Handwritten signatures verification based on arm and hand muscles synergy

Asemi, Arsalan; Maghooli, Keivan; Rahatabad, Fereidoun Nowshiravan; Azadeh, Hamid

doi:10.1016/j.bspc.2022.103697

Cited by 7 publications

(2 citation statements)

References 54 publications

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“…Because the correct muscle coactivation pattern will gradually emerge when trying to track the displayed trajectory. In other words, recovering the muscle synergy means recognizing the correct movement patterns in performing a functional activity [42], [43], [44], [45]. Therefore, in this study, we present a fundamental method for designing visual biofeedback based on muscle synergy pattern analysis for the neurorehabilitation of stroke survivors.…”

Section: Introductionmentioning

confidence: 99%

Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke

Zendehbad,

Kobravi,

Khalilzadeh

et al. 2023

IEEE Access

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This study aimed to evaluate the effect of a novel underlining mechanism of visual biofeedback based on muscle synergy pattern on upper extremity motor functions for subacute stroke patients. The experimental studies were conducted on 12 participants in the control group and 24 subjects with ischemic stroke. In the first step, a visual biofeedback trajectory designed for rehabilitation was produced using the patterns extracted from the muscle synergy of arm movement using the hierarchical alternating least squares (HALS) method and with the help of nonlinear autoregressive with exogenous inputs (NARX) from the family of recurrent neural networks and was evaluated on healthy participants. In the second step, all patients received conventional therapy for the upper extremity, 2 times per week for 5 weeks. The interventional group additionally received training with the proposed visual biofeedback system for 30 minutes per session. The evaluations were performed regarding modeling performance, trackability, and clinical efficacy. In terms of modeling performance, the results showed that the NARX method has the best performance compared to other conventional models. Regarding trackability, the analyses based on computing the correlation coefficient showed significant improvement in the trackability from baseline to post-treatment in both the interventional and the control groups. In terms of clinical efficacy and based on analyzing the NIHSS, Fugl-Meyer, and MRS scores, the findings showed that the proposed visual biofeedback mechanism along with the conventional therapy may have supplemental benefits for stroke survivors.

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Section: Introductionmentioning

confidence: 99%

Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke

Zendehbad,

Kobravi,

Khalilzadeh

et al. 2023

IEEE Access

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show abstract

“…In the realm of biometrics, it holds a unique position, encapsulating both personal and distinctive attributes. [7] As the world transitions to digital and paperless transactions, the handwritten signature undergoes a transformative shift, evolving from traditional ink-on-paper to digitized representations. Despite this transformation, its importance persists, becoming a vital element in securing nancial transactions, document validity, and personal identity con rmation.…”

mentioning

confidence: 99%

Mobile Handwritten Signature Verification and Identification Using PCA for RemoteAuthentication

Kumar,

Awasthi

2024

Preprint

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This research proposal aims to develop a system for handwritten signature verification and identification using Principal Component Analysis (PCA) with user access via mobile cameras. The goal is to create a convenient and secure method for remote authentication without requiring in-person visits. We will investigate the feasibility of capturing high-quality signature samples through mobile cameras, implement PCA for feature extraction, and develop an efficient system for remote signature-based authentication.

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Leveraging deep feature learning for wearable sensors based handwritten character recognition

Singh

Chaturvedi²

2023

Biomedical Signal Processing and Control

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Handwritten signatures verification based on arm and hand muscles synergy

Cited by 7 publications

References 54 publications

Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke

Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke

Mobile Handwritten Signature Verification and Identification Using PCA for RemoteAuthentication

Leveraging deep feature learning for wearable sensors based handwritten character recognition

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