Differentiation and classification of different materials on a touch-screen using a piezoelectric contact microphone
(2016) EEM820 20161Department of Biomedical Engineering
- Abstract
- In this report, a potential improvement of an optical touch screen solution by
means of acoustic sensing is presented. The goal is to utilize the different vibration
patterns that arise when different materials touch the screen to be able to identify
them. This identification of different materials opens up new opportunities to give
input to the touch-device which could potentially replace menu bars or touch-andhold
interaction that exists today. The ability to discriminate between object also
gives the opportunity for different objects to represent different things, such as
colors, tools or unique users. A piezoelectric accelerometer is used to collect the
sound signals, which are then classified using the K-Nearest-Neighbour... (More) - In this report, a potential improvement of an optical touch screen solution by
means of acoustic sensing is presented. The goal is to utilize the different vibration
patterns that arise when different materials touch the screen to be able to identify
them. This identification of different materials opens up new opportunities to give
input to the touch-device which could potentially replace menu bars or touch-andhold
interaction that exists today. The ability to discriminate between object also
gives the opportunity for different objects to represent different things, such as
colors, tools or unique users. A piezoelectric accelerometer is used to collect the
sound signals, which are then classified using the K-Nearest-Neighbour method
to identify which material that touched the screen. The result shows that the
classification for three different objects was done with a 96% correct classification.
If only two different pens are used, the correct classification goes above 99%.
The report further describes some of the key factors that needs to be taken in
account when developing such an acoustic sensing system, such as force, distance
and location of the taps on the screen. Those factors does all seem to alter the
frequency spectrum in some way. (Less) - Popular Abstract (Swedish)
- Klassificering av pennor av olika material till touch-skärm
Genom att koppla en kontaktmikrofon till en touchskärm och undersöka de vibrationsmönster som uppstår då olika objekt rör vid skärmen har en dator tränats till att känna igen olika typer av material. Om en filt och en träpenna användes kunde datorn känna skillnad på vilket material som användes i mer än 99,5% av fallen. Resultatet av projektet innebär att det finns möjlighet för pennor gjorda av olika material att nu representera specifika användare, färger eller verktyg i de program som körs på enheter med touchskärm genom att koppla en kontaktmikrofon till skärmen.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8889313
- author
- Björn, Anders LU
- supervisor
- organization
- course
- EEM820 20161
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- additional info
- 2016-15
- id
- 8889313
- date added to LUP
- 2016-09-12 11:41:47
- date last changed
- 2016-09-12 11:53:05
@misc{8889313, abstract = {{In this report, a potential improvement of an optical touch screen solution by means of acoustic sensing is presented. The goal is to utilize the different vibration patterns that arise when different materials touch the screen to be able to identify them. This identification of different materials opens up new opportunities to give input to the touch-device which could potentially replace menu bars or touch-andhold interaction that exists today. The ability to discriminate between object also gives the opportunity for different objects to represent different things, such as colors, tools or unique users. A piezoelectric accelerometer is used to collect the sound signals, which are then classified using the K-Nearest-Neighbour method to identify which material that touched the screen. The result shows that the classification for three different objects was done with a 96% correct classification. If only two different pens are used, the correct classification goes above 99%. The report further describes some of the key factors that needs to be taken in account when developing such an acoustic sensing system, such as force, distance and location of the taps on the screen. Those factors does all seem to alter the frequency spectrum in some way.}}, author = {{Björn, Anders}}, language = {{eng}}, note = {{Student Paper}}, title = {{Differentiation and classification of different materials on a touch-screen using a piezoelectric contact microphone}}, year = {{2016}}, }