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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.
… 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.