Hand Gesture Recognition using Flex Sensor and Machine Learning Algorithms

Panda, Akash Kumar; Chakravarty, Rommel; Moulik, Soumen

doi:10.1109/iecbes48179.2021.9398789

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…Considering four commonly used MCUs, a total of 159 design points were determined according to the configuration of the sensor operating frequency, the presence of preprocessing filters, and the size of the MLP classifier. As a result of Pareto Fronts, the proposed design achieved up to 95.5% accuracy with an energy consumption of 2.74 mJ, which shows up to 10% higher accuracy than previous studies [26] with similar low-end MCUs. Collectively, this study details how to achieve energy-accuracy aware design points under given energy or accuracy constraints.…”

Section: Discussionmentioning

confidence: 67%

Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices

Jung

Lee

2022

Sensors

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Wearable Internet of Things (IoT) devices can be used efficiently for gesture recognition applications. The nature of these applications requires high recognition accuracy with low energy consumption, which is not easy to solve at the same time. In this paper, we design a finger gesture recognition system using a wearable IoT device. The proposed recognition system uses a light-weight multi-layer perceptron (MLP) classifier which can be implemented even on a low-end micro controller unit (MCU), with a 2-axes flex sensor. To achieve high recognition accuracy with low energy consumption, we first design a framework for the finger gesture recognition system including its components, followed by system-level performance and energy models. Then, we analyze system-level accuracy and energy optimization issues, and explore the numerous design choices to finally achieve energy–accuracy aware finger gesture recognition, targeting four commonly used low-end MCUs. Our extensive simulation and measurements using prototypes demonstrate that the proposed design achieves up to 95.5% recognition accuracy with energy consumption under 2.74 mJ per gesture on a low-end embedded wearable IoT device. We also provide the Pareto-optimal designs among a total of 159 design choices to achieve energy–accuracy aware design points under given energy or accuracy constraints.

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Section: Discussionmentioning

confidence: 67%

Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices

Jung

Lee

2022

Sensors

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“…vocalization have been promising. In a study by Mahajan et al (2020), the authors used a Random Forest model to classify the gestures. The model achieved an accuracy of an Arduino board to collect data on hand gestures and trained a Random Forest model to classify the gestures.…”

Section: Resultsmentioning

confidence: 99%

Breaking Barriers: Hand Gesture Vocalizer for the Deaf and Mute

Jha,

Pathak

2024

JMC Res. Jnl

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This study addresses communication challenges faced by deaf and mute individuals by exploring the feasibility of utilizing flex sensors and the Random Forest algorithm for hand gesture vocalization. The background underscores the significance of accessible communication tools in enhancing the lives of those with hearing and speech impairments. The study's purpose is to assess the eﬀectiveness of flex sensors in detecting hand gestures and the Random Forest algorithm's potential to generate vocalized speech corresponding to these gestures. The methodology involves data collection from flex sensors through Arduino, Random Forest model training, and accuracy evaluation in gesture recognition. Promising results indicate the model's high accuracy in classifying diverse hand gestures. The study emphasizes the technology-driven solution's importance in bridging communication gaps for those with impairments. Combining flex sensors and the Random Forest algorithm oﬀers an intuitive communication tool, transforming interactions for deaf and mute individuals. Consideration for real-world scenarios and user diversity during system development is highlighted, crucial for practical accuracy. Beyond individual communication, the study's implications span education, employment, and social integration for people with disabilities. Implementing this technology in education fosters inclusive environments, empowering deaf and mute students to engage actively. The integration of flex sensors and the Random Forest algorithm holds immense promise, revolutionizing communication, and life quality. As an accessible gesture-based vocalization tool, it can reshape societal perspectives, fostering inclusivity and empathy. The study advocates continuous research and development, urging widespread technology adoption to create an inclusive society valuing diversity.

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“…The proposed recognition system's workflow includes the preprocessing and preparation of the collected data, Cross-Validation, Classification, and Performance Evaluation [14] [15]. Arabic sign language can be recognized using data that has been collected.…”

Section: The Proposed Ai Recognition Modelmentioning

confidence: 99%

Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing

Elmatary,

Maher,

Ibrahim

2023

JCC

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A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called "Lifesign" for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.

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Hand Gesture Recognition using Flex Sensor and Machine Learning Algorithms

Cited by 14 publications

References 3 publications

Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices

Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices

Breaking Barriers: Hand Gesture Vocalizer for the Deaf and Mute

Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing

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