Rozpoznawanie gestów z wykorzystaniem czujników inercyjnych o 9 stopniach swobody

• Autorzy: Barczewska K. , Drozd A. , Folwarczny Ł.

Warianty tytułu

EN

Gesture recognition based on 9DOF inertial sensor

• Języki publikacji: PL

Abstrakty

PL

Rozpoznawanie gestów za pomocą czujników inercyjnych może być alternatywą dla standardowych interfejsów człowiek-komputer. Do śledzenia gestów wykorzystano czujnik zawierający trójosiowy akcelerometr, magnetometr i żyroskop. W dotychczasowych badaniach bazowano na sygnałach przyspieszenia. Autorzy zaproponowali i porównali rozwiązania wykorzystujące zarówno analizę przyspieszenia, jak i orientacji w przestrzeni, a także umożliwili badanym osobom wykonywanie gestów w sposób naturalny. Wyniki pokazują, że za pomocą algorytmu DTW (Dynamic Time Warping) możliwa jest klasyfikacja indywidualna dla danej osoby (ze skutecznością 92%), a także klasyfikacja uogólniona - na podstawie uniwersalnego wzorca (ze skutecznością 83%).

EN

Gesture recognition may be applied to control of computer applica-tions and electronic devices as an alternative to standard human-machine interfaces. This paper reports a method of gesture classification based on analysis of data from 9DOF inertial sensor - NEC-TOKIN, Motion Sensor MDP-A3U9S (Fig.1). Nine volunteers were asked to perform 10 different gestures (shown in Fig.2) in a natural way with a sensor attached to their hand. The gesture data base consisting of 2160 files with triaxial acceleration and orientation signals was created. In the first step the data were divided into training and testing sets. The designed system uses the Dynamic Time Warping (DTW) algorithm to calculate similarity of signals (formulas (1)-(3)). Using this method the authors chose representative signals to indi-vidual and generalized exemplars data base from the training set. The DTW algorithm was also used in the classification process. Different recognition approaches were tested basing on acceleration-only, orientation-only and acceleration-orientation signals. The results listed in Tab.4 show that the best recognition efficiency of 92% was obtained in the individual recognition (only one person gestures taken into account) for modified exemplars data base. The modification proposed by the authors (Section 3) improved the recognition rate by 10 percentage points. The efficiency rate of 83% (Tab. 5) was reached in the generalized case. The next step of im-proving the designed recognition system is application of an inertial system with a bluetooth module and real-time gesture classification.

Słowa kluczowe

PL

rozpoznawanie gestów; czujnik inercyjny; DTW

EN

gesture recognition; inertial sensor; DTW

• Wydawca: Wydawnictwo PAK
• Czasopismo: Pomiary Automatyka Kontrola
• Rocznik: 2013
• Tom: R. 59, nr 3
• Strony: 235--238
• Opis fizyczny: Bibliogr. 17 poz., rys., schem., tab., wzory

Twórcy

autor

Barczewska K.

• AGH Akademia Górniczo-Hutnicza, WEAIiIB, Katedra Automatyki i Inżynierii Biomedycznej, Al. Mickiewicza 30 Kraków

autor

Drozd A.

• AGH Akademia Górniczo-Hutnicza, WEAIiIB, Katedra Metrologii i Elektroniki, Al. Mickiewicza 30, Kraków

autor

Folwarczny Ł.

• SILVERMEDIA Sp. z o. o., ul. Wadowicka 6, Kraków

Bibliografia

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