Good Vibrations

Inspiration

Wearable vibrotactile devices are becoming increasingly popular for medical interventions. This technology is based on the concept of stochastic resonance, in which low level noise can enhance signal detection—like those signals used for balancing oneself! By using these devices, medical professionals can provide targeted and personalized interventions that can improve patient outcomes. Furthermore, these devices are non-invasive and easy to use, making them ideal for medical applications and for in-home clinical trials.

For our project, we have designed and produced a vibrotactile vest that applies vibrations at varying frequencies at different points around the trunk of the body. By modifying the frequency and statistical structure of these vibrations, different types of intervention can be achieved. It is hoped that this device can not only be used for the advancement of sensorimotor research, but also assist in motor therapies for elderly or movement-impaired individuals. It also has the massive potential to help those beyond our interested population, including vision impaired individuals.

What it does

Our device generates signals using CircuitPython to produce specific vibration patterns. This vibration could be noisy and improve sensation and balance via stochastic resonance as described above. We can also control the vibration motors individually to provide more fine-grained information about the user’s environment. For example, the motor array could vibrate more intensely on the left side, indicating an approaching obstacle from that direction. It is easy to see how this device opens the opportunity to study a number of unique research questions with clinical importance.

More broadly, this device provides a way to potentially increase safety in balance-impaired individuals. Research shows sensory degradation associated with aging, resulting in an increased number of falls in older individuals. Research also suggests that the introduction of auditory or vibratory stochastic resonance can improve sensory detection. Through this, it is hoped that the application of vibratory stimulation can reduce fall risk and balance impairment in these communities. Recurrent elderly fallers are often hospitalized and have much higher mortality rates, hence the importance of developing a non-invasive intervention and research tool, such as ours. This device, in addition to traditional research use, can also be sent home with research participants to see how its regular use alters fall risk.

How we built it

Our device currently makes use of 16 vibrating coin motors, with room for 48, which are connected to PWM motor controllers that are controlled by a Raspberry Pi Pico W. We cut and soldered the wires that connect our circuit. Commands are sent to the Raspberry Pi Pico W using CircuitPython to allow for the activation and control of the individually addressable motors. As a result, different amounts of vibration with varying statistical structure may be applied.

The motors were connected to an orthopedic belt to allow for easy and comfortable application to the torso. The motors were stuck to three strips of Velcro which were sewn onto the orthopedic belt.

Challenges we ran into

The most significant challenge we ran into was the development of the hardware. We initially faced issues of needing extra materials than those that we brought, needing to get extra wire, insulation, converters, and transistors. Further, we faced issues of accuracy in our soldering, wherein we had to redo a significant chunk of the wiring we had done initially. These issues slowed us down pretty harshly.

Due to the hardware delays, we had limited time to test the planned Bluetooth functionality of the device, and had to save that for a future improvement to the device.

Accomplishments that we're proud of

We are very proud of the progress we made on this project. There may be some parts that are not as cleanly developed as they could be (an issue we intend to fix in the future), and we are happy to have developed a functional device, with a 3D virtual demo.

We are also happy that we were able to practice and learn some new functional skills that will aid in the development of other research tools in our respective labs.

What we learned

To conduct this project, we had to learn a significant amount about the theoretical perspectives on noise-based stimulation and the application of noise therapies to different medical conditions. Through this, we were able to gain perspectives on the societal impact of the technologies that we can develop. We were able to consider not only the development of this tech, but also who the tech is there to serve.

From a technical perspective, this project was one of the more ambitious tasks that we have undertaken. As a team of majority Psychology students and one Computer Science student, we were able to learn a lot about electrical engineering to accomplish this project. Specifically, we were able to develop skills in soldering, circuit design, and communication using the I2C protocol.

What's next for Good Vibrations

The Vibrotactile belt that we have developed will be employed in research at the University of Cincinnati Center for Cognition, Action, and Perception. We plan to continue this research line, investigating the potential benefits of vibratory stimulation on the perceptual-motor system, and the potential therapeutic avenues this technology can be applied to. Before implementation, we also plan to increase the number of motors and clean up the design of the device, including 3D printing a storage pouch for the motor controllers and other external materials.

We hope we can continue to develop different wearable technologies, aimed at providing vibratory stimulation in more task-specific areas. For example, generating a vibrotactile glove to enhance sensory function in the upper limbs, or socks to improve function of the lower limbs, etc. By targeting specific areas, we may be able to see more finely-grained benefits, such as improving the manipulation of the hands in Parkinson’s Disease patients – though more research is also necessary for these advancements.

Built With

• c#
• circuitpython
• github
• i2c
• onedrive
• powerpoint
• soldering