Pilot study on fine finger movement regression, using FMG

Chegani, Rana Sadeghi; Menon, Carlo

doi:10.1109/smc.2017.8122753

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…There are some other machine learning algorithms used in the past few years. Chegani et al [ 107 ] used a random forest regression algorithm to achieve fine finger regression by FMG. In their work, an average squared correlation coefficient of 75% was gained, which showed that it was feasible for FMG to predict finger movements.…”

Section: Data Processingmentioning

confidence: 99%

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Zheng

Zhao

et al. 2022

Biosensors

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Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

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Section: Data Processingmentioning

confidence: 99%

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Zheng

Zhao

et al. 2022

Biosensors

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Hand Gesture Recognition With Acoustic Myography and Wavelet Scattering Transform

et al. 2022

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Analysis of Skin Deformation Differences on the Upper Arm Between Active and Passive Movements During Elbow Flexion and Extension

Cho

Toyoda

Ding

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The motion ability of patients in the acute phase of stroke is difficult to define with existing indexes such as the Brunnstrom stage. Hence, for designing a novel evaluation index for stroke rehabilitation in the acute phase, we focused on the differences between the skin deformations in active and passive movements. Skin deformation reflects the activities of body tissues that are related to motion ability. We measured skin deformations on the upper arm in active and passive movements during elbow flexion and extension and extracted features from these deformations. For practical rehabilitation applications, we developed a novel flexible distance sensor array to reduce the time needed for attaching sensors to patients. Using principal component analysis (PCA), the skin deformation could be decomposed into joint movements and activeness of movements as the first two components (PC1 and PC2). The joint angle and PC1 exhibited a high correlation, and the standard deviation (SD) of PC2 indicated a significant difference in the types of movements. From the above results, we concluded that the SD ratio between PC2 and PC1 may be used to evaluate motion ability considering the inherent biomechanical characteristics.

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Pilot study on fine finger movement regression, using FMG

Cited by 4 publications

References 22 publications

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Hand Gesture Recognition With Acoustic Myography and Wavelet Scattering Transform

Analysis of Skin Deformation Differences on the Upper Arm Between Active and Passive Movements During Elbow Flexion and Extension

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