Human Computer Interaction for the Accelerometer-Based Mobile Game

Baek, Jaemin; Jang, Ik-Jin; Park, Keehyun; Kang, Hyun‐Soo; Yun, Byoung-Ju

doi:10.1007/11802167_52

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Gesture interactions in VR are facilitated through different input devices such as wearable sensor-based devices, touch-based devices, and computer vision-based devices [13]. The wearable devices include data gloves, inertial sensors and myoelectricity sensors [14], [15]. The touch-based devices comprise touch screens and stylus pens.…”

Section: Gesture Interactions and Recognition In Virtual Realitymentioning

confidence: 99%

Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Harichandran¹,

Teizer²

2022

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Construction safety training through Virtual Reality (VR) environments offers workers an interactive and immersive experience. Many complex interactions and realistic scenarios are possible in VR using accessories such as data gloves and trackers which allow data recording on incidents. For example, misinterpretation of hand signals construction workers give during operations may result in severe accidents on construction sites. This study proposes automatic gesture recognition to identify hand signals for crane rigging operations in VR training. The developed gesture recognition algorithm tracks information from a data glove and a tracker. Preliminary data on movements and orientation of hands and fingers were recorded. Mathematical models of hand gestures were created based on finger movement data. Gesture rules were created based on the rotation and orientation of the hand. The gesture models were combined with the gesture rules to develop the algorithm for automated gesture recognition. Final experiments estimated the efficacy of the proposed method in automatically recognizing crane rigging signals in real-time. The performance is evaluated by comparing the identified hand gestures with independently created ground truth labels. The proposed method identified static hand gestures with an average accuracy of 96.55 percent. This method recognizes the gestures along with the hand movements and displays the results in real-time equivalent to dynamic gesture recognition. More refined dynamic gesture recognition based on this method is in progress.

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Section: Gesture Interactions and Recognition In Virtual Realitymentioning

confidence: 99%

Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Harichandran¹,

Teizer²

2022

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Their research shows that compared with voice gestures, laser pointer and the the physical entities based on Shu ID, gestures interact more naturally. In order to improve the handling of games on the mobile device, Baek, J. and others [4] in 2006 proposed the acceleration data by…”

Section: Technical Support -Realizability Of Gesture Andmentioning

confidence: 99%

Gesture-based visualization interaction design in handheld devices

Ding

Luo

2013

IEEE Conference Anthology

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A new gesture-based visualization interaction design process model was proposed to support the method of gesturebased visualization interaction design in handheld devices. The development of handheld device, especially smart phones, usercentered interaction design theory and the circumstance of the application of gestures in handheld devices were studied. The example of And-Enjoy, which is an app based on android system, was shown to verify the effectiveness and practicality of the model through design evaluation.

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“…Later this approach was extended to more complex activities of daily living [3], [4]. Other applications are in sports [5], [6], industry [7], [8], sign language recognition [9] and human computer interfaces [10].…”

Section: Introductionmentioning

confidence: 99%

Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks

Förster

Brem

Roggen

et al. 2009

2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Activity and gesture recognition from body-worn acceleration sensors is an important application in body area sensor networks. The key to any such recognition task are discriminative and variation tolerant features. Furthermore good features may reduce the energy requirements of the sensor network as well as increase the robustness of the activity recognition. We propose a feature extraction method based on genetic programming. We benchmark this method using two datasets and compare the results to a feature selection which is typically used for obtaining a set of features. With one extracted feature we achieve an accuracy of 73.4% on a fitness activity dataset, in contrast to 70.1% using one selected standard feature. In a gesture based HCI dataset we achieved 95.0% accuracy with one extracted feature. A selection of up to five standard features achieved 90.6% accuracy in the same setting. On the HCI dataset we also evaluated the robustness of extracted features to sensor displacement which is a common problem in movement based activity and gesture recognition. With one extracted features we achieved an accuracy of 85.0% on a displaced sensor position. With the best selection of standard features we achieved 55.2% accuracy. The results show that our proposed genetic programming feature extraction method is superior to a feature selection based on standard features.

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Human Computer Interaction for the Accelerometer-Based Mobile Game

Cited by 5 publications

References 7 publications

Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Gesture-based visualization interaction design in handheld devices

Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks

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