Writing in the air: Recognize Letters Using Deep Learning Through WiFi Signals

Fang, Yinyin; Xu, Yang; Li, Heju; He, Xin; Kang, Longlong

doi:10.1109/bigcom51056.2020.00008

Cited by 17 publications

(15 citation statements)

References 28 publications

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“…This system would be beneficial for tasks, such as gesture recognition based on multiple technologies, which numerous authors are researching. Most of the studies in this field are focused on radar [ 15 , 23 ], Wi-Fi [ 24 ] and ultrasound sensors [ 9 , 13 , 25 ]. In this paper, the framework will be evaluated for the generation of ultrasound data for gesture recognition.…”

Section: Envisioned Systemmentioning

confidence: 99%

Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation

Saez-Mingorance

Escobar-Molero

Mèndez

et al. 2021

Sensors

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This work studies the feasibility of a novel two-step algorithm for infrastructure and object positioning, using pairwise distances. The proposal is based on the optimization algorithms, Scaling-by-Majorizing-a-Complicated-Function and the Limited-Memory-Broyden-Fletcher-Goldfarb-Shannon. A qualitative evaluation of these algorithms is performed for 3D positioning. As the final stage, smoothing filtering techniques are applied to estimate the trajectory, from the previously obtained positions. This approach can also be used as a synthetic gesture data generator framework. This framework is independent from the hardware and can be used to simulate the estimation of trajectories from noisy distances gathered with a large range of sensors by modifying the noise properties of the initial distances. The framework is validated, using a system of ultrasound transceivers. The results show this framework to be an efficient and simple positioning and filtering approach, accurately reconstructing the real path followed by the mobile object while maintaining low latency. Furthermore, these capabilities can be exploited by using the proposed algorithms for synthetic data generation, as demonstrated in this work, where synthetic ultrasound gesture data are generated.

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Section: Envisioned Systemmentioning

confidence: 99%

Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation

Saez-Mingorance

Escobar-Molero

Mèndez

et al. 2021

Sensors

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“…Using these two datasets (images and 3D coordinates), a large range of DNN models can be trained for the classification of the gestures. The most relevant DNN structures in gesture recognition have been selected to classify our data due to previous high-performance results [ 3 , 4 , 6 , 7 , 8 , 9 , 13 , 31 , 32 , 33 ]. Different DNN approaches are included in this work to also compare the effect in the classification of the two previously depicted data types.…”

Section: Character Recognition Algorithmsmentioning

confidence: 99%

“…Consequently, this DNN is trained to classify each input data point individually without taking into account the length of time of each character or the time distribution of the positions. This DNN structure was selected according to the high-accuracy results achieved in the literature for gesture recognition [ 3 , 6 , 8 , 9 ]. These works focus on gesture data recorded with multiple sensors such as radar or Wi-Fi.…”

Section: Character Recognition Algorithmsmentioning

confidence: 99%

“…Air-writing systems are described in the literature using different technologies. There are systems based on video [ 3 ], infrared (IR) sensors [ 4 , 5 ], radar [ 6 , 7 , 8 ], Wi-Fi signal [ 9 ], RFID [ 10 ], or a combination of those technologies (i.e., IR sensors and video [ 11 , 12 ]). There are also works based on ultrasound technology such as Chen H. et al [ 13 ], who proposed a system where the recognition is based in a fixed receiver array performing the localization of a transmitter array attached to the user’s hand.…”

Section: Introductionmentioning

confidence: 99%

“…To be precise, these Deep Learning (DL) algorithms are Convolutional Neural Networks (CNN), Long Short-Term Memory Neural Networks (LSTM NN), Autoencoders, and variations of these algorithms. These algorithms have been selected according to the high-accuracy results achieved by other authors for gesture recognition tasks [ 6 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Air-Writing Character Recognition with Ultrasonic Transceivers

Saez-Mingorance

Mèndez

Mauro

et al. 2021

Sensors

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The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.

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Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Pramanik

Mukhopadhyay²,

Mirjalili³

et al. 2022

Neural Comput & Applic

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Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

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Writing in the air: Recognize Letters Using Deep Learning Through WiFi Signals

Cited by 17 publications

References 28 publications

Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation

Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation

Air-Writing Character Recognition with Ultrasonic Transceivers

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

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