Eyelid Gesture Control using Wearable Tunnelling Magnetoresistance Sensors
| Autor: | Asfand Tanwear, Ricardo Ferreira, Tim Böhnert, Elvira Paz, Hadi Heidari |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Magnetoresistance
Noise (signal processing) business.industry Computer science 020208 electrical & electronic engineering Electrical engineering 02 engineering and technology Signal law.invention Magnetic circuit Bluetooth 03 medical and health sciences Microcontroller 0302 clinical medicine Gesture recognition law 0202 electrical engineering electronic engineering information engineering business Sensitivity (electronics) 030217 neurology & neurosurgery |
| Zdroj: | ICECS |
| DOI: | 10.1109/icecs49266.2020.9294878 |
| Popis: | Everyday technologies are more than ever digitized with the internet of thing's systems and disabled individuals may feel excluded. Handsfree gesture approaches such as eye movements/blinking can enhance interacting with modern technology. This work presents eye blinking for eyelid gesture control using a wearable magnetic system consisting of a flexible magnetic strip on the eyelid and spintronic magnetic sensors with its analogue front-end circuit. To detect eye blinking, tunnelling magnetoresistance (TMR) sensors with a sensitivity of 11mV/V/Oe are embedded into an eyeglass frame. For successful detection of the small magnetic field generated by 6 mm diameter with 1 mm thickness magnetic strip on the eyelid, a sensor readout circuit is designed to amplify the collected signal and cancel the external noise and offset. The circuit is capable of filtering low frequencies 28 Hz are filtered for both magnetic field and eyelid movement noise. Each TMR sensor circuit is equipped with a fixed-gain amplifier for detecting low-magnetic field from the mm-sized magnetic strips. The blinks can be repeated within a set time frame and since both eyelids will be detected, multiple command combinations are possible for classification. Based on magnetic field simulation results, the circuit was simulated and has shown high repeatability and stability that can classify eyeblinks based on an amplitude threshold. As a result, the signal can be scaled and classified on a Bluetooth microcontroller capable of connecting to various Bluetooth enabled devices for disabled individuals to communicate with external technology. |
| Databáze: | OpenAIRE |
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