Wrist Player: A Smartwatch Gesture Controller for Smart TVs

Luna, Mateus Machado; Carvalho, Thyago Peres; Soares, Fabrízzio; Nascimento, Hugo Alexandre Dantas do; Costa, Ronaldo Martins da

doi:10.1109/compsac.2017.266

Cited by 26 publications

(8 citation statements)

References 14 publications

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“…Li [14] Luna [15] Lian [16] Bindroo [ If the data matches the hand gesture for smart lighting control, the light will turn on, if it does not match, the user will have to make another gesture.…”

Section: Itemmentioning

confidence: 99%

“…Based on the above problems, a system is needed to overcome some of the smart lighting control system deficiencies. Wearable devices such as smartwatches are simple and easy to use so that they can be a solution to the above problems, such as in [14] and [15] who succeeded in building a smartwatch to control smart home using hand gestures using an Inertial Measuring Unit (IMU) sensor. Besides IMU sensors, [16] is research that uses an electromyography sensor for hand gesture detection.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control

2021

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Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance.

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Section: Itemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control

2021

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“…Luna et al [13], developed a method that allows the control of Smart TVs using gestures made by the pulse of a person using a smartwatch. With the focus on device development, the work proposed by Volkinburg and Washington [23] developed a wearable controller for gesture recognition using polyvinylidene fluoride (PVDF), the controller is attached to a comprehension glove used on the user's forearm.…”

Section: Related Workmentioning

confidence: 99%

WatchControl: A Control for Interactive Movie Using Continuous Gesture Recognition in Smartwatches

Nascimento

Soares

2020

Journal of Information Processing

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This work proposes the development of a method to allow the use of smartwatch as a control for interactive movies using continuous recognition of gestures in smartwatches. We developed two prototypes to interact with interactive movies. The first one uses gesture recognition on the smartwatch screen. Thus, we created a set of gestures composed of straight lines that represent the actions in the interactive movie. In this prototype, the recognition of gestures is performed by the algorithm of continuous recognition of gestures. In this way, a gesture can be recognized before the user finalizes, and the action is sent quickly to the movie. The second prototype uses a touch of pressure in the smartwatch to control the film. As a study case, we have conducted our user study with the movie "Black Mirror: Bandersnatch", with usability and experience evaluation. A result has shown that the gestures proposed in the method are intuitive and that it has the potential to be used pleasantly and satisfactorily by the users. For 90% of the participants, the use of the smartwatch is more practical and easy to control Netflix compared to the remote control.

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“…Abhilfe schafft ein physisches Gerät, das in der Hand gehalten oder am Arm getragen wird ("tangible HCI"). Neben Datenhandschuhen und Armbändern [9] werden dazu heute vielfach Smartphones und andere mobile Endgeräte empfohlen, die mit entsprechenden Steuereinheiten beispielsweise im "Smart Home" gekoppelt sind und mit denen auf der Touchscreen-Oberfläche oder mittels Gesten, erfasst z.B. durch die Beschleunigungssensoren im Smartphone, ausgewählte Geräte gesteuert werden können (siehe u.a.…”

Section: Motivationunclassified

P2.05 - Entwurf einer universellen tastenlosen optischen Fernbedienung

Ruser¹

2019

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Wrist Player: A Smartwatch Gesture Controller for Smart TVs

Cited by 26 publications

References 14 publications

An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control

An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control

WatchControl: A Control for Interactive Movie Using Continuous Gesture Recognition in Smartwatches

P2.05 - Entwurf einer universellen tastenlosen optischen Fernbedienung

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