Razpoznavanje vrste aktivnosti uporabnika in prevoznega sredstva s pomočjo vgrajenih senzorjev mobilne naprave

Slamek, Rok

| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

First page > Show document

Show document

Title:	Razpoznavanje vrste aktivnosti uporabnika in prevoznega sredstva s pomočjo vgrajenih senzorjev mobilne naprave
Authors:	ID Slamek, Rok (Author) ID Potočnik, Božidar (Mentor) More about this mentor...
Files:	MAG_Slamek_Rok_2018.pdf (4,62 MB) MD5: 1B9F336569188A67D055BB8F462A7D32 PID: 20.500.12556/dkum/ee481f3f-150f-4dee-91b7-851ab5830376
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Cilj tega zaključnega dela je izboljšati detekcijo vrst aktivnosti uporabnika in prevoznih sredstev v mobilni aplikaciji MobilitApp. Z uporabo znanih tehnik obdelave signalov in razpoznavanja vzorcev smo razvili dve metodi za detekcijo aktivnosti uporabnika. Prepoznavali smo naslednje aktivnosti: mirovanje, hoja, tek, vožnja s kolesom, motorjem, avtom, avtobusom, metrojem, vlakom in tramvajem. Zgolj z uporabo vgrajenih senzorjev mobilne naprave smo v prvem pristopu zgradili vektor značilnic, ki smo ga uporabili za učenje odločitvenih dreves. V drugem pristopu smo na vhod nevronskih mrež poslali neobdelane podatke iz pospeškometra. Zajemanje podatkov smo zabeležili z video kamero z namenom natančnejše izločitve posameznih delov aktivnosti iz signala. Izvedli smo še študijo, če je kontrolirano zajemanje podatkov z video kamero dejansko potrebno. Naše pristope smo validirali na manjši lastni podatkovni zbirki, ki obsega okrog sedem ur aktivnosti. Podatke smo zajeli s pomočjo treh prostovoljcev in štirih različnih mobilnih naprav. Rezultate smo ovrednotili z različnimi metrikami, s poudarkom na metriki »F1 score«. S pristopom Random Forest je bila metrika »F1 score« 94 %, kar je bolje kot uspešnost obstoječih rešitev iz literature. Naš pristop na osnovi nevronskih mrež se je izkazal nekoliko slabše (»F1 score« je bil 91 %), a še vedno bolje kot najsodobnejše metode iz literature.
Keywords:	obdelava signalov, razpoznavanje vzorcev, aktivnost uporabnika, prevozno sredstvo
Place of publishing:	[Maribor
Publisher:	R. Slamek
Year of publishing:	2018
PID:	20.500.12556/DKUM-72257
UDC:	004.93(043.2)
COBISS.SI-ID:	21873686
NUK URN:	URN:SI:UM:DK:R7SJXHEU
Publication date in DKUM:	08.11.2018
Views:	1146
Downloads:	113
Metadata:
Categories:	KTFMB - FERI
:	Copy citation

Average score:	(0 votes)
Your score:	Voting is allowed only for logged in users.
Share:

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:	17.09.2018

Secondary language

Language:	English
Title:	User activity and transportation mode recognition using embedded mobile device sensors
Abstract:	This study aims to improve the recognition of user activity type and transportation mode in the mobile application MobilitApp. By using known signal processing and pattern recognition techniques, we developed two methods to recognize user activities. We were recognizing the following activities: stationary, walking, running, driving on a bicycle, a motorcycle, a car, taking the bus, the metro, the train and the tram. By using embedded mobile device sensors only in the first approach, we built a feature vector that we used to train decision trees. In the second approach, we provided raw data from the accelerometer to the input of a neural network. Data capture was recorded with a video camera in order to precisely identify the individual parts of the activity in the signal. We also did a study to ascertain whether a controlled capture of data with a video camera was, in fact, necessary. We validated our approaches on a smaller database of our own, which covers about seven hours of activity. The data was captured with the help of three volunteers and four different mobile devices. The results were evaluated with different metrics, with an emphasis on the 'F1 score' metric. With the Random Forest approach, the 'F1 score' metric was 94%, which is better than the success of existing 'state-of-the-art' solutions. The performance of our approach based on neural networks was slightly lower ('F1 score' was 91%), but still better than most of the recent methods in the literature.
Keywords:	signal processing, pattern recognition, user acitivity, transporation mode

Comments

Leave comment

You must log in to leave a comment.

Comments (0)

0 - 0 / 0

There are no comments!

Back