# Title Team Members TA Documents Sponsor
10 Assistive Technology for Patients with Medical Face Blindness
Jeffrey Luan
Timothy Chia
Yuchen He TA final_paper
Prosopagnosia is a neurological condition characterized by the inability to recognize faces. After talking a few times with an ECE professor that has this condition, I'd like to work on developing a prototype for a minimally intrusive assist technology designed to help normalize social interactions. The basic idea is to create one piece of wearable tech that captures image data to be sent to a smartphone. The phone can handle facial recognition from a user managed database, and provide the needed information to the user. For example, an ear mounted camera with a subtle activation button might send a photo to the phone, which will identify the largest face in the image and transmit the information to the user through a second piece of wearable tech.

Building on projects FA15_30 and FA12_17, the second wearable is a wristband with a screen, with their WiFi replaced by bluetooth if we keep the phone app, the button I/O for camera activation. Battery and charging for both the camera and wristband prototypes as well. In addition, the wristband buzz once if the face is not in the database, so the user can immediately transition into introductions. A different buzz pattern would indicate that the face was identified, and the information transmitted to the screen.

On the software side, we weren't too interested in duplicating the work already done by so many other labs, and were hoping to just use API calls to any of these taking advantage of cellular networks and the cloud. This would then free us up to do the more interesting software work of creating the UI to manage the database, and give us time to work on more interesting hardware (like the second piece of wearable tech) which could be more important for this class.

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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