Project

# Title Team Members TA Documents Sponsor
24 Machine Learning Enabled Wearable Stethoscope
Erlis Kllogjri
Natalia Migdal
Samuel Felder
Hershel Rege design_review
final_paper
final_paper
other
proposal
There have been many recent advances around using several machine learning methods to detect and identify abnormalities in heart beat and lung breath audio. We are proposing a wearable system (to be worn around the chest) which will record audio, and analyze it, looking for abnormalities in the heart beats or lung sounds. This would provide a significant improvement in care for people at risk of issues with heart or lungs because it would be equivalent to having the attention of a doctor at all times. Use cases include examples such as firefighters (who have high rates of heart defects, and are also at risk of smoke inhalation on the job), hospital patients coming out of heart or lung surgery, or people who have history of issues with heart or lungs.

The sensor would comprise of a sensitive microphone (and associated DSP circuitry), which would pick up the sounds from the heart or lungs. This would then get sent to the next sub-unit, which would process the audio. Depending on the complexity of either implementation (and the time and resource limitations) we could either use a microprocessor to implement a k-NN or a CNN algorithm to identify the sounds, or process through a hardware implementation of the k-NN or CNN.

Once a worrisome sound is identified, it is communicated to the relevant party. In the case of hospital patients, it would be communicated to the doctor and nurses station. In the case of at home care, it would be communicated to doctor and emergency services. Finally in the case of emergency personnel (firefighters) it would be communicated to the captain and other emergency personnel. The communication would be implemented through RF. This has many advantages; it can be integrated with existing medical pager system, and since it only communicates when there is an issue it does not always need to be activated (and in the use cases it would only need to activate a handful of times), saving in power requirements.

Low Cost Myoelectric Prosthetic Hand

Michael Fatina, Jonathan Pan-Doh, Edward Wu

Low Cost Myoelectric Prosthetic Hand

Featured Project

According to the WHO, 80% of amputees are in developing nations, and less than 3% of that 80% have access to rehabilitative care. In a study by Heidi Witteveen, “the lack of sensory feedback was indicated as one of the major factors of prosthesis abandonment.” A low cost myoelectric prosthetic hand interfaced with a sensory substitution system returns functionality, increases the availability to amputees, and provides users with sensory feedback.

We will work with Aadeel Akhtar to develop a new iteration of his open source, low cost, myoelectric prosthetic hand. The current revision uses eight EMG channels, with sensors placed on the residual limb. A microcontroller communicates with an ADC, runs a classifier to determine the user’s type of grip, and controls motors in the hand achieving desired grips at predetermined velocities.

As requested by Aadeel, the socket and hand will operate independently using separate microcontrollers and interface with each other, providing modularity and customizability. The microcontroller in the socket will interface with the ADC and run the grip classifier, which will be expanded so finger velocities correspond to the amplitude of the user’s muscle activity. The hand microcontroller controls the motors and receives grip and velocity commands. Contact reflexes will be added via pressure sensors in fingertips, adjusting grip strength and velocity. The hand microcontroller will interface with existing sensory substitution systems using the pressure sensors. A PCB with a custom motor controller will fit inside the palm of the hand, and interface with the hand microcontroller.

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