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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  • My first question : How many have watched this movie “Big Hero 6”? In case you don’t know, it’s a fun Disney movie, and Baymax is a personal health companion – a huggable robot that will take care of your health, always. Second question: If possible, do you want to give your family member who’s sick one personal health companion? My boss of my first job taught me “you must tell the conclusion at the beginning of your report”, so here is my conclusion – xAPI is a very effective tool to help us build a Baymax! Of course I’ll tell you why later.
  • A little about us…in short, Classroom Aid is our consulting company, I have been working on xAPI project for 3 years, Visca is our effort to give real-time dataviz for xAPI data, we believe returning data to learners and instructors immediately is crucial. We even provide API for you to embed dataviz inside your own App or dashboard. I am also the lead of xAPI Chinese CoP, Here are our major CoP members. We set up our AcrossX profile, recipes and vocabulary for our CoP. Vocabulary, profile and recipe are to define how we record specific experiences. xAPI is totally different with SCORM. It’s highly flexible and extensible, this is great but xAPI spec. only guarantees data structure interoperability, semantic interoperability must rely on people working together to decide how to record a behavior, that’s why we need CoP. BTW, CoP’s work is different with DISC, run by Aaron Silvers. This community driven vocabulary/profile/recipe is why xAPI is so powerful.
    Here comes the 2nd conclusion: successful xAPI implementation is about “people working together”, among the lessons learned along the way, this is the no. 1 factor.
    Today’s story is from ICAN Lab of National Taiwan University.
  • According to research, average Americans check their mobile phones 150 times a day. 60% is for health purpose, like tracking fitness goal, they said they will use health App more in the next 5 years…. we all agree mobile phone are now our most intimate friend. Apple offers ResearchKit and CareKit for healthcare support and research, and Google has Google Fit SDK, too. Now hospitals also use mobile portal to engage and serve patients.
  • 2/3 of mHealth App only provides health information, 1/3 is to monitor physiological values, it’s not enough for special need patients.
  • 92% of spinal cord injury patients need to use wheelchairs,within 10-15 years, 50-70% have upper limb overuse problem, the probability of chronic illness increases significantly.
  • The solution is to develop Smartchair App that provides instructional videos of rehab. For patients to follow along, medical knowledge, peers interactions functionality, activity monitoring and management. From the data collected, the system behind it, we called Context Awareness Suggestion Engine(iCASE), builds context-awareness and then provides recommendations dynamically to support patients.
    .
  • At the center is Smartchart App, data from the wheelchair and the prescription from physician or therapist are sent here to integrate with data collected by Smartchair. This App is now offered to spinal cord injury patients in these 2 organizations. This simple diagram also shows another layer of people working together.
  • Let’s identify problems to be solved regarding these 3 aspects. Notice that this is how we should use xAPI – pinpoint problems first, not sending whatever xAPI data we can send without purposes in mind.
  • For user’s need, frequent clicks will result in chronic injuries.
  • The solution is to build context-awareness from user’s history records, the prescription and other context data to prompt the recommended action for user. This reduces clicks and provides guidance at the same time.
  • The 2nd issue is we need to communicate between different services – for example, the site for therapists, and wheelchair vendor’s system, in order to solve this, xAPI is leveraged for data transfer and integration.
  • And, system developers need a more efficient way to collect user behavior to feedback to improve the system. Also after revision, users also need effort to re-learn newly-designed interface. Is there a more flexible and agile way?
  • The solution is a recommendation engine to prompt recommended action for supporting users as well as collecting feedback, developers will update the system design only after the hit rate of recommendation is higher than a threshold.
  • Smartchair is a hybrid App with its UI based on web language, here we discuss how to collect interaction data.
  • XAPI is a user-centered data format, use Actor-Verb-Object along with other rich contextual and result data, the experience can be captured with necessary details. You might know better than me that for medical data, context information is crucial. This way allows collecting and transferring data in between heterogeneous platforms through intermediary server – the Learning Record Store, LRS.
  • Now let’s look at context-awareness modeling. Contexts could be static or dynamic, and belong to many categories that matter to our analytics. You can imagine sensors are like our 5 senses, and computing is like our brain. Only with xAPI, we can record heterogenous data in a coherent way, so those 5 senses data can be put together immediately for computing brain to build context-awareness. For example, it’s like Baymax observes when, how long, with whom, the patient did exercises.
  • So how does computer brain work? it’s like Baymax can reason that the patient walks longer when it’s morning than afternoon, or exercise longer when exercising together with peers. There are many well-developed machine learning methods to let machine to find patterns in data and do predictions.
    Statistics is statistical inference from history data, i.e. if a patient eats candies more often than others in the past, we can assume he will more candies in future.
    Sequential pattern is analyzing data sequence on timeline, i.e. if the patient walks longer in the morning, machine can suggest him to walk in the morning, or if him usually did A and B in sequence, the system can prompt B after he does A to make the user interface more friendly, which is especially useful for spinal cord injury patient.
    Association rule is to find the association possibility whenever A happens, B will happen. If B is something we like to avoid, when machine detects A happens, it can provide intervention or alarm humans to take actions accordingly.
    Classification is to classify old data, and predict future data. i.e. John is more self-driven or Mary is more social-oriented, it can prompt more social interaction streams to Mary to motivate her, but more personal dashboard summary for John. Or machine can detect patient’s self-reporting data is fake. Another possibility is that potential illness might be detected thr. User’s data pattern, physicians can plan prevention care.
    Clustering is to group data by property similarity, there might be no pre-defined groups in the beginning, it really is finding patterns in data themselves.
    You can find that this is all about personalization of health care.

    Other machine learning applications….
  • This is the conceptual system architecture, mostly you can have an idea about how the system works to process data to support users. xAPI collects data from functionalities interface into building behavior model, which will be sent to context-awareness model for calculation, and then combine with expert knowledge, these data will be processed by filter model to sort out suggested next steps and offer to the patient. The cost-benefit analysis is to analyze if this recommendation engine really improve user experience and solve the issue.
  • This block “behavior model” is where xAPI works. So let’s go into more details here.
  • This is a way to model interaction data from this mobile App – first, interface segmentation.
  • 2nd, block naming, it’s suggested to make it meaningful for both humans and machine to read.
  • 3rd, map and design xAPI statements
  • A simple example is like this : John recorded discomfort record. That’s the most basic sentence of an xAPI statement, along with it context information can be recorded as well – such as timestamp (when did this happen), location (where did this happen), result (what is the discomfort level), or even the environment temperature.
  • Then user’s behavior records will be processed along with context awareness model, using those mentioned machine learning methods to model data and find patterns in data, also taking into consideration of current contexts such as current time and location.
  • This filter model is to filter and sort the recommended next steps for the user.
  • Usually this kind of recommendation engine only factors in user’s habits, but for a patient, we need to put therapist’s prescription into filter model. All recommended actions are referring to the prescription list, the priority is adjusted according to user’s history and context.(for example, time)
  • It adjusts the weight for recommending actions according to user’s history data, for behaviors with lower frequency, the weight will be increased, for behaviors with higher frequency, the weight will be reduced, or totally remove an action if the frequency exceeds the threshold.
  • The recommendation engine prompts recommended action at 0.5 layer, on top of the current hierarchy. As I mentioned, this reduces clicks, and provides guidance as well.
  • Now we can have tons of medical devices and sensors to collect data around a patient, even swallow a pill and take pictures inside the body. Data, data, data everywhere, 2 things we care here. As mentioned before, for medical data the related contexts of a data point is very important, and xAPI can record that in a standard way.
  • 2nd, now all these data talk in different languages…
  • Based on the same system architecture, 2 related works are 1. food control for cancer patients (to help them balance between preferences and nutrition requirements). 2. the other is in learning and training domain, in that case learning plans designed by teachers will replace the prescription. The strategy here is the collaboration relation between machine and experts (therapist or teacher). First the machine builds recommendations from expert’s prescription and user’s behavior history to balance both dynamically, the output of machine learning is under monitoring of expert, the expert can modify the recommendation if needed, and then machine will fit to that labeled data every time. That means, machine learns from its humans and real data continuously.
    The benefit for experts to use this system is that collected data can give them a better picture about their patients or students, they can take actions at the moments of needs; and with enough training, machine’s recommendation can be very helpful and share the expert’s work loading. Machine’s findings from big volume data could be valuable inputs to his prescription design.
  • Here we go. Dataviz can help you see the whole picture of integrated xAPI data IN REAL TIME, zoom in to see a tree or zoom out to see a forest, for learners, instructors, patients, family, and therapists.
  • 3 takeaways:

    … Usually driven by people who care about patient-centered or learner-centered service. Talk to an xAPI expert about your questions needed to be answered or something you like to improve, work together to design xAPI implementation. We connect with machine learning experts too. xAPI project is really across domains.
    … maybe you’ve heard of API economy, XAPI is API for experience data, if with proper xAPI profile/recipe design, it can be plugged with machine learning APIs or our iCASE system. To reduce technological barrier, xAPI wrappers and connectors are available.
    Think of the fact that we have such “personalized advertisements” all around us everyday, can we bring that “personalization” to learners and patients? It’s up to all of us.
  • 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
    38. 38. 37
    39. 39. 38
    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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