A Methodology to Introduce Gesture-Based Interaction into Existing Consumer Product

Cavalieri, Lorenzo; Mengoni, Maura; Ceccacci, Silvia; Germani, Michele

doi:10.1007/978-3-319-39516-6_3

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Hand gesture-based interaction is one common approach being considered as HMI inputs [3]. Hand gestures are recognized by two major methods: Vision image processing [4] or wearable electronics [5].…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Han

Yoon

2019

Sensors

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Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90–96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.

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Section: Introductionmentioning

confidence: 99%

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Han

Yoon

2019

Sensors

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“…Hand gestures are an important tool that help us communicate with each other. Recently, human-computer interaction, which involves employing new ideas and technologies, has become increasingly important in solving many problems in our daily lives [2]. Gesture recognition involves a human-computer interaction method wherein human gestures are processed and recognized by the machine.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

Chin-Shyurng

Lee

2019

Applied Sciences

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Gesture recognition is a human−computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.

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Gesture Detection Using Accelerometer and Gyroscope

Gupta

Chaudhary

Vedant

et al. 2022

Emerging Research in Computing, Information, Communication and Applications

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A Methodology to Introduce Gesture-Based Interaction into Existing Consumer Product

Cited by 5 publications

References 16 publications

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

Gesture Detection Using Accelerometer and Gyroscope

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