A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

Yap, Hui-Kim; Ang, Benjamin W. K.; Lim, Jeong Hoon; Goh, James Cho Hong; Yeow, Chen-Hua

doi:10.1109/icra.2016.7487535

Cited by 95 publications

(56 citation statements)

References 27 publications

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“…Voice recognition is robust in ideal conditions, but sensitive to noise. Radio frequency identification (RFID) tags on objects can serve as non-biological switches to identify desired hand postures, again using EMG as a trigger to execute these postures [16]. RFID tags predetermine which objects the subject can interact with, which limits their utility in realworld environments.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Sensing and Interaction for a Robotic Hand Orthosis

Park

Meeker

Weber

et al. 2019

IEEE Robot. Autom. Lett.

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Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstationlike counterparts. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. Even when focusing on a specific condition, such as stroke, the variety of encountered upper limb impairment patterns means that a single sensing modality, such as electromyography (EMG), might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We believe that robotic hand orthoses developed as multimodal sensory platforms with help address some of the key challenges in physical interaction with the user.

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Section: Related Workmentioning

confidence: 99%

Multimodal Sensing and Interaction for a Robotic Hand Orthosis

Park

Meeker

Weber

et al. 2019

IEEE Robot. Autom. Lett.

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“…Se implementa el método umbral para la detección de activación muscular en Matlab 2016b (MathWorks). El método umbral determina que se ha producido una activación muscular si las señales EMG normalizadas superan un umbral [8], [12], [13]. En primer lugar, se realiza el filtrado de las señales mediante un filtro Notch, pasa bajo y pasa alto con una frecuencia de corte de 50, 10 y 500 Hz, respectivamente.…”

Section: Detección De Intencionalidad Del Movimientounclassified

Robhand, un exoesqueleto de mano para la rehabilitación neuromotora aplicando terapias activas y pasivas

Cisnal¹,

Lobo²,

Fraile³

et al. 2020

Actas De Las XXXIX Jornadas De Automática, Badajoz, 5-7 De Septiembre De 2018

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“…To evaluate the performance of the amplitude independent muscle activity detection method against amplitude dependent detection methods, a comparison has been conducted between the performance of the amplitude independent FLA-MSE algorithm and three amplitude dependent detection methods. The first method is the classical detection method which compares the rectified and filtered sEMG signal with a predefined threshold, where this method was used in most of the practical implemented hand robotic devices [16][17][18][19][20][21][22][23][24][25]. The second method is the Teager Kaiser Energy Operator (TKE) [7][8][9][10]26] and the third method is the Integrated Profile (IP) [13,27] of the sEMG signal.…”

Section: Performance Comparison Between the Fla-mse Algorithm And Thrmentioning

confidence: 99%

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Hameed

Hasan

Shafie

et al. 2020

IJEECS

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The amplitude dependent muscle activity detection algorithms of the surface electromyography (sEMG) signals are very sensitive to the changes in the background noise levels and the performance of these amplitude-based methods is highly deteriorated when the Signal to Noise ratio (SNR) of the sEMG signal is low. sEMG signals of deep and small muscles as well as sEMG signals recorded from patients that have neuromuscular diseases may not meet this SNR requirement which motivates the need for amplitude independent algorithms that can detect weak muscle activities. Moreover, the sEMG signal amplitude is not constant during the recording time due to the variation in the characteristics of the electrode-skin interface and due to the changes in the ground reference level. Therefore, the performance of the muscle activity detection algorithms should not be affected by the involuntary amplitude variations of the sEMG signal in order to achieve reliable control of robotic devices intended for disabled people. To accentuate the importance of the amplitude independent muscle activity detection methods over the amplitude dependent detection methods, a comparative study has been conducted in this paper between the performance of an amplitude independent muscle activity detection algorithm (FLA-MSE algorithm) and three amplitude dependent algorithms with respect to the detection capability of weak muscle activities and with respect to the immunity against false alarms. The results have showed that the performance of the amplitude independent algorithm outperformed the performance of the amplitude dependent algorithms for detecting weak muscle activities and for robustness against false alarms.

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A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

Cited by 95 publications

References 27 publications

Multimodal Sensing and Interaction for a Robotic Hand Orthosis

Multimodal Sensing and Interaction for a Robotic Hand Orthosis

Robhand, un exoesqueleto de mano para la rehabilitación neuromotora aplicando terapias activas y pasivas

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

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