XAPI and Machine Learning for Patient / Learner

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Experience API and Machine Learning for Patients and Learners JESSIE CHUANG CLASSROOM AID INC. VISCA ANALYTICS XAPI CHINESE COP

Transcript of XAPI and Machine Learning for Patient / Learner

Experience API and Machine Learning for Patients and Learners

JESSIE CHUANGCLASSROOM AID INC.

VISCA ANALYTICS

XAPI CHINESE COP

Background and Motivation (1/2)Fact Mobile Health(mHealth) flourished. (Saxon, 2016.)

150Health60%

Other40%

Background and Motivation (1/2)

Related

Monitoring physiological

value

Health Information(Text, Picture, Video)

FactMobile Health(mHealth) flourished. (Saxon, 2016.)

Mostly focused on providing information and monitoring physiological value. (Vashist, et al, 2014. Saxon, 2016.)

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.)

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)

FactMobile 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.)

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

Objective

Identify problems, dynamic correction, improve the system.

System DeveloperUserIntegration

Implement a mHealth APP for Patients with SCI.

Purpose

Objective

Click : 1 Click : 2 Click : 3 Click : 4System DeveloperUserRelated Staff

Implement a mHealth APP for Patients with SCI.

Issue Frequent clicks will result in chronic injuries.

Purpose

Objective

Click : 1 Click : 2System DeveloperUserIntegration

Implement a mHealth APP for Patients with SCI.

Issue Frequent click will result in chronic injuries.

Purpose

Solution

Machine builds context-awarenessfrom user history, prescription, & other contexts and prompts recommendations dynamically.

Objective

Solution

Using Experience API (xAPI) , data can be transferred between different services

System DeveloperUserIntegration

Implement a mHealth APP for Patients with SCI.

Purpose

IssueIssue Collect data from different services

System DeveloperUserIntegration

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

Objective

Solution

Add "Recommended Interface” to the current architecture to get feedback. (A/B testing)

System DeveloperUserIntegration

Implement a mHealth APP for Patients with SCI.

Purpose

System rebuilding is usually inefficient and time-consuming.

Issue

Methodology

Collection

Analysis

Technique ServerLevel

Client Level

The actual usage can not be completely recorded.

Server LogAccess easily to information, but can not collect JavaScript event. (Srivastava, 2000.)

MethodologyTechnique 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

Methodology

Technique ServerLevel

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.

MethodologyTechniqu

e

xAPI

Client Level

Through Escrow Services to avoid incompatibility problem with the grammar migration.

Intermediary Server-

SidexAPI

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 descriptio

n

Parameters for different situations can be recorded as context data.

Collection

Analysis

Data integrity

Methodology

Context-Awarene

ss

Collect user behavior through xAPIDefinitio

n

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

Usercontext

Physical context

Timecontext

i.e. network connectivity

i.e. time of a day, week

i.e. lighting, noise level

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-Awarene

ss

Collect user behavior through xAPICollection

Analysis

Association Rule

Clustering

Classification

System DeveloperUserIntegration

System Architecture

Filter Model

Context-Awareness

Model

Behavior Model

Expert Knowledge

Cost-Benefit Analysis

iCASE

xAPI

Therapist

SystemDeveloperRecommended

Interface

iCASE systemData Flow

Filter Model

Context-Awareness

Model

Expert Knowledg

e

Cost-Benefit Analysis

iCASE

xAPI

Therapist

SystemDeveloperRecommende

d Interface

iCASE systemData Flow

System BlocksContext-Awareness Model

xAPI

Behavior ModelxAPI

format Mapping

Interface Segmentation

Block Naming

Behavior Library(LRS)

Behavior Model

Context-Awareness Model

xAPI

Behavior ModelxAPI

format Mapping

Block Naming

Behavior Library(LRS)

Behavior Model: Collecting User Data through xAPI

VIPS algorithm (Microsoft, 2003.)

Interface Segmentation

Context-Awareness Model

xAPI

Behavior ModelxAPI

format Mapping

Interface Segmentation

Behavior Library(LRS)

Behavior Model: Collecting User Data through xAPI

DiscomfortRecord

ActivityRecord

RouteRecord

Exercise

ExerciseTime

Block Naming

Context-Awareness Model

xAPI

Behavior Model

Interface Segmentation

Block Naming

Behavior Library(LRS)

Behavior Model: Collecting User Data through xAPI

xAPI Verb xAPI Object

viewedexperiencedmodifiedrecorded

Discomfort RecordActivity RecordExercise…

xAPI format

Mapping

DiscomfortRecord

ActivityRecord

RouteRecord

Exercise

ExerciseTime

xAPI Verb xAPI Object

viewedexperiencedmodifiedrecorded

Discomfort RecordActivity RecordExercise…

Context-Awareness Model

Behavior ModelxAPI

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

Filter Model Expert Knowledge

Cost-Benefit Analysis

iCASE

xAPI

Therapist

SystemDeveloperRecommended

Interface

iCASE systemData Flow

Behavior Model

Context-Awareness ModelFilter Model

Behavior Model

Context Analysis

TimeContext

UserContext ……

Context Definition

Context-Awareness Model

Context-Awareness

Model

Behavior Model

Context-Awareness

Model

Expert Knowledge

Cost-Benefit Analysis

iCASE

xAPI

Therapist

SystemDeveloperRecommende

d Interface

iCASE systemData Flow

Filter Model

Sort Results Rule filtering engine

Filter Model

Context-Awareness

Model Behavior Model

Filter Model

Behavior Model

xAPIRecommended

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

# 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 ModelAdjust the weight to improve the bad habits.

Time

Frequency

Therapist prescription(Threshold)

Forcibly removed

Time

FrequencyTherapist prescription

(Upper limit)Forcibly removed

Therapist prescription(Lower limit)

1) User habits2) Increase the weight

Reduce the weight

Expert Knowledge Therapist

Behavior Model

xAPI

Sort Results Rule filtering engine

Filter Model

Context-Awareness Model

L0.5L1

L0Recommend

L2

L3

Login

function1

Index

function1.1

function1.3

function2

function3

function2.1function2.2

function3.1function3.2function3.3

function1.2

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

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.

Serve Humanity ASAPXAPI 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.

Related Works

LRS

Behavior Model

PrescriptionLearning

Plan

iCASE brainxAPI Applicatio

ns

Human-in-the-loop Machine Learning : machine is human’s collaborator.

Food Control for Cancer PatientsTraining and Learning

Dataviz as a Cognitive Agent

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

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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Instructor and machine as collaboratorsto help learner navigate through learning journey, but shall give them agency

TakeawaysXAPI 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

[email protected]

Citationjia-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.