Project

# Title Team Members TA Documents Sponsor
35 Acoustic Motion Tracking
Hojin Chun
Sean Nachnani
Yuchen He TA design_review
final_paper
other
proposal
Group Members:
Sean Nachnani (nachnan2)
Kevin Chun (hchun8)

General Description:
The project idea is to use sound rather than video as a means of motion recognition. Current smart devices are limited to only using natural language processing to interpret a user's needs. We want to expand upon this further and allow devices to perform commands using simple gestures.
The current idea is to create a 4-input microphone array with an ADC that allows for at least a 48khz sample rate, and use a speaker that can reproduce sounds up to at least 24khz. We will start off by sending pseudo-random pulses across a large bandwidth and correlating the sent signal with the received input from the microphones. Given time we will switch to using FMCW (Frequency Modulated Continuous Waveform) radar as a basis for this approach. This will allow us to achieve accurate distance and velocity measurements, and potentially transmit in the inaudible range.
I have spent the last month prototyping this device using a raspberry Pi and a speaker array. I've gotten the pseudo random pulse approach to work, coding all the signal processing in Python, mainly with the PyAudio and SciPy libraries. The prototype's speaker array is currently sampling at 44.1khz and using a speaker that can play up to 20khz. I was able to achieve accurate measurements within the range of a normal living room (about the size of a smaller classroom in eceb).
We plan on building the microphone array using 4 MEMS microphones and appropriate ADCs to sample up to 48khz. This will allow us to play sounds up to 24khz, which will give us enough bandwidth to get accurate measurements. We'll also use a micro controller (most likely a raspberry pi) to sample from these microphones and perform the DSP needed. This system will be designed to be plugged into a regular power outlet.

Related Research Papers:
CAT: High-Precision Acoustic Motion Tracking http://www.cs.utexas.edu/~wmao/resources/papers/cat.pdf
FingerIO: Using Active Sonar for Fine-Grained Finger Tracking https://fingerio.cs.washington.edu/fingerio.pdf

Recovery-Monitoring Knee Brace

Dong Hyun Lee, Jong Yoon Lee, Dennis Ryu

Featured Project

Problem:

Thanks to modern technology, it is easy to encounter a wide variety of wearable fitness devices such as Fitbit and Apple Watch in the market. Such devices are designed for average consumers who wish to track their lifestyle by counting steps or measuring heartbeats. However, it is rare to find a product for the actual patients who require both the real-time monitoring of a wearable device and the hard protection of a brace.

Personally, one of our teammates ruptured his front knee ACL and received reconstruction surgery a few years ago. After ACL surgery, it is common to wear a knee brace for about two to three months for protection from outside impacts, fast recovery, and restriction of movement. For a patient who is situated in rehabilitation after surgery, knee protection is an imperative recovery stage, but is often overlooked. One cannot deny that such a brace is also cumbersome to put on in the first place.

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Solution:

Our group aims to make a wearable device for people who require a knee brace by adding a health monitoring system onto an existing knee brace. The fundamental purpose is to protect the knee, but by adding a monitoring system we want to provide data and a platform for both doctor and patients so they can easily check the current status/progress of the injury.

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Audience:

1) Average person with leg problems

2) Athletes with leg injuries

3) Elderly people with discomforts

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Equipment:

Temperature sensors : perhaps in the form of electrodes, they will be used to measure the temperature of the swelling of the knee, which will indicate if recovery is going smoothly.

Pressure sensors : they will be calibrated such that a certain threshold of force must be applied by the brace to the leg. A snug fit is required for the brace to fulfill its job.

EMG circuit : we plan on constructing an EMG circuit based on op-amps, resistors, and capacitors. This will be the circuit that is intended for doctors, as it will detect muscle movement.

Development board: our main board will transmit the data from each of the sensors to a mobile interface via. Bluetooth. The user will be notified when the pressure sensors are not tight enough. For our purposes, the battery on the development will suffice, and we will not need additional dry cells.

The data will be transmitted to a mobile system, where it would also remind the user to wear the brace if taken off. To make sure the brace has a secure enough fit, pressure sensors will be calibrated to determine accordingly. We want to emphasize the hardware circuits that will be supplemented onto the leg brace.

We want to emphasize on the hardware circuit portion this brace contains. We have tested the temperature and pressure resistors on a breadboard by soldering them to resistors, and confirmed they work as intended by checking with a multimeter.

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