Smart Wristband for Gesture Recognition

Xia, Yuanjie; Heidari, Hadi; Ghannam, Rami

doi:10.1109/ucet51115.2020.9205426

Cited by 3 publications

(1 citation statement)

References 14 publications

(13 reference statements)

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“…In addition to that, more advanced eye tracking algorithm could be developed improve the accuracy and recognition speed. In addition to traditional Human-computer interaction methods, for instance, hand gesture [18], this eye tracking system could also be integrated in IoT applications, which can improve and enhance the interaction of futuristic IoT designs.…”

Section: Discussionmentioning

confidence: 99%

Low Cost Real-time Eye Tracking System for Motorsports

Xia¹,

Lunardi

Heidari³

et al. 2022

2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

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Eye-tracking technology can be used to determine reaction time and cognitive abilities of a subject based on response time to visual changes and analysis of focus points and duration. In the context of motorsports, quantifying driver visual data adds to the wealth of information required for the marginal performance gains that make up motorsport competition. Specifically, the gaze of drivers can be used to analyze and assess driving abilities and improve driving performance. Despite eye trackers being perfectly suited for this purpose, their high cost may impede their penetration in the motorsports industry. Therefore, we demonstrate a low-cost wearable real-time eye tracking system for motor sport applications. This system was mounted on a racing helmet to monitor the driver's gaze in real-time. A dualcamera system was used to capture both eye image and front view. The eye image was processed using OpenCV-Python to determine the gaze coordinates. Moreover, the gaze position was mapped into the front view to determine the wearer's line of sight. The eye tracking helmet was tested and demonstrated good accuracy, where the deviation angle was 3.8 degrees. These promising results further add to the growing body of knowledge in low cost eye trackers.

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Section: Discussionmentioning

confidence: 99%

Low Cost Real-time Eye Tracking System for Motorsports

Xia¹,

Lunardi

Heidari³

et al. 2022

2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

Self Cite

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A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Cao,

Ma,

Chen

et al. 2024

Advanced Intelligent Systems

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Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

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A Bio-Impedance Analysis Method Based on Human Hand Anatomy for Hand Gesture Recognition

Chen

Wang

et al. 2021

IEEE Trans. Instrum. Meas.

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Smart Wristband for Gesture Recognition

Cited by 3 publications

References 14 publications

Low Cost Real-time Eye Tracking System for Motorsports

Low Cost Real-time Eye Tracking System for Motorsports

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

A Bio-Impedance Analysis Method Based on Human Hand Anatomy for Hand Gesture Recognition

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