A Smart Healthcare Monitoring System for Independent Living

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Presented by N K Suryadevara
Massey University

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A Smart Healthcare Monitoring System for Independent Living

  1. 1. A Smart Healthcare Monitoring System for Independent Living N. K. Suryadevara Ph.D student Under Supervision Prof. Subhas Chandra Mukhopadhyay SEAT-Massey University
  2. 2. Courtesy: National Geographic Magazine It’s not just hype New Science could lead to very long lives Good News But…
  3. 3. Need Assistance…….
  4. 4. How does someone die alone in their home without anyone realising?
  5. 5. • Ambient Assisted Living • Wellness Determination • Sensor Deployment • Wellness Indices and Forecasting • Sensor Activity Pattern Analysis • Conclusion • Q & A
  6. 6. The concept of Ambient Assisted Living • To extend the time, people can live in their preferred environment by increasing their autonomy, self-confidence and mobility; • Support maintaining health and functional capability of the elderly individuals; Ref: http://www.aal-europe.eu/
  7. 7. www.standards.co.nz www.stats.co.nz
  8. 8. Functional Blocks of Health Informatics System for Wellbeing and Independent Living Health Informatics System Activities of Daily Living Monitoring Physiological Monitoring Environmental Monitoring Wellness Determination Non -Invasive Wearable and Non-Wearable- Unobtrusive
  9. 9. Health Informatics System collects more data  Plenty of Data Collection methods/tools  More resources are required for Analyzing the data Proper Information can be gained from the analysis (translation) of data Important indications for proper decision-making Notify policy development Our Solution: Web-based reporting tool to analyze and infer right decisions
  10. 10. Key Components – Health Informatics System Instrumentation Sensing Objects Wireless Communication Information Processing  Information and Communication Technology • Compatibility of Sub-Systems • Flexibility • Robust • Real-Time Processing of Data
  11. 11. Smart Home Monitoring System AAL Services (Energy Consumption) (Human Physiological) Recognition of Human ADL’s Human Wellness Determination β1, β2 Domestic Objects usage Trend through Time Series Data Mining Sensors Data Acquisition Remote Interoperability Internet www.iots2is.org Health Care Provider/ Relatives/ Tele-Care Services Data Base WSN
  12. 12. • How “Well” a person living alone in their home is able to perform their essential daily activities in terms of using household appliances? • Performance of Daily Activities Performance behavior • Livelihood activities are Cyclic • Monitor usage of household objects for recognizing the habitual nature of the person. Wellness of an Independent Living Person
  13. 13. Monitoring Basic ADL Microwave, Water Kettle, Toaster or (any other item used regularly in Kitchen) Room Heater Television/Radio Bed, Couch, Chair, Toilet Any other appliance used as habitual Preparation of Food (Breakfast,Lunch,Dinner) Sleeping Toileting, Self Grooming Dinning Relax Watching TV(while sitting on Couch) ADL α Household Appliance Usage N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, “Forecasting the behavior of an elderly using wireless sensors data in a smart home”, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004. Passive Infra Red(PIR) Sensors: Mobility(Movements monitoring)
  14. 14. Sensing Units-HMS
  15. 15. Human Emotional State Recognition Unit An a ad Ne t a
  16. 16. Robust Supervisory Control Unit of Home Monitoring System
  17. 17. Integrated system for simultaneous human emotion and ADL recognitions
  18. 18. Medicine Dispenser Unit ZigBee Based Wireless Receiver Unit Micro Controller based Opening + Locking Arrangement Automatic Electronic Medicine Dispenser Real-Time Clock and Control Unit, ZigBee based Wireless Communications (Central Coordinator)
  19. 19. Sensor activity status Subject #1 Subject #2 Subject #3 Subject #4 Suryadevara N.K, Mukhopadhyay S.C, “Wireless Sensor Network based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal-2012, Vol: 12 Issue: 6, Page(s): 1965 – 1972
  20. 20. • Domestic objects are used at regular time intervals in the day to day life • Usage durations and the frequency of use are varied • “Human behaviours in constant contexts recur, because the processing that initiates and controls their performance becomes automatic” • “Frequency of past behaviour reflects the habit strength and has a direct effect on future performance”
  21. 21. Selection of Sensors and Using Minimum Number of enso s fo Monito in Basic ADL’s Life Style of the Elderly Sensors for basic ADL monitoring Determination of minimum sensors | | ( ) 1/ ( ) l c l c s c loc TT s S f s    Frequency of Sensor usage Room Type Sensor Type Connected to Device η Trail Test Living Force, Electrical Couch, Chair, TV, Heater 0.03, 0.05, 0.05,0.1 0.03, 0.04, 0.03,.1 Kitchen Electrical Microwave, Toaster, Kettle 0.05, 0.05, 0.02 0.04, 0.06, 0.00 Bed Force Bed 0.29 0.37 Bath Force Toilet 0.35 0.33 Storage Contact Cupboard 0.01 0.00 N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
  22. 22. N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004. Block diagram of the Wellness Determination
  23. 23. Behaviour Detection Regular or Irregular Sensor Activity Pattern ForecastingWellness Indices To Minimize “Fa se A a ms” N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
  24. 24. Sensor Deployment Layout
  25. 25. WSN-Communication Using XBee Module • IEEE-ZigBee Protocol, ISM 2.4 GHz frequency Configuration: Mesh Topology-Reliable Data Transmission. Sampling Rate: Depending on the Type of Sensing Unit.
  26. 26. Wellness Indices Inactive usage measurement of appliances β1 = 1 – t/T t = Time of Inactive duration of all appliances (i.e.) duration of time during which no appliances are used. T= Maximum inactive duration during which no appliances are used under normal condition. Excess usage measurement of appliance β2 = 1 + (1 – Ta / Tn) Ta= Actual(Current) usage duration of a appliance. Tn = Maximum usage time of appliance under normal condition. Suryadevara N.K, Mukhopadhyay S.C, “Wireless Sensor Network based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal-2012, Vol: 12 Issue: 6, Page(s): 1965 – 1972
  27. 27. Limitations • Seasonal variations such as day of the week, weekly, monthly are not taken into consideration therefore it is likely that more false warning messages will be generated. • The threshold value of wellness indices was derived to 0.5 and has been considered as safe limit beyond which a warning message is sent to the elderly/healthcare provider regarding the daily activity behaviour. (Need for Dynamic Wellness Functions) Suryadevara N.K, Mukhopadhyay S.C, “Wireless Sensor Network based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal-2012, Vol: 12 Issue: 6, Page(s): 1965 – 1972
  28. 28. Improved Wellness Function (β1, new) β1,new = e −t T
  29. 29. Improved Wellness Function (β2, new): β2,new = e Tn−Ta Tn
  30. 30. Advantages of Improved Wellness Functions: • For linear wellness indices (β1 and β2) the threshold value was kept at 0.5 for generating irregular behaviour warning messages. • The improved wellness index β1,new, β2,new allow more time to generate warning messages for the same threshold. • It was also observed that at 50% of the time period the new wellness functions indicates a wellness of 62%, better than 50% of the previous wellness indices.
  31. 31. Dynamic – Maximum Inactive Usage and Excess Active Usage Durations (T, Tn): • T = δ (C1t – C1t-1) + (1 −δ) Tt-1 C1t = α (xt) + (1 − α) (C1t-1+ Tt-1) + St Tn = δ (C2t – C2t-1) + (1 −δ) Tnt-1 C2t = α (xt) + (1 − α) (C2t-1+ Tnt-1) + St • T: Trend of the Maximum Inactive usage Durations, • Tn: Trend of the Maximum excess active usage durations, • C1t, C2t: Seasonal trends; • xt is the object usage observation at the current time, • s is the number of periods in one cycle (week) (i.e. s=7), Tt=(1/s)((xs+1-x1)/s+ (xs+2-x2)/s+…. (x2s-xs)/s) • α, δ are the smoothing parameters (0 to 1), selected by minimizing mean square errors. • St is the seasonal term (for spring =1, summer=2, monsoon=3, autumn=4, winter=5, prevernal=6) N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
  32. 32. Forecasting τ =Tt = δ (Lt − Lt−1) + (1 −δ) Tt−1 Lt = α (xt − St−s) + (1 − α) (Lt−1 + Tt−1) St = γ (xt − Lt) + (1 − γ) St−s (Lt=(1/s) (x1+x2+x3+…..xs) Tt=(1/s)((xs+1-x1)/s+ (xs+2-x2)/s+…. (x2s-xs)/s) St= xk -Ls, where k=1, 2….s.) Ft+m=Lt+Ttm+St-s+m • Allowable duration of regular activity = Forecasted duration ± 2 * standard deviation Trend using activity duration time series N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
  33. 33. Wellness Functions and forecast of the basic ADL’S N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
  34. 34. 600. 700. 800. 900. 1000. 1100. 1200. 0 10 20 30 40 50 60 70 Fig 9(a) Toilet usage Trend for70 days Fig 9(b) Toilet usage (Ninth week forecast pattern) (b) Fig 9 (c) Toilet usage (Tenth weekforecast pattern) Fig 9(d) Chair Usage Trend for 70 days 600. 700. 800. 900. 1000. 1100. 1200. 0 10 20 30 40 50 60 70 600. 800. 1000. 1200. 1400. 1600. 1800. 2000. 2200. 2400. 0 10 20 30 40 50 60 70 Forecast Observed Forecast Trend Observed Observed Trend Observed Days Days Days Days N.K.Suryadevara, S. C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Elsevier: Engineering Applications of Artificial Intelligence, Available online 12 September 2013, ISSN 0952-1976, http://dx.doi.org/10.1016/j.engappai.2013.08.004.
  35. 35. Sensor Activity Status Subject #1 Monitor the Movements of the Person
  36. 36. Sensor Activity Pattern Discovering Interesting Patterns in Data Perform Classification of New Data based on Training Data Sequential Patterns/Rules (Finding inherent regularities in data) With Time Constraints Cluster of Similar Instances Associations Top-K Sequential Rule Mining (Redundant/Non-Redundant)
  37. 37. Sequence Pattern Analysis* of Data Mining Sequence Pattern: A pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. *First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context of frequent item sets and association rule mining. Ex: Let Sensor_ID={FR,CR,R1,KT} Let t1=(FR,CR),t2=(FR,CR,R1) and t3=(KT) be three sets of sizes 2,3 and 1. A eq ence ‘ ’=<t1,t2,t3> = <(FR,CR),(FR,CR,R1),(KT)> e esents a length |S|=6. A Sequence S1=<(FR,CR),(FR,CR,R1),(FR,CR,KT),(CR),(CR,CR)> supports S A “Sequential Database” Expected Data Base(EDB) is derived from Data Base(DB) by eliminating certain Sequences
  38. 38. • Algorithm: Sensor Activity Pattern–Pruning (node n= 𝑠1, 𝑠2, 𝑠3, … . . 𝑠𝑙 , Sn) 1.EDB = ∅,Support=0 2. For each (i ∈ Sn) If ( (𝑠1, 𝑠2, 𝑠3, … . . 𝑠𝑙, 𝑖 ) is frequent ) EDB = EDB ∪ 𝑖 3. For each (i ∈ EDB) 4. Sensor Activity Pattern-Pruning((𝑠1, 𝑠2, 𝑠3, … . . 𝑠𝑙, 𝑖 , EDB, all e ements in EDB eate than ‘i’ and satisfies Support(EDB) // Generating EDB at node i that satisfies Support for the SAP Support (EDB): If SAP is infrequent, and in order for the SAP to propagate to frequent then it should have a length at-least σ−1(SAP).
  39. 39. BK #}BK #}</00> FR #}BK R3 #}FR #}</02> R2 BK #}R2 BK #}CR #}</04> FR KT #}BK KT #}R2 KT #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 KT #}CR R2 KT #}R2 #}R2 #}R2 #}</05> R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}FR CR KT #}BK #}R2 BK CR KT #}BK KT #}CR KT #}CR R2 #}#}</06> BK #}R2 BK #}R2 #}R2 #}R2 #}BK #}R2 #}R2 #}R2 #}R2 #}BK KT #}KT #}CR R2 #}R2 #}#}</09> BK #}BK #}BK #}CR FR #}FR CR #}CR FR KT #}KT FR #}BK FR KT #}KT #}KT CR FR #}KT #}CR KT #}CR FR #}R3 #}</10> R2 #}R2 CR FR BK #}BK FR CR KT #}R2 #}R2 KT #}R2 KT #}KT #}KT #}KT #}KT #}KT #}FR R3 KT #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}R2 #}CR KT #}BK #}R3 #}R3 #}</11> 06-Oct-2013(Sunday) Parser to generate set of Sequences(Sensor ID’s) based on the (day of the week) and (hour) and (Minute) enso ID’s eq ence within the Min te “#}” “</” o “>” Pattern Sequence Detection
  40. 40. Tracking System for Inhabitant Movements inside the house
  41. 41. Tuesdays(21May-06Aug)
  42. 42. Grouped matrix-based visualization (Hahsler and Chelloboina, 2011) Tuesdays(21May-06Aug)
  43. 43. 43 WSN Assisted Intelligent Integrated Healthcare Platform for Wellbeing and Independent Living The healthcare platform consists of 1. Appliances Monitoring Unit 2. Physiological parameters monitoring unit 3. Human Posture and Position Detection Unit 4. Human Emotion Recognition Unit 5. Automatic Medicine Dispenser Unit 6. Power Management Unit 7. Robust Supervisory Control Unit 8. Safety Box Unit Conclusion
  44. 44. • A WSN Assisted and Embedded Processing based smart home to care elderly people. • The integrated system is able to support people who wish to live independently. • The developed system is robust and is possible to develop at a low cost due to ingenious development. •The technology assisted home will alert the caregiver in advance about the trend of the health status, so that necessary precaution can be taken. 44
  45. 45. s.c.mukhopadhyay@massey.ac.nz n.k.suryadevara@massey.ac.nz

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