Hand Gesture Recognition system for physically challenged people using IoT

Nair, Rajit; Singh, Dileep Kumar; Ashu,; Yadav, Shivam; Bakshi, Sourabh

doi:10.1109/icaccs48705.2020.9074226

Cited by 39 publications

(7 citation statements)

References 15 publications

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“…Human activity recognition (HAR) is concerned with recognizing people's daily routines using sensor-based time-series information [1]. Sensors, the Internet of Things (IoT), cloud computing, and edge computing have all advanced in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Nair

Ragab

Mujallid³

et al. 2022

Wireless Communications and Mobile Computing

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Human activity recognition is a time series classification problem that is difficult to solve (HAR). Traditional signal processing approaches and domain expertise are necessary to appropriately create features from raw data and fit a machine learning model for predicting a person’s movement. This work aims to demonstrate how a hybrid deep learning model may be used to recognize human behavior. Deep learning methodologies such as convolutional neural networks and recurrent neural networks will extract the features and achieve the classification goal. The suggested model has used wireless sensor data mining datasets to predict human activity. The model’s performance has been assessed using the confusion matrix, accuracy, training loss, and testing loss. Thus, the model has achieved greater than 96% accuracy, superior to other state-of-the-art algorithms in this field.

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Section: Introductionmentioning

confidence: 99%

Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Nair

Ragab

Mujallid³

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…It supports many emerging Internet of Things (IoT) applications such as user recognition [2], smart home [3], healthcare [4], etc. Generally, the technologies based on gesture recognition include sensors [5], web cameras [6], and millimeter-wave radars [7]. However, they all have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal

et al. 2022

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Wireless sensing has been increasingly used in smart homes, human–computer interaction and other fields due to its comprehensive coverage, non-contact and absence of privacy leakage. However, most existing methods are based on the amplitude or phase of the Wi-Fi signal to recognize gestures, which provides insufficient recognition accuracy. To solve this problem, we have designed a deep spatiotemporal gesture recognition method based on Wi-Fi signals, namely Wi-GC. The gesture-sensitive antennas are selected first and the fixed antennas are denoised and smoothed using a combined filter. The consecutive gestures are then segmented using a time series difference algorithm. The segmented gesture data is fed into our proposed RAGRU model, where BAGRU extracts temporal features of Channel State Information (CSI) sequences and RNet18 extracts spatial features of CSI amplitudes. In addition, to pick out essential gesture features, we introduce an attention mechanism. Finally, the extracted spatial and temporal characteristics are fused and input into softmax for classification. We have extensively and thoroughly verified the Wi-GC method in a natural environment and the average gesture recognition rate of the Wi-GC way is between 92–95.6%, which has strong robustness.

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“…Technology that involves interaction between humans and computers has increased widely and rapidly with various methods and techniques to overcome various problems in human life [4]. Human-computer interaction methods mainly include voice interaction and gesture interaction [2] because gestures contain a lot of information that can convey semantics, emotions and conform to human daily life habits [13].…”

Section: Introductionmentioning

confidence: 99%

“…Human-computer interaction methods mainly include voice interaction and gesture interaction [2] because gestures contain a lot of information that can convey semantics, emotions and conform to human daily life habits [13]. The gesture can be in the form of a hand gesture that allows us to express our thoughts clearly in our everyday interactions [4]. Gesture recognition is a mathematical interpretation of human movement by computing devices [6].…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition as Signal for Help using Deep Neural Network

Thejowahyono¹,

Setiawan²,

Handoyo³

et al. 2022

IJETAE

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Hand gesture recognition research has increased widely and rapidly to overcome various problems in human life. When in a dangerous situation humans are required to act quickly and precisely, such as being a victim of violence, accidents, disasters, and so on. In the asking process for help, it is necessary to use an appropriate safe method, communication tools, and speed in sending messages requesting help so that something unwanted does not happen. One of the ways to overcome this problem is to use sign language such as hand gestures. So that this study designs and develops a real-time hand gesture recognition system as signal for help using Deep Neural Network. The purpose of this research is to simplify, speed up, and produce a safe way that humans can use when asking for help from others without speaking when in danger happen. Base on the experiment that real-time detection and an average accuracy of 98.79%. When the system detects a hand gesture to ask for help, a request for help message will be sent to the designated emergency email. So that the benefits derived from the design and development of this system are convenience, speed, and safety in the process of asking for help when humans are in a dangerous situation. Keywords— Hand gesture recognition, Signal for help, Deep Neural Network, Dangerous Situation, Emergency Email.

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Hand Gesture Recognition system for physically challenged people using IoT

Cited by 39 publications

References 15 publications

Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal

Hand Gesture Recognition as Signal for Help using Deep Neural Network

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