Generalized Hand Gesture Recognition for Wearable Devices in IoT: Application and Implementation Challenges

Iyer, Darshan; Mohammad, Fahim; Guo, Yuan; Safadi, Ebrahim Al; Smiley, Benjamin J.; Liang, Zhiqiang; Jain, Nilesh

doi:10.1007/978-3-319-41920-6_26

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Content may change prior to final publication. Embedded signal processing to avoid unnecessary data transmission by carrying out local processing [54], [57], [58], [87] Compressive sensing to avoid generating unnecessary redundant data [53], [84], [92], [99] Embedded ML to carry out complex computation tasks on the device to reduce latency Wireless technologyrelated issues A, HW [45] Duty cycling to periodically power off communication modules when not in use to conserve energy [49], [61] Adaptive transmission power control of transceiver based on distance to remote node [59] Use of low power wireless communication protcols such as 6LoWPAN [61] A multi channel TDMA approach to allocate slots for simultaneous transmission [109] An adaptive connection interval selection in dynamic channel environments for improved connectivity [98], [148] Low power wireless communication protocols…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

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Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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“…We train the classifier using the training set, tune the parameters using the validation set and evaluate the performance using the test set. The precision, recall and F1-score is used for the performance evaluating from different aspects [14], [24].…”

Section: ) Evaluation Metricsmentioning

confidence: 99%

SmartGe: Identifying Pen-Holding Gesture With Smartwatch

Zhang

Chen

2020

IEEE Access

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Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, a detailed design and evaluation of the system, called SmartGe, is presented, which can identify the pen-holding gesture with smartwatch when writing Chinese and English. We firstly analyze the hand movement and propose a novel handwriting detection algorithm to segment each stroke or letter. Then we recognize the pen-holding gesture using deep convolution neural network(DCNN). To improve system performance in Chinese writing, we connect a vertical stroke and a horizontal stroke for pen-holding gesture recognition. SmartGe provides a convenient and natural way to improve users' writing habits, which is a lightweight system, and extensive experiments confirm its effectiveness and robustness. INDEX TERMS Smartwatch, handwriting detection, pen-holding gesture, combined signal, deep convolution neural network.

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“…al. [9] proposed inertial measurement unit sensor based hand gesture recognition system. The system proposed to spotting gestures and reduces false positives using sensor based system.…”

Section: Related Workmentioning

confidence: 99%

IOT Based Gesture Recognition for Smart Controlling

Kshirsagar*,

Rokade,

Kamble

2019

IJRTE

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Internet of things (IOT) analyzing network things embedded with software, sensors, n/w etc. IOT extend to internet connectivity with flexible devices. IOT makes life easier and makes smart with improving networks and power of data correlations. Major fundamental component of IOT systems are sensors, connectivity, data processing, active engagement and user interface. User interface make comfort between computer and human interaction. Human interaction as consider in form of command and computer interpret that commands. This paper presents user interface with internet of things (IOT) more effectively with comparing different techniques on recent review base.

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Generalized Hand Gesture Recognition for Wearable Devices in IoT: Application and Implementation Challenges

Cited by 9 publications

References 13 publications

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

SmartGe: Identifying Pen-Holding Gesture With Smartwatch

IOT Based Gesture Recognition for Smart Controlling

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