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Exploring the AmIHEALTH paradigm
Jesús Fontecha
University of Castilla-La Mancha
Escuela Superior de Informática de Ciudad...
2
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
CONTENT
• Definition of concepts...
FROM AMI TO AMIHEALTH
Introduction. Definition of concepts
3
4
• Introduction
Internet of Things
eHealth mHealth
Smart environments
& devices
Healthcare
Distributed systems
“diagnosis...
5
• AmIHEALTH
• Ambient Intelligence for Health
More & better infrastructure
- Technologies
- Resources
- Devices
- Commun...
DEFINITION, GOALS, ECOSYSTEMS, LIMITATIONS, SCENARIOS,
INTEROPERABILITY
mHealth
6
7
• mHEALTH
• Mobile Health
• An evolving concept
• Keys
• Use of smart and mobile devices
• Inclusion of wireless technol...
8
• Goals of mHEALTH solutions
• Better management of health
• Make better healthcare decisions
• Find appropriate care
• ...
9
• Objectives for mHealth users
Source: National eHealth Collaborative
Importance (%)Objectives
• Uses of mHealth (in dev...
10
• The mHealth Ecosystem
Source: Wikipedia
• “Ecosystem is a community of living organisms in conjunction with the nonli...
11
• mHealth Ecosystems
Source: http://www.mdtmag.com/article/2013/05/wireless-enabled-remote-patient-
monitoring-solution...
12
• Limitations of mHEALTH solutions and ecosystems
• Technological education
• Infrastructure
• Communications
• System ...
13
• Elements of a mHealth system
• Users
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monit...
14
• Elements of a mHealth system
• Environment
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm....
15
• Elements of a mHealth system
• Sensors and devices
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH p...
16
• Elements of a mHealth system
• Communication technologies
Study casesMonitoringmHealthIntroduction
Exploring the AmIH...
17
• Simulating a real scenario
Obese patient
Smartphone + smartwatch
with HR monitor
Computer, tablet,
smartphone
Server
...
18
• Technology convergence and consistency
Connectivity
Data
Sensors
Mobile
Users
mHealth
care
Study casesMonitoringmHeal...
19
• Standards and interoperability
Continua Health Alliance
• Interoperability between health devices
• Fundamentals of d...
STAGES, BIG DATA, MHEALTH SYSTEMS
Monitoring fundamentals and study cases
20
21
• Monitoring fundamentals
Data acquisition
Data segmentation &
filtering
Data analysis
Not only signal analysis!... Dat...
22
• Big data
• Set of mechanisms to process large amounts of data
• Data is too big, moves too fast, doesn’t fit the stru...
23
• Study cases
• Three mHealth systems
• [Completed] Mobile system for detection and assessment of
frailty syndrome in s...
MOBILE SYSTEM FOR DETECTION AND ASSESSMENT OF FRAILTY
SYNDROME IN SENIORS
First study case <<frailty monitoring>>
24
25
• Goal
Design and development of a system which uses mobile devices to provide a support to
physicians in the frailty a...
26
• Conceptual
model
Mobile system for detection and assessment of frailty syndrome in seniors
Study casesMonitoringmHeal...
27
• Service-oriented mHealth approach
Mobile Web
Services
- Accelerometer data acquisition
- Accelerometer data processin...
28
• Accelerometer data acquisition and processing
Mobile system for detection and assessment of frailty syndrome in senio...
29
• Clinical factors acquisition
Mobile system for detection and assessment of frailty syndrome in seniors
2
Analysis of ...
30
• Clustering and similarities calculation
Mobile system for detection and assessment of frailty syndrome in seniors
3
S...
31
• System overview
Mobile system for detection and assessment of frailty syndrome in seniors
Mobile Services Infrastruct...
32
• Evaluation and results summary
Mobile system for detection and assessment of frailty syndrome in seniors
• Descriptiv...
A SMART AND SENSORIZED FRAMEWORK FOR CONTINUOUS
MONITORING OF DISEASES BASED ON HEALTH ASPECTS
Second study case <<disease...
34
• Goal
Development of a modular framework based on health aspects for monitoring multiple
diseases by using smartphones...
35
• Monitoring cycle
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Vital...
36
• Levels of monitoring
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
A...
37
• Monitoring behavior (trends and objectives)
A smart and sensorized framework for continuous monitoring of diseases ba...
38
• Study cases. Framework overview
A smart and sensorized framework for continuous monitoring of diseases based on healt...
39
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Study casesMonitoringmHe...
40
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Study casesMonitoringmHe...
41
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Study casesMonitoringmHe...
42
• Conclusions
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
• Proposal...
PIA: PERSONAL IADL ASSISTANT. DEVELOPMENT OF A WEB ANALYSIS
TOOL
Third study case <<analysis tool>>
43
44
• Goal & Scenario
Design and development of a Analysis tool for a
AAL system which uses mobile devices and web
platform...
45
• How user’s information (from interaction with environmental objects
and system applications) is collected?
PIA: Perso...
46
• Questionnaires system
• Creation of questionnaires adapted
to the person
PIA: Personal IADL Assistant. Development of...
47
• Recommendations based on results
• Completion of the questionnaire provides
• Recommendations
• Results
PIA: Personal...
48
PIA: Personal IADL Assistant. Development of a web analysis tool
• Some conclusions
• The system saves information abou...
49
• Conclusions
• mHealth as part of AmIHEALTH
• Everything is possible with unlimited time and resources!
• Most importa...
Exploring the AmIHEALTH paradigm
Jesús Fontecha
jesus.fontecha[at]uclm[dot]es
http://jesusfontecha.name
University of Cast...
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Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems

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Ambient Intelligent paradigm for Healthcare. Overview of mHealth systems and ecosystems. Some examples of mHealth project carried out at MAmI Research Group (UCLM, Spain)

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Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems

  1. 1. Exploring the AmIHEALTH paradigm Jesús Fontecha University of Castilla-La Mancha Escuela Superior de Informática de Ciudad Real Ciudad Real, Spain MAmI Research Lab Santiago, Chile, Nov. 25-27, 2015 Summer School Monitoring in Healthcare: Building mHealth ecosystems
  2. 2. 2 Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems CONTENT • Definition of concepts • AmI, IoT, Healthcare, AmIHEALTH • mHealth • Goals, Ecosystems, Limitations, Scenarios, Interoperability • Monitoring fundamentals and study cases • Frailty monitoring • Disease monitoring • Analysis tool • Conclusions
  3. 3. FROM AMI TO AMIHEALTH Introduction. Definition of concepts 3
  4. 4. 4 • Introduction Internet of Things eHealth mHealth Smart environments & devices Healthcare Distributed systems “diagnosis, treatment, and prevention of diseases and impairments in human beings” Use of technology for supporting Healthcare Use of mobile technology for supporting Healthcare Sensors Networks Services Devices Embedded AmI HEALTH AmI Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  5. 5. 5 • AmIHEALTH • Ambient Intelligence for Health More & better infrastructure - Technologies - Resources - Devices - Communication possibilities mHEALTH AmIHEALTH AmI Integration of mobile technologies in Healthcare + • Context Aware • Personalized • Anticipatory • Adaptive • Ubiquity • Transparency Source: http://www.ncbi.nlm.nih.gov/ pmc/articles/PMC3890262/ Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  6. 6. DEFINITION, GOALS, ECOSYSTEMS, LIMITATIONS, SCENARIOS, INTEROPERABILITY mHealth 6
  7. 7. 7 • mHEALTH • Mobile Health • An evolving concept • Keys • Use of smart and mobile devices • Inclusion of wireless technology • Easy social adoption “the delivery of healthcare services via mobile communication devices” …are converging Source: 2010 mHealth Summit of the Foundation for the National Institutes of Health (FNIH) Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  8. 8. 8 • Goals of mHEALTH solutions • Better management of health • Make better healthcare decisions • Find appropriate care • Engage people and access providers • Management of ongoing health (monitoring) Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  9. 9. 9 • Objectives for mHealth users Source: National eHealth Collaborative Importance (%)Objectives • Uses of mHealth (in developed countries) • Collect data • Self monitoring (patient) • Remember events • Appointment • Remote monitoring (doctor) Underdeveloped countries Increasing use of mobile phones Lack of infrastructures Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  10. 10. 10 • The mHealth Ecosystem Source: Wikipedia • “Ecosystem is a community of living organisms in conjunction with the nonliving components of their environments (things like air, water and mineral soil), interacting as a system”. • “mHealth ecosystem is a community of people who interact with mobile devices of an environment to get clinical benefits”. There is no standard definition for mHealth ecosystem • Aspects to consider in development of mHealth ecosystems • Right information • Secure communication • Good system adherence & accessibility • Right time • Reduce technology impact on disease • Sustainable use of resources Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  11. 11. 11 • mHealth Ecosystems Source: http://www.mdtmag.com/article/2013/05/wireless-enabled-remote-patient- monitoring-solutions Source: H. T. Cheng and W. Zhuang, "Bluetooth-enabled in-home patient monitoring system: early detection of Alzheimer's disease," IEEE Wireless Communications, vol. 17, no. 1, pp. 74-79, Feb. 2010 Source: http://www.ece.uah.edu/~jovanov/whrms/ • But, it is not easy… Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  12. 12. 12 • Limitations of mHEALTH solutions and ecosystems • Technological education • Infrastructure • Communications • System Friendliness • Budget “Everything is possible if there is an unlimited amount of time and resources” Theoretically anything is feasible, but in practise… • There is no device that monitor this parameter! • We have commercial devices but, this API is not open! • I do not know how it works! • Here we have not network connection! • Implementation of the project is expensive! • I prefer traditional methods! • … Scientific and technological advances improve our life quality Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  13. 13. 13 • Elements of a mHealth system • Users Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  14. 14. 14 • Elements of a mHealth system • Environment Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  15. 15. 15 • Elements of a mHealth system • Sensors and devices Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  16. 16. 16 • Elements of a mHealth system • Communication technologies Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  17. 17. 17 • Simulating a real scenario Obese patient Smartphone + smartwatch with HR monitor Computer, tablet, smartphone Server DoctorRelatives Tablet, smartphone Patientdata Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  18. 18. 18 • Technology convergence and consistency Connectivity Data Sensors Mobile Users mHealth care Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  19. 19. 19 • Standards and interoperability Continua Health Alliance • Interoperability between health devices • Fundamentals of data exchange • Define the interfaces that enable the secure flow of medical data among sensors, gateways, and end services, removing ambiguity Source: http://www.continuaalliance.org/ Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  20. 20. STAGES, BIG DATA, MHEALTH SYSTEMS Monitoring fundamentals and study cases 20
  21. 21. 21 • Monitoring fundamentals Data acquisition Data segmentation & filtering Data analysis Not only signal analysis!... Data analysis Not only monitoring!... Many processes Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  22. 22. 22 • Big data • Set of mechanisms to process large amounts of data • Data is too big, moves too fast, doesn’t fit the structures of traditional database architectures • Characteristics • Volume. Quantity of data • Variety. Type of content • Velocity. Speed of data generation and retrieving • Variability. Deal with data inconsistency effectively • Veracity. Quality of data • Complexity. Deal with complex data management. • Big data is very useful in data monitoring! Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  23. 23. 23 • Study cases • Three mHealth systems • [Completed] Mobile system for detection and assessment of frailty syndrome in seniors. • <<Frailty monitoring>> • [Ongoing Work] A smart and sensorized framework for continuous monitoring of diseases based on health aspects. • <<Disease monitoring>> • [Completed] PIA: Personal IADL Assistant. Development of a web analysis tool. • <<Analysis tool>> Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  24. 24. MOBILE SYSTEM FOR DETECTION AND ASSESSMENT OF FRAILTY SYNDROME IN SENIORS First study case <<frailty monitoring>> 24
  25. 25. 25 • Goal Design and development of a system which uses mobile devices to provide a support to physicians in the frailty assessment, taking into account a set of relevant clinical variables and movement data. Patient record Accelerometer + Clinical factors Physical activity Analysis of patients and factors Assessment Adquisition Similarity study Tinetti test Mobile system for detection and assessment of frailty syndrome in seniors Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  26. 26. 26 • Conceptual model Mobile system for detection and assessment of frailty syndrome in seniors Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  27. 27. 27 • Service-oriented mHealth approach Mobile Web Services - Accelerometer data acquisition - Accelerometer data processing - Visualization of frailty assessment - Patient record extraction - Comparison and analysis procedure - Setting up a built result - Storage into patient stack 1 2 Mobile system for detection and assessment of frailty syndrome in seniors Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  28. 28. 28 • Accelerometer data acquisition and processing Mobile system for detection and assessment of frailty syndrome in seniors 1 Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  29. 29. 29 • Clinical factors acquisition Mobile system for detection and assessment of frailty syndrome in seniors 2 Analysis of patients and factors Assessment Similarity study Tinetti test Anthropometric Assessment Functional AssessmentNutritional Assessment Cognitive Assessment Geriatric Syndromes Independence in ADL Pathologies & Diseases Gender, Age, Size, Weight, Body Mass Index, Body Mass, Fat Mass, Lean Mass, Total Water, Drug Number Tinetti gait score, Tinetti balnce score, Barthel index, Lawton score, Get-Up and Go, Need help Total protein, Serum albumin, Cholesterol level, Triglycerides, Blood iron, Ferritin, Vitamin B12, Serum folic acid, Serum transferrin, Leukocytes, Lymphocytes, Hemoglobin, Calcium Mini Mental Status, CRP Dementia, Depression, Incontinence, Immobility, Recurrent falls, Polypharmacy, Comorbidity, Sensory deprivation, Pressure ulcer, Malnutrition, Terminally illness Independent, Mild dependence, Moderate dependence, Great dependence, Serious dependence Cardiovascular, Neurological, Respiratory, Digestive, Endocrine, Orthopedic, Osteomuscular, Eyes, ENT, Dermatological Dispersion Measures Arithmetic mean, Standard deviation, Absolute mean diff., Amplitude, Pearson’s coefficient of variation, Variance, Acceleration mean Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  30. 30. 30 • Clustering and similarities calculation Mobile system for detection and assessment of frailty syndrome in seniors 3 Selection of relevant variables • Frailty risk factors Normalization of variables Calculation of similarity measures • Strength of relationship between 2 objects • Gower coefficient 1 2 3 Gower similarity coefficient Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  31. 31. 31 • System overview Mobile system for detection and assessment of frailty syndrome in seniors Mobile Services Infrastructure for Frailty Diagnosis Support based on Gower’s Similarity Coefficient and Treemaps. Jesús Fontecha, Ramón Hervás, José Bravo. Journal of Mobile Information System. July 2013. DOI: 10.3233/MIS-130174 Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  32. 32. 32 • Evaluation and results summary Mobile system for detection and assessment of frailty syndrome in seniors • Descriptive analysis • 20 elderly people (10 men, 10 women) • 60 patient instances (data from 3 times on 20 users) • Global evaluation • More values in variables -> more accuracy • Improve decissions making frailty diagnosis by physicians • Specific evaluation • Results adapted to different domains • Modifying some parameters in the mobile system • Useful in evolutionary studies (nutritional & functional) Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  33. 33. A SMART AND SENSORIZED FRAMEWORK FOR CONTINUOUS MONITORING OF DISEASES BASED ON HEALTH ASPECTS Second study case <<disease monitoring>> 33
  34. 34. 34 • Goal Development of a modular framework based on health aspects for monitoring multiple diseases by using smartphones and smart devices. A smart and sensorized framework for continuous monitoring of diseases based on health aspects • Monitoring and treatment of a disease • Primary aspects • Common to most diseases • Directly related to the disease • Vital signs, physical activity, clinical profile, education, relatives, diet. • Complementary aspects • Depending on the disease • Improve the monitoring • Environment, social relationships, emotions, stress, incomes,… • Patient side (Self-control) & Doctor side (remote monitoring) • New mobile technologies, communication networks, new devices… • New possibilities and opportunities Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  35. 35. 35 • Monitoring cycle A smart and sensorized framework for continuous monitoring of diseases based on health aspects Vital signs monitoring Interaction Primary aspectsComplementary aspects Information flow Self-monitoring Patient profile Physician Patient Smart devices & sensors Smartphone Diet Education Relatives Exercise Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  36. 36. 36 • Levels of monitoring A smart and sensorized framework for continuous monitoring of diseases based on health aspects Aspects coverage + - Self-care + - Level 1 – Basic aspects monitoring Level 3 – Complete monitoring Level 2 – Usual aspects monitoring • Adapted to the patient • Different action levels • Considered aspects • Variables • Level of supervision • Temporary or permanent • Some examples Aspects Devices & sensors Monitoring level Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  37. 37. 37 • Monitoring behavior (trends and objectives) A smart and sensorized framework for continuous monitoring of diseases based on health aspects • Main goal of the framework! • It depends on the disease • Objectives proposed by doctors • Trends calculated by the system (prevention!) Diabetes Trends Objectives • Glucose trend • Glucose level • Physical exercise • Carbohydrate intake Hypertension Trends Objectives • Blood pressure trend • Blood pressure level • Physical activity • Diet Diabetes behavior Hypertension behavior Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  38. 38. 38 • Study cases. Framework overview A smart and sensorized framework for continuous monitoring of diseases based on health aspects • A generic framework to deal with specific disesases Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  39. 39. 39 A smart and sensorized framework for continuous monitoring of diseases based on health aspects Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  40. 40. 40 A smart and sensorized framework for continuous monitoring of diseases based on health aspects Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  41. 41. 41 A smart and sensorized framework for continuous monitoring of diseases based on health aspects Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  42. 42. 42 • Conclusions A smart and sensorized framework for continuous monitoring of diseases based on health aspects • Proposal of health aspect-based framework for smart monitoring • Monitoring of chronic and non-chronic diseases • Interaction with sensors & smart devices • Reducing human interaction • Promoting patient self-control and remote supervision • Future work • Development of software pieces covering each health aspect • Deal with gathering of data from smart devices through open API • Application to specific domain (Diabetes) Endocrine Diet Education Physical activityGlucose level Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  43. 43. PIA: PERSONAL IADL ASSISTANT. DEVELOPMENT OF A WEB ANALYSIS TOOL Third study case <<analysis tool>> 43
  44. 44. 44 • Goal & Scenario Design and development of a Analysis tool for a AAL system which uses mobile devices and web platforms to assess IADL of elderly people at home. PIA: Personal IADL Assistant. Development of a web analysis tool Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  45. 45. 45 • How user’s information (from interaction with environmental objects and system applications) is collected? PIA: Personal IADL Assistant. Development of a web analysis tool System QuestionnairesEnvironmental Interaction Caregivers and physicians complete questionnaires during the use of AAL system. Involves recording the subjects behavior in their AAL environment. Questionnaires collect factual information about individuals Collecting data from user actions in the environment Analysis tool Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  46. 46. 46 • Questionnaires system • Creation of questionnaires adapted to the person PIA: Personal IADL Assistant. Development of a web analysis tool Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  47. 47. 47 • Recommendations based on results • Completion of the questionnaire provides • Recommendations • Results PIA: Personal IADL Assistant. Development of a web analysis tool Need of help in telephone tasks scoreResults Recommendations Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  48. 48. 48 PIA: Personal IADL Assistant. Development of a web analysis tool • Some conclusions • The system saves information about • Minimum interaction of elderly people with environmental objects at home (NFC technology) • Questionnaires completed by caregivers and doctors • Information is used with analysis purposes • Evaluated on 10 caregivers • Useful to assess IADL activities in elderly people and the burden of caregivers by means of interactions and questionnaire results http://mamilab.esi.uclm.es/PIAToolv16/web/ Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  49. 49. 49 • Conclusions • mHealth as part of AmIHEALTH • Everything is possible with unlimited time and resources! • Most important element in mHealth ecosystems -> End User • Main drawbacks • Technology • Communication networks • Environmental resources and interoperability • UX and UI • Functional and friendly systems -> successful proposal! • Scenarios where monitoring is quite relevant • Integration of mHealth ecosystems in bigger environments (Smart cities?) Study casesMonitoringmHealthIntroduction Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems Conclusions
  50. 50. Exploring the AmIHEALTH paradigm Jesús Fontecha jesus.fontecha[at]uclm[dot]es http://jesusfontecha.name University of Castilla-La Mancha Escuela Superior de Informática de Ciudad Real Ciudad Real, Spain MAmI Research Lab Santiago, Chile, Nov. 25-27, 2015 Summer School Monitoring in Healthcare: Building mHealth ecosystems https://www.linkedin.com/pub/jes%C3%BAs-fontecha/28/896/b98 https://www.researchgate.net/profile/Jesus_Fontecha http://www.slideshare.net/JessFontecha/ Thank you!

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