XAPI and Machine Learning for Patient / Learner

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xAPI and Machine Learning can help us build "intelligent assistance" for patients and learners, but human-in-the-loop machine learning is important. We need good learning design from the beginning and as we return data to instructors and learners immediately, humans can give great inputs to this human-machine collaboration.

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XAPI and Machine Learning for Patient / Learner

  1. 1. Experience API and Machine Learning for Patients and Learners JESSIE CHUANG CLASSROOM AID INC. VISCA ANALYTICS XAPI CHINESE COP
  2. 2. by KenPan
  3. 3. About Us AcrossX Vocabulary Wiki.visualcatch.org
  4. 4. Background and Motivation (1/2) Fact Mobile Health(mHealth)flourished. (Saxon, 2016.) 150 Health 60% Other 40%
  5. 5. Background and Motivation (1/2) Related Monitoring physiological value Health Information (Text, Picture, Video) Fact Mobile Health(mHealth)flourished. (Saxon, 2016.) Mostly focused on providing information and monitoring physiological value. (Vashist, et al, 2014. Saxon, 2016.)
  6. 6. Background and Motivation (1/2) 1. We need to monitor patients daily activities and motions of upper limb. (Subbarao, et al, 1995.) Gap 92% 50 - 70% Fact Related Mostly focused on providing information and monitoring physiological correlation value. (Vashist, et al, 2014. Saxon, 2016.) Mobile Health(mHealth)flourished. (Saxon, 2016.)
  7. 7. Background and Motivation (1/2) 2. Applications provided for Spinal cord injury patients only taught health information, but lack of monitoring and management. Solution 1. Develop an intelligent assistance system - SmartChair APP 2. Proposed Context Awareness Suggestion Engine (iCASE) Fact Mobile Health(mHealth)flourished. (Saxon, 2016.) Mostly focused on providing information and monitoring physiological correlation value in Health APPs. (Vashist, et al, 2014. Saxon, 2016.) Gap Related 1. The need for patients daily activities and rehabilitation of upper limb motion should be monitored. (Subbarao, et al, 1995.)
  8. 8. Background and Motivation (2/2) SmartChair APP (Dept Engineering Science, NTU) Motor power wheelchair (Dept Mechanical Engineering, NTU) Physician, occupational therapist (Dept Occupational Therapy, NTU) Spinal cord injury (Taoyuan Potential Development Center, National Taiwan University Hospital) Data Prescription
  9. 9. Objective Identify problems, dynamic correction, improve the system. System Developer User Integration Implement a mHealth APP for Patients with SCI. Purpose
  10. 10. Objective Click:1 Click:2 Click:3 Click:4 System Developer User Related Staff Implement a mHealth APP for Patients with SCI. Issue Frequent clicks will result in chronic injuries. Purpose
  11. 11. Objective Click:1 Click:2 System Developer User Integration Implement a mHealth APP for Patients with SCI. Issue Frequent click will result in chronic injuries. Purpose Solution Machine builds context-awareness from user history, prescription, & other contexts and prompts recommendations dynamically.
  12. 12. Objective Solution Using Experience API (xAPI) , data can be transferred between different services System Developer User Integration Implement a mHealth APP for Patients with SCI. Purpose IssueIssue Collect data from different services
  13. 13. System Developer User Integration Objective System System’ Collection Analysis FeedbackRevision System System’ Collection Analysis Feedback Revision Stable? Y N Implement a mHealth APP for Patients with SCI. Purpose System revision is usually inefficient and time-consuming. Issue
  14. 14. Objective Solution Add "Recommended Interface” to the current architecture to get feedback. (A/B testing) System Developer User Integration Implement a mHealth APP for Patients with SCI. Purpose System rebuilding is usually inefficient and time-consuming. Issue
  15. 15. Methodology Collection Analysis Technique Server Level Client Level The actual usage can not be completely recorded. Server Log Access easily to information, but can not collect JavaScript event. (Srivastava, 2000.)
  16. 16. Methodology Technique Server Level Client Level Access easily to information, but can not collect JavaScript event. (Srivastava, 2000.) Server Log Direct Access Intermediary Server-Side There will exist the problem of grammar incompatibility in migration. (Corbi and Burgos, 2014.) Leverage Escrow Services to avoid grammar migration issue. Software / APIs Collection Analysis
  17. 17. Methodology Technique Server Level Client Level Server Log Direct Access Intermediary Server-Side xAPI Since the Experience API (xAPI) is an open standard, so it is used as Intermediary Server. Collection Analysis Access easily to information, but can not collect JavaScript event. (Srivastava, 2000.) There will exist the problem of grammar incompatibility in migration. (Corbi and Burgos, 2014.) Leverage Escrow Services to avoid grammar migration issue.
  18. 18. Methodology Technique xAPI Client Level Through Escrow Services to avoid incompatibility problem with the grammar migration. Intermediary Server-Side xAPI Cross- platform Use the Activity Streams to record user experience. Actor (Who) Verb (How) Object (What) Collect and transfer data between heterogeneous platforms through Learning Record Store (LRS). Context description Parameters for different situations can be recorded as context data. Collection Analysis Data integrity
  19. 19. Methodology Context- Awareness Collect user behavior through xAPI Definition Categories Dynamic a person, place or object. (Dey, et al. 2001.)Static Actor behaviors (Actor, Verb, Object) (G. Chen and D. Kotz, 2000.) i.e. user profile, location(G. Chen and D. Kotz, 2000.) Collection Analysis Computing context User context Physical context Time context i.e. network connectivity i.e. time of a day, week i.e. lighting, noise level
  20. 20. Methodology Methods Statistics Sequential Pattern Statistical inference (frequency, average, etc.) is the most popular. Investigate the probability that when an event appears, another event also appears. Classify old data, and then predict the future data. Cluster the data by property similarity. Analyze data pattern on timeline. Context- Awareness Collect user behavior through xAPICollection Analysis Association Rule Clustering Classification
  21. 21. System Developer User Integration System Architecture Filter Model Context- Awareness Model Behavior Model Expert Knowledge Cost-Benefit Analysis iCASE xAPI Therapist System DeveloperRecommended Interface iCASE system Data Flow
  22. 22. Filter Model Context- Awareness Model Expert Knowledge Cost-Benefit Analysis iCASE xAPI Therapist System DeveloperRecommended Interface iCASE system Data Flow System BlocksContext-Awareness Model xAPI Behavior Model xAPI format Mapping Interface Segmentation Block Naming Behavior Library (LRS) Behavior Model
  23. 23. Context-Awareness Model xAPI Behavior Model xAPI format Mapping Block Naming Behavior Library (LRS) Behavior Model: Collecting User Data through xAPI VIPS algorithm (Microsoft, 2003.) Interface Segmentation
  24. 24. Context-Awareness Model xAPI Behavior Model xAPI format Mapping Interface Segmentation Behavior Library (LRS) 23 Behavior Model: Collecting User Data through xAPI Discomfort Record Activity Record Route Record Exercise Exercise Time Block Naming
  25. 25. Context-Awareness Model xAPI Behavior Model Interface Segmentation Block Naming Behavior Library (LRS) Behavior Model: Collecting User Data through xAPI xAPI Verb xAPI Object viewed experienced modified recorded Discomfort Record Activity Record Exercise … xAPI format Mapping Discomfort Record Activity Record Route Record Exercise Exercise Time
  26. 26. xAPI Verb xAPI Object viewed experienced modified recorded Discomfort Record Activity Record Exercise … Context-Awareness Model Behavior Model xAPI format Mapping Interface Segmentation Block Naming Behavior Model: Collecting User Data through xAPI Event: { “Actor”:”John Lee”, “Verb”:”recorded”, “Object”:”Discomfort Record” } Behavior Library (LRS) … xAPI
  27. 27. Filter Model Expert Knowledge Cost-Benefit Analysis iCASE xAPI Therapist System DeveloperRecommended Interface iCASE system Data Flow Behavior Model Context-Awareness Model Filter Model Behavior Model Context Analysis Time Context User Context …… Context Definition Context-Awareness Model Context- Awareness Model Behavior Model
  28. 28. Context- Awareness Model Expert Knowledge Cost-Benefit Analysis iCASE xAPI Therapist System DeveloperRecommended Interface iCASE system Data Flow Filter Model Sort Results Rule filtering engine Filter Model Context- Awareness Model Behavior Model Filter Model
  29. 29. Behavior Model xAPI Recommended Interface Sort Results Rule filtering engine Filter Model Context-Awareness Model Filter Model + Expert Advice # System functions Therapist prescription 135347 Route Distance 5 - 10 km 116170 Frequency of Wheelchair Repair At least once a month 99242 Rehabilitation Three times a week Hash Table Expert Knowledge Therapist
  30. 30. # System functions Therapist prescription 135347 Route Distance 5 - 10 km 116170 Frequency of Wheelchair Repair At least once a month 99242 Rehabilitation Three times a week Sort Results Rule filtering engine Filter Model Filter Model Adjust the weight to improve the bad habits. Time Frequency Therapist prescription (Threshold) Forcibly removed Time Frequency Therapist prescription (Upper limit) Forcibly removed Therapist prescription (Lower limit) 1) User habits 2) Increase the weight Reduce the weight
  31. 31. Expert Knowledge Therapist Behavior Model xAPI Sort Results Rule filtering engine Filter Model Context-Awareness Model L0.5 L1 L0 Recommend L2 L3 Login functio n1 Index functio n1.1 functio n1.3 functio n2 functio n3 functio n2.1 functio n2.2 functio n3.1 functio n3.2 functio n3.3 functio n1.2 Ln:Level n;n: clicks required to access the function 1) Sequentially outputs. 2) Until satisfy the size of the recommended list. Recommendation Interface Recommended Interface
  32. 32. Physiological sensors : blood pressure; blood glucose level; temperature; blood oxygen level; and the signals related to ECG, EEG, and EMG. Biokinetic sensors : to measure the acceleration and the angular rate of rotation that results from body movements. Ambient sensors : to measure environmental factors such as temperature, humidity, light, and the sound pressure level. Self-reporting : alarm, habit, discomfort recording, survey, check- list, request help. Patient-centered “Sensor Network” XAPI records rich CONTEXT information, which is crucial for medical data.
  33. 33. Serve Humanity ASAP XAPI data are highly structured in a pre-designed way, can be integrated meaningfully as soon as collected, data can be put to use right away.  for human to read,  for machine to compute & respond (less guess),  services can talk to each other & work together in real time !! (If … then … ) If data talk in different languages, we can NOT make sense out of them or use them UNTIL the time and computing power are committed to integrate and interpret them.
  34. 34. Related Works LRS Behavior Model Prescription Learning Plan iCASE brain xAPI Applications Human-in-the-loop Machine Learning : machine is human’s collaborator. Food Control for Cancer Patients Training and Learning
  35. 35. Dataviz as a Cognitive Agent
  36. 36. Development Strategy Support instructors & learners with workflow and data flow, connect with their brains with effective visualizations. Put data into use in real time for data-driven actions / automation. Computer learns from human’s actions to build learner model, adaptive recommendations, and iterate from human’s feedback continuously. Image credit: LACEproject
  37. 37. Design Thinking w/I xAPI  From content-oriented to experience-oriented design  Data + Design = Behavior Engineering  Return data to learners first, help them understand their own data, give them agency and ownership of learning.  Process matters, from fix mindset to growth mindset  Learner as co-designer in their learning journey
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  40. 40. 39 Instructor and machine as collaborators to help learner navigate through learning journey, but shall give them agency
  41. 41. Takeaways XAPI is a very effective tool in enabling Apps to serve humanity ASAP, because it connects heterogeneous data immediately. XAPI is about people working together. xAPI projects are really across domains collaboration. XAPI is about connecting current technologies, instead of re-inventing wheels.(API’s power) @classroomaidinc Jessie@classroomaid.org
  42. 42. Citation jia-Ru Ho, Yun Yen Chuang, Ray-I Chang, “SmartChair APP - Mobile Technologies for Supporting Patients with Spinal Cord Injury,” The 11th E-Learning and Information Technology Symposium, 2016. Jessie Chuang is co-founder of Classroom Aid Inc., lead of ADL xAPI Chinese Community of Practice, and consultant of Visca – xAPI visual analytics service. She has provided consulting services and courses in OER (Open Educational Resources), mobile learning design, learning standards, educational technology product/solution design and visualization design for educators, researchers and vendors. Recently she is passionate about xAPI implementation design and analysis, data-driven learning design and how analytics & machine learning work in different industries. She often connects ideas from different domains, in her past career in high tech. R&D she had obtained more than 20 patents for new inventions. Bio.

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