Project

# Title Team Members TA Documents Sponsor
7 Adaptive Light-Filtering Glasses
Jahnavi Thejo Prakash
Kewal Ghosalkar
# Team Members:
- Kewal Ghosalkar (kewalkg2)
- Jahnavi Thejo Prakash (jahnavi7)

# Problem

People around the world struggle with photosensitive epilepsy, migraines, TBI, and other such photosensitive medical conditions that increase sensitivity to light. Intense flickering lights can worsen sensitivity and in some cases also lead to convulsions and seizures. Current solutions include remedies like avoiding triggers, or using tinted glasses. In unfamiliar situations, these passive solutions can prove to be unreliable since they cannot update dynamically.

# Solution

We propose adaptive smart glasses that can detect lighting conditions that may trigger such photosensitive conditions and adapt the tint on the lenses accordingly. The glasses can also provide warnings and possibly contain a system to detect seizures.

# Solution Components
## Optical Attenuation Subsystem
- LCD light valve attached to the lenses of the glasses. These can darken proportionally to the perceived level of risk.

## Sensor Subsystem
- Set of 3 photodiodes for detecting the intensity of incoming light.
- Wide FOV photodiode placed in the front for measuring ambient light brightness
- Narrow FOV photodiode placed in front to measure light incident on the users eyes
- Feedback photodiode placed behind the lenses to measure the actual light exposure on the user's eye. This provides feedback for a closed feedback loop to make adjustments based on the attenuation of the LCD light valve on the lenses.
- Transimpedance amplifiers for each of the 3 photodiodes in the sensor subsystem to utilize the full range of the ADC

## Processing Subsystem
- STM32L series microcontroller, with an internal ADC sampling rate of 1000 Hz, which is large enough to avoid aliasing within the Epilepsy ranges of 3-60Hz
- Signal processing will first estimate and remove average brightness or DC component from each signal in the amplifier results. A band-pass filter will remove slower flicker changes caused by movements and noise. An FFT & modulation depth measurements will determine the frequency and strength of the dominating flicker.
- Similarity flicker frequency checks will run for the two forward facing photodiodes over several sampling windows. If risk is detected, the lens will become more attenuated and adjustments will be made based off of the flicker magnitude on the behind-lens sensor with a close loop feedback system.

## Power Subsystem
- Single LiPo to power the entire system.
- Boost converter to step up 3.7V of the LiPo to 5V for the Light valve.
- 3.3V LDO for the STM32, photodiodes and amplifiers.
- USB C charging.
- The Power subsystem will live on a separate hip mounted pack out of concerns for weight, space and safety.

# Criterion For Success
- Our solution can be considered successful if the glasses can:
- Accurately detect epilepsy triggering frequencies around 3 to 60Hz with an accuracy of at least 80%
- Accurately reject non-hazardous changes in light caused my sudden movements or environment changes with a false positive rate of less than 10%
- Assign a replicable risk score to each situation and activate the optical attenuation system within 0.5s of hazardous inputs
- Have a reasonable battery life for daily use of around >8 hours on a single charge.

# Alternatives
A paper titled “EpilepSee” from 2024 attempts to build a device very similar to ours, however there are a few improvements we are trying to make:
- The sampling frequency in the paper was limited to 40Hz which can lead to aliasing at flicker frequencies above 20Hz leading to faults in signal processing. We plan on using a much higher sampling frequency at around 1 kHz to avoid aliasing.
- The paper is measuring light inputs using a single sensor, we want to use multiple sensors to provide more data for signal processing.
- The device in the paper is a prototype and is not very portable. We are attempting to make our design more portable, hence more comfortable for use.
- The paper uses an open loop design. We are going to use a closed loop design which should allow for more precise attenuation
The response time for the device in the paper is 1-1.5s, we plan on having a much faster response time of <0.5s
# Extensions
- The ability for the users to input the exact frequencies they are susceptible to. This can be done through Bluetooth or with an extra serial debugger module to input this data.
- An IMU can monitor for seizure-like body movements and trigger a safety response, alerting the user’s emergency contact.
- A display that provides a warning before any optical attenuation takes place and allows users the option for manual overriding.

# Links

- [EpilepSee Paper](https://ora.ox.ac.uk/objects/uuid%3Aabb3258d-264e-42e0-8967-40408397f96f/files/scj82k980j)
- [Small Liquid Crystal Light Valve – The Pi Hut](https://thepihut.com/products/small-liquid-crystal-light-valve-controllable-shutter-glass)

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Our group proposes to create robot drummer which would respond to human voice "beatboxing" input, via conventional dynamic microphone, and translate the input into the corresponding drum hit performance. For example, if the human user issues a bass-kick voice sound, the robot will recognize it and strike the bass drum; and likewise for the hi-hat/snare and clap. Our design will minimally cover 3 different drum hit types (bass hit, snare hit, clap hit), and respond with minimal latency.

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