Accelerometer-based gesture control for a design environment

Kela, Juha; Korpipää, Panu; Mäntyjärvi, Jani; Kallio, Sanna; Savino, Giuseppe; Jozzo, Luca; Marca, Di

doi:10.1007/s00779-005-0033-8

Cited by 248 publications

(204 citation statements)

References 14 publications

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“…The uWave paper [22] and the gesture control work of Kela et al [23] explore gesture recognition using DTW and HMM algorithms respectively. They both used the same set of eight simple gestures which included up, down, left, right, two opposing direction circles, square, and slanted 90 degree angle movements for their final accuracy reporting.…”

Section: B Gesture Recognitionmentioning

confidence: 99%

Driving style recognition using a smartphone as a sensor platform

Johnson

Trivedi

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

598

310

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Abstract-Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentiallyaggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.

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Section: B Gesture Recognitionmentioning

confidence: 99%

Driving style recognition using a smartphone as a sensor platform

Johnson

Trivedi

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

598

310

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“…One gesture set is based on geometric shapes (Fig. 5, introduced by Kela et al [20]), while the other represents a set of human to human gestures (Fig. 6).…”

Section: Gesture Recognitionmentioning

confidence: 99%

Collocated Multi-user Gestural Interactions with Unmodified Wearable Devices

et al. 2017

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Many real-life scenarios can benefit from both physical proximity and natural gesture interaction. In this paper, we explore shared collocated interactions on unmodified wearable devices. We introduce an interaction technique which enables a small group of people to interact using natural gestures. The proximity of users and devices is detected through acoustic ranging using inaudible signals, while in-air hand gestures are recognized from threeaxis accelerometers. The underlying wireless communication between the devices is handled over Bluetooth for scalability and extensibility. We present (1) an overview of the interaction technique and (2) an extensive evaluation using unmodified, off-the-shelf, mobile, and wearable devices which show the feasibility of the method. Finally, we demonstrate the resulting design space with three examples of multi-user application scenarios.

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“…Forearm gestures are very popular in many application domains and several accelerometer based algorithms exist for their recognition [2][3][4][5]. However, feedback for stroke-like gestures or metaphoric gestures is still a research topic that needs further investigations.…”

Section: Related Workmentioning

confidence: 99%

“…Kela et al [2] presented an accelerometer-based gesture recognizer for metaphoric 3D gestures. The gestures they presented were used to operate a VCR and to operate in the Smart Design Studio where they use gestures such as up, down, left, right, push and pull.…”

Section: Related Workmentioning

confidence: 99%

Improving In-game Gesture Learning with Visual Feedback

Schwaller

Kühni

Angelini

et al. 2014

Human-Computer Interaction. Applications and Services

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Abstract. This paper presents a research work on gesture recognition and feedback to reduce the learning time of new gestures and to augment user performance in a game application. A Wiimote controlled space shooter game, GeStar Wars, has been developed. The player controls a spaceship through the buttons in the controller, while forearm gestures can be used to perform special actions. Gesture strokes are mapped in a 3x3 grid and are differentiated according to the path of the covered grid cells. In-game visual feedback displays to the user the current gesture path and which cells were covered after the gesture is performed. The novelty of this research resides in the correlated gesture recognition methodology and feedback which helps the user to learn and correct the gestures. The evaluation, conducted with 12 users, showed that the users performed significantly better if feedback was provided.

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Accelerometer-based gesture control for a design environment

Cited by 248 publications

References 14 publications

Driving style recognition using a smartphone as a sensor platform

Driving style recognition using a smartphone as a sensor platform

Collocated Multi-user Gestural Interactions with Unmodified Wearable Devices

Improving In-game Gesture Learning with Visual Feedback

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