Adjusting mining parameters according to resource availability –E.g: LWC (LightWeight Clustering) [GKZ05] >considers a threshold distance measure for clustering >Increasing the threshold discourages forming of new clusters –in turn reduces memory consumption Resource-aware Adaptation

16. www.monash.edu.au 16 Situation-aware Adaptation based on the concept of resource-aware adaptation but adjustment of parameters according to results of situation inference (FSI engine) starts with pre-set values of parameters for each situation at run-time based on degree of fuzziness of each situation these parameters adjusted µ: degree of fuzziness of each situation p: parameter value

17. www.monash.edu.au 17 Hybrid Adaptation when both resources and situations are getting critical a trade-off between the results of these two strategies hybrid method combines resource-aware and situation-aware strategies and deals with the trade-off: criticality of resources and situations represented by a value between 0 and 1

18. www.monash.edu.au 18 Implementation healthcare monitoring application Implemented in J2ME deployed on a Nokia N95 mobile phone situations: ‘normal’, ‘Pre- Hypotension’, ‘Hypotension’, ‘Hypertension’ and ‘Pre-Hypertension’ context: SBP, DBP and HR using a Bluetooth-enabled ECG sensor

19. www.monash.edu.au 19 Evaluation of FSI A Comparative Evaluation The reasoning approaches –FSI –CS –Dempster-Shafer (DS) to highlight the benefits of the FSI for reasoning about uncertain situations

20. www.monash.edu.au 20 FSI Evaluation: Dataset The dataset: –generated continuously (data rate is 30 records/minute) in ascending order –131 context states –used our data synthesizer >to represent the different events (of the DS model) –contribute to the occurrence of each pre-defined situation as well as the uncertain situations

21. FSI Evaluation: Results

22. www.monash.edu.au 22 FSI Evaluation: Results when situations are stable and pre-defined (not vague) –all have a relatively similar trend –more noticeable with the CS and FSI models when situations change and evolve –the CS and DS methods show sudden rises and falls with sharp edges >not matching the real-life situations –Yet FSI reflects very minor changes between situations >represent changes in a more gradual and smooth manner >more appropriate approach for health monitoring applications

23. www.monash.edu.au 23 Evaluation of Situation-aware Adaptation Data stream mining algorithm used –the LWC algorithm situations –‘normal’, ‘hypertension’ and ‘hypotension’ –situations’ importance: 0.1, 0.9 and 0.5 –parameter set values: 42 (normal), 10 (hypertension) and 26 (hypotension) –context attributes: SBP, DBP and HR Dataset –the same used in the FSI evaluation >131 context states (rows)

24. www.monash.edu.au 24 SA Evaluation: Results

25. www.monash.edu.au 25 SA Evaluation: Results threshold value automatically adjusted according to the fuzziness and membership degree of each situation when situations are normal, threshold increases –increasing the threshold value for normal situations decreases the mining output –reduces resource consumption when situation get critical, threshold decreases –increases the number of the output (clusters) and accuracy level of results that is required for closer monitoring

26. www.monash.edu.au 26 Future work currently finalizing implementation and evaluation of hybrid adaptation using RA- Cluster using RA-Cluster enables adaptation of sampling rate according to battery charge integrating time-constraint into adaptation of battery usage working on testing of our prototype in real- world situation in conjunction with relevant healthcare professionals

27. www.monash.edu.au 27 References [GZK04] Gaber MM, Zaslavsky A, Krishnaswamy S (2004), A Cost-Efficient Model for Ubiquitous Data Stream Mining, Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia Italy. [GKZ05]Gaber MM, Krishnaswamy S, Zaslavsky A (2005) On-board Mining of Data Streams in Sensor Networks”, A Book Chapter in Advanced Methods of Knowledge Discovery from Complex Data, (Eds.) S. Badhyopadhyay, U. Maulik, L. Holder and D. Cook, Springer Ver-lag. [MWH07] Mei, H., Widya, I., Halteren, A.V., and Erfianto, B., A Flexible Vital Sign Representation Framework for Mobile Healthcare. 2007. [PLZ05] Padovitz, A., Loke, S.W., Zaslavsky, A., Burg, B. and Bartolini, C.: An Approach to Data Fusion for Context-Awareness. Fifth International Conference on Modeling and Using Context, CONTEXT’05, Paris, France (2005). [PZL06] Padovitz, A., Zaslavsky, A. and Loke, S.W.:. A Unifying Model for Representing and Reasoning About Context under Uncertainty, 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), July 2006, Paris, France (2006). [RFN05] Rubel, P., Fayn, J., Nollo, G., Assanelli, D., Li, B., Restier, L., Adami, S., Arod, S.,Atoui, H., Ohlsson, M., Simon-Chautemps, L., Te´lisson, D., Malossi, C., Ziliani, G., Galassi, A., Edenbrandt, L., and Chevalier, Ph., Toward Personal eHealth in Cardiology: Results from the EPI-MEDICS Telemedicine Project. Journal of Electrocardiology 2005. 38: p. 100-106

28. www.monash.edu.au 28 Thank you Questions?