It's an Internet-Connected Wheelchair (Video)

Video It's an Internet-Connected Wheelchair (Video) 22

Posted by Roblimo on Wednesday October 08, 2014 @04:46PM from the the-internet-of-things-keeps-rolling-along dept.

Video no longer available.

If you're in a wheelchair, wouldn't it be nice to have your chair automatically alert a caregiver if changes in your heart rate or another vital sign showed that you might be having a problem? And how about helping you rate sidewalks and handicapped parking spaces to help fellow wheelchair users get around more comfortably? Steven Hawking endorses the idea, and the Connected Wheelchair Project, in this short video. (You can see our interviewee, David Hughes, at 0:58 and again at 1:38.) This is an Intel project, in conjunction with Wake Forest University, run by student interns. | Besides helping wheelchair-dependent people live a better life, the Connected Wheelchair Project may help prevent Medicare fraud, says Hughes in our video interview with him. Falsified requests for durable medical goods are a huge drain on Medicare's budget. What if a connected wheelchair spent all of its time far from the home of the person to whom it was assigned? That would be a red flag, and investigators could follow up to see if that wheelchair was in legitimate hands or was part of a scam. | The Connected Wheelchair is still proof-of-concept, not a commercial product. Will it see production? Hard to say. This may never be a profitable product, but Intel CEO Brian Krzanich has said that that this project is an example of how “the Internet of Things can help change lives.” (Alternate Video Link)

Tim: David, what is this chair that is next to you, could you explain this a little bit?

David: Yes, this is my intern project over the summer that I worked on, with a group of five other interns. In six weeks we went from a conception of what a connected wheelchair should be, all the way to this prototype you see here today.

Tim: It's aconnected wheelchair.

David: Yeah.

Tim: That could mean a lot of things. Explain what kind of connections you are talking about?

David: Yeah, so what we’re trying to do is get aggregate information out from a wheelchair user themselves through what they experience in their everyday life. So we gather information in three ways here, in three kind of what we call pillars of data. The first one is location data of the wheelchair user. So what we did is we created this Android app for the user to actually – to constantly track their location and then also give feedback to cities about good and bad accessibility points. So as you can here, you just can press whether you want to mark this geographic location as a good curb or bad curb. The idea is to make cities smarter by actually giving that information back to city so they can get feedback on what they’re doing. The other thing is sending the users location a given point in time back into the cloud.

The second piece of data is biometric. Biometric data is collected through this BioHarness as you see right here which streams data from a Bluetooth connection to the Android application. Here the user is able to monitor their heart rate, their posture, their respiration rate and then their skin temperature as well. Again, this information is sent back up to the cloud for monitoring and it is shared with their caretakers and healthcare providers. The last is wheelchair data itself. So this device right here is an Intel Moon Island kit. It also works in Intel Galileo. We have them working on both. These are IOT enabled devices that are designed to just stream data as a gateway up to the cloud. So we've outfitted this one with a board that will actually aggregate data from the wheelchair batteries, both the voltage and current from the batteries as well as it has an accelerometer on there as well. The accelerometer is there so we can always know at any given point in time in any given geographical location the orientation of the wheelchair.

Tim: David, you mentioned the idea of a curb being good or bad as one of the data points to take away. What makes a curb good or bad?

David: Exactly, so what makes a curb good or bad is how steep it is, so one thing that was shocking to us that we found out is that curbs, particularly those mandated by the ADA, those within like federal regulation, some of them can be too steep for wheelchairs to get up, so this wheelchair for example can get up some of these curbs but not back down, or vice versa. One of the things that’s very shocking is that, if a curb is too steep what it’ll do is throw the user forward and across their knees. If no one is around, there is a possibility they could die and just suffocate because they are not able to push themselves back over and open up the lungs again.

Tim: The orientation of the wheelchair itself, that’s one of the things you track?

David: Exactly, yes.

Tim: And how about emergency messaging?

David: Can you say that again?

Tim: How about emergency messaging?

David: So, emergency messaging, one of the end goals of this wheelchair project is actually to do real time monitoring of patients' data, to understand whenever, for lack of a better term, bad things occur. Those can be things like a wheelchair user being thrown over their knees and not being able to move back because they are quadriplegic or the wheelchair user being thrown out of their chair or their chair is being thrown around, really special cases where we will detect that the chair is running but for whatever reason the person is not in the chair. So we’re trying to use this information to actually monitor that and then develop models so we can actually understand what that looks like from a data perspective and then tell the user whenever we think something interesting or unfortunate is going on.

Tim: David, you’re gathering a lot of data here and you’re trying to visualize it, what kind of changes by the data you’re gathering end up suggesting to cities or to wheelchair makers or others. What are some examples where data visualization is helpful?

David: Yeah, absolutely, so the data visualization can impact people on a number of levels. So at the very top level for wheelchair makers, we can provide information by how the wheelchair is being used in everyday life, back to them so they can kind of make – they can make the designs better and more effective. Second, for people actually giving out wheelchairs, someone like Medicare, we can help reduce frauds. So Medicare has $100 million every single year, it’s allowed to use to give out power wheelchairs. Now unfortunately a lot of that money actually goes into fraud, and actually fraudulent efforts. What happens is, people who don’t need wheelchairs will actually request a wheelchair and qualify for one, get one and then sell it off. These things are – starting price is about $25,000 so it’s quite lucrative, it’s actually quite a big problem that people will actually sell off these chairs. So what we’re trying to do is actually show who is using a chair and how they are using it and providing that back to Medicare, so they can better suit their choices on who gets chairs.

Tim: How do you that specifically, what kind of data gives that sort of insight?

David: Yeah, absolutely. So, we’re able to give a timestamp and location with how the chair is actually being used, so we’re actually able to understand that, well you have no battery use in three weeks, so probably you’re not actually sitting in chair and using it, even though we just gave it you fresh out of the gate. So that’s just something that we’ve kept in mind. The last is actually leveraging the users on personal information and kind of very much the quantified self movement to actually understand how their body works and how they interact with their chair, what we found is that the wheelchair users tend to be really left – tend to be greatly left out of the quantified self movement and this is actually a device that very much can help them understand the unique aspects of their lives.

Tim: How fast and how far does this wheelchair go?

David: How far I don’t know. I know we can get this guy up to about 10 miles an hour. They are pretty zippy, it’s a pretty good jog for kind of the rest of us. And far, it really depends on how fast and how long you’re going to be going for.

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