Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint

Paul, Aditya Jyoti; Mohan, Puranjay; Sehgal, Stuti

doi:10.1109/raics51191.2020.9332480

Cited by 29 publications

(7 citation statements)

References 13 publications

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“…Interpolation augmentation, as suggested in [22], was used in the proposed model, improving generalization, and corroborating the statement, "Interpolation Augmentation seems to improve generalization for resource-constrained endpoints."…”

Section: Discussionsupporting

confidence: 68%

“…The documentation of the board suggests keeping the model under 400KB, but during this study [22] we found that the biggest model which can be fit successfully in memory is under 230KB. A larger model of size upto 1MB can be stored on the flash memory but for that the model has to converted into a FlatBuffer using STM32Cube.AI and the operating system has to be recompiled, which leads to the loss of the utility of the MicroPython.…”

Section: Hardware Setup For Deploymentmentioning

confidence: 76%

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A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

Mohan,

Paul,

Chirania

2020

Preprint

Self Cite

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The world is going through one of the most dangerous pandemics of all time with the rapid spread of the novel coronavirus . According to the World Health Organisation, the most effective way to thwart the transmission of coronavirus is to wear medical face masks. Monitoring the use of face masks in public places has been a challenge because manual monitoring could be unsafe. This paper proposes an architecture for detecting medical face masks for deployment on resourceconstrained endpoints having extremely low memory footprints. A small development board with an ARM Cortex-M7 microcontroller clocked at480 Mhz and having just 496 KB of framebuffer RAM, has been used for the deployment of the model. Using the TensorFlow Lite framework, the model is quantized to further reduce its size. The proposed model is 138 KB post quantization and runs at the inference speed of 30 FPS.

show abstract

Section: Discussionsupporting

confidence: 68%

Section: Hardware Setup For Deploymentmentioning

confidence: 76%

A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

Mohan,

Paul,

Chirania

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…From Table 4, it is clear that the proposed method, whether using a single model or a multi-model, is better than the models presented in the previous studies referred to in the table. [30] Transfer learning using MobileNetV2 on 29 classes 98.67% Sinha et al, 2019 [31] Custom CNN model with fully connected layer on 29 classes 96.03% Kadhim et al, 2020 [32] Transfer learning using VGG1 on 28 classes 98.65% Paul et al 2020 [33] Custom CNN model with fully connected layer on 24 classes 99.02% Mahmud et al, 2018 [34] HOG feature extraction & KNN classifier on 26 classes 94.23% Prasad 2018 [35] Image magnitude gradient for feature extraction on 24 classes 95.40% Phong &Ribeiro 2019 [36] Transfer learning on multiple architecture, etc on 29 classes 99.00% Ashiquzzaman et al, 2020 [37]…”

Section: Resultsmentioning

confidence: 99%

Static hand gesture recognition of Arabic sign language by using deep CNNs

Ismail

Dawwd

Ali

2021

IJEECS

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An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected from different volunteers. The pre-trained models were used and trained on prepared Arabic sign language data. These models were used after some modification. Also, an attempt has been made to adopt two models from the previously trained models, where they are trained in parallel deep feature extractions. Then they are combined and prepared for the classification stage. The results demonstrate the comparison between the performance of the single model and multi-model. It appears that most of the multi-model is better in feature extraction and classification than the single models. And also show that when depending on the total number of incorrect recognize sign image in training, validation and testing dataset, the best convolutional neural networks (CNN) model in feature extraction and classification Arabic sign language is the DenseNet121 for a single model using and DenseNet121 & VGG16 for multi-model using.

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“…A DL model was introduced to detect Sign Language Alphabet on tiny edge devices, with an aim to enable deaf-mute to easily communicate with the community. Authors in [28] proposed a model to detect the American Sign language (ASL) Alphabet and transcribed to text and speech in real time on tiny wearable IoT devices. The device has the smallest and cheapest microcontrollers.…”

Section: Sign Language Detectionmentioning

confidence: 99%

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

Alajlan

Ibrahim

2022

Micromachines

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Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed in various fields. Many of these devices are based on machine learning (ML) models, which render them intelligent and able to make decisions. IoT devices typically have limited resources, which restricts the execution of complex ML models such as deep learning (DL) on them. In addition, connecting IoT devices to the cloud to transfer raw data and perform processing causes delayed system responses, exposes private data and increases communication costs. Therefore, to tackle these issues, there is a new technology called Tiny Machine Learning (TinyML), that has paved the way to meet the challenges of IoT devices. This technology allows processing of the data locally on the device without the need to send it to the cloud. In addition, TinyML permits the inference of ML models, concerning DL models on the device as a Microcontroller that has limited resources. The aim of this paper is to provide an overview of the revolution of TinyML and a review of tinyML studies, wherein the main contribution is to provide an analysis of the type of ML models used in tinyML studies; it also presents the details of datasets and the types and characteristics of the devices with an aim to clarify the state of the art and envision development requirements.

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Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint

Cited by 29 publications

References 13 publications

A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

Static hand gesture recognition of Arabic sign language by using deep CNNs

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

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