Control with Gestures: A Hand Gesture Recognition System Using Off-the-Shelf Smartwatch

Zhu, Peide; Zhou, Hao; Cao, Shuai; Yang, Panlong; Xue, Shuangshuang

doi:10.1109/bigcom.2018.00018

Cited by 22 publications

(9 citation statements)

References 9 publications

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“…Therefore, RNNs were difficult to train through backpropagation. In recent approaches, existing simple neuronal structures have been modified using memory cells and gate units to more efficiently learn dependencies over longer intervals [35][36][37][38]. In this study, we evaluate the performance of the proposed floor type classification using two such neural networks, namely long short-term memory (LSTM) and gated recurrent units (GRUs).…”

Section: Gated Rnns-lstm and Grumentioning

confidence: 99%

Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Ryu¹,

Kim²

2020

Sensors

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Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.

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Section: Gated Rnns-lstm and Grumentioning

confidence: 99%

Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Ryu¹,

Kim²

2020

Sensors

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“…The work developed by Zhu et al [26] presents a deep neural network based on bidirectional LSTM that can recognize the gestures movements of the wrist and fingers of the users, the results show that the system has the potential to use a smartwatch as a remote control. The work of Gkournelos et al [5] proposed a method that allows controlling robots using a smartwatch as a control mechanism.…”

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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“…Socher et al proposed an intelligent system that classifies 3D objects in RGB-D (RGB and Depth) video sequences [17] by employing convolutional neural network (CNN) and RNN-based sequence learning architecture. RNN-based architecture was widely employed in the area of gesture recognition tasks to process RGB videos [20] and inertial sensor sequences from wearable devices [21,22]. Recently, Kim and Han revealed that a multidimensional temporal sequence can be encoded as latent space vectors using gated RNNs (e.g., long short-term memory (LSTM) [23] and gated recurrent units (GRUs) [15]) [24].…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Winter Sports Based on Multivariate Sequence Learning

Han

Ryu

Kim

2019

Sensors

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In this paper, we present an intelligent system that is capable of estimating the status of a player engaging in winter activities based on the sequence analysis of multivariate time-series sensor signals. Among the winter activities, this paper mainly focuses on downhill winter sports such as alpine skiing and snowboarding. Assuming that the mechanical vibrations generated by physical interaction between the ground surface and ski/snowboard in motion can describe the ground conditions and playing contexts, we utilize inertial and vibration signals to categorize the motion context. For example, the proposed system estimates whether the player is sitting on a ski lift or standing on the escalator, or skiing on wet or snowy ground, etc. To measure the movement of a player during a game or on the move, we develop a custom embedded system comprising a motion sensor and piezo transducer. The captured multivariate sequence signals are then trained in a supervised fashion. We adopt artificial neural network approaches (e.g., 1D convolutional neural network, and gated recurrent neural networks, such as long short-term memory and gated recurrent units). The experimental results validate the feasibility of the proposed approach.

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Control with Gestures: A Hand Gesture Recognition System Using Off-the-Shelf Smartwatch

Cited by 22 publications

References 9 publications

Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

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

Context-Aware Winter Sports Based on Multivariate Sequence Learning

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