"We're interested in making accessible assistive technology with 3D printing, but we have no easy way to know how people are using it," said Professor Jennifer Mankoff of UW. "Could we come up with a circuitless solution that could be printed on consumer-grade, off-the-shelf printers and allow the device itself to collect information? That's what we showed was possible in this paper."
Previously, the team developed the first 3D printed objects that connect to Wi-Fi without electronics. These purely plastic devices can measure if a detergent bottle is running low and then automatically order more online.
"Using plastic for these applications means you don't have to worry about batteries running out or your device getting wet. That can transform the way we think of computing," said Associate ProfessorShyam Gollakota of UW. "But if we really want to transform 3D printed objects into smart objects, we need mechanisms to monitor and store data."
The researchers tackled the monitoring problem first. In their previous study, their system tracks movement in one direction, which works well for monitoring laundry detergent levels or measuring wind or water speed. But now they needed to make objects that could monitor bidirectional motion like the opening and closing of a pill bottle.
"Last time, we had a gear that turned in one direction. As liquid flowed through the gear, it would push a switch down to contact the antenna," said lead author Vikram Iyer. "This time we have two antennas, one on top and one on bottom, that can be contacted by a switch attached to a gear. So opening a pill bottle cap moves the gear in one direction, which pushes the switch to contact one of the two antennas. And then closing the pill bottle cap turns the gear in the opposite direction, and the switch hits the other antenna."
Both of the antennas are identical, so the team had to devise a way to decode which direction the cap was moving.
"The gear's teeth have a specific sequencing that encodes a message. It's like Morse code," said co-author Justin Chan. "So when you turn the cap in one direction, you see the message going forward. But when you turn the cap in the other direction, you get a reverse message."
This same method can be used to monitor how people use prosthetics, such as 3D printed e-NABLE arms, the researchers say. The team 3D printed an e-NABLE arm with a prototype of their bidirectional sensor that monitors the hand opening and closing by determining the angle of the wrist. When the user flexes their wrists, cables on the hand tighten to make the fingers close.
The researchers also wanted to create a 3D printed object that could store its usage information while out of Wi-Fi range. For this application, they chose an insulin pen that could monitor its use and then signal when it was getting low.
"You can still take insulin even if you don't have a Wi-Fi connection," Prof. Gollakota said. "So we needed a mechanism that stores how many times you used it. Once you're back in the range, you can upload that stored data into the cloud."
This method requires a mechanical motion, like the pressing of a button, and stores that information by rolling up a spring inside a ratchet that can only move in one direction. Each time someone pushes the button, the spring gets tighter. It can't unwind until the user releases the ratchet, hopefully when in range of the backscatter sensor. Then, as the spring unwinds, it moves a gear that triggers a switch to contact an antenna repeatedly as the gear turns. Each contact is counted to determine how many times the user pressed the button.
These devices are only prototypes to show that it is possible for 3D printed materials to sense bidirectional movement and store data. The next challenge will be to take these concepts and shrink them so that they can be embedded in real pill bottles, prosthetics or insulin pens.
"This system will give us a higher-fidelity picture of what is going on," said Prof. Mankoff. "For example, right now we don't have a way of tracking if and how people are using e-NABLE hands. Ultimately, what I'd like to do with these data is predict whether or not people are going to abandon a device based on how they're using it.”
