Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map

Li, Nan; Yang, Dapeng; Jiang, Li; Liu, Hong; Cai, Hansheng

doi:10.1016/s1672-6529(11)60095-4

Cited by 98 publications

(58 citation statements)

References 18 publications

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“…This results in pressure changes observable between the surface of the forearm skin and the socket. This pressure pattern may be used as the primary information source for prosthetic control [22][23][24][25][26]. The hypothesis is that the pressure patterns generated by various hand motions are distinct enough to differentiate the various motions from each other.…”

Section: Introductionmentioning

confidence: 99%

“…Yungher and Craelius then used an array of force sensors to measure pressure changes on the skin caused by muscular activity and a linear regression-based approach to accurately discriminate six different grasps with the arm in a fixed static position [23]. Li et al recently used an array of 32 force-sensitive resistor sensors combined with an SVM classifier for finger-motion recognition based on pressure distribution maps [24]. They were able to accurately identify 17 different finger motions in within-session validation.…”

Section: Introductionmentioning

confidence: 99%

“…HD-FMG measures changes in surface pressure caused by the displacement (i.e., the bulge) of the underlying muscle, movement of the bone, or even displacement of soft tissue to create a circumferential pressure map of the limb. HD-FMG extends previous FMG methods [22][23][24][25] by completely encircling the forearm with a custom, high-resolution grid of 126 pressure sensors. This advantage enables HD-FMG to accurately sense even small changes in the surface pressure map of the limb.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

101

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Abstract-Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using highdensity force myography (HD-FMG) for prosthetic control. HD-FMG uses a high-density array of pressure sensors to detect changes in the pressure patterns between the residual limb and socket caused by the contraction of the forearm muscles. In this work, HD-FMG outperforms the standard electromyography (EMG)-based system in detecting different wrist and hand gestures. With the arm in a fixed, static position, eight hand and wrist motions were classified with 0.33% error using the HD-FMG technique. Comparatively, classification errors in the range of 2.2%-11.3% have been reported in the literature for multichannel EMG-based approaches. As with EMG, position variation in HD-FMG can introduce classification error, but incorporating position variation into the training protocol reduces this effect. Channel reduction was also applied to the HD-FMG technique to decrease the dimensionality of the problem as well as the size of the sensorized area. We found that with informed, symmetric channel reduction, classification error could be decreased to 0.02%.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

101

View full text Add to dashboard Cite

show abstract

“…Based on an electronic learning machine classifier, the methodology provided 92.33% accuracy with real-time performance. Alternatively, the identification of 17 hand postures comprising combinations of flexed fingers was proposed in [12]. The system was designed using a 32 FSR socket and signal processing by support vector machines, yielding >99% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…The electromyography signals are severely affected by electromagnetic noise, sweat, and variations of transducer placement. Moreover, the application of feature extraction procedure over a large number of channels is required in order to decode the subject motions with a suitable dexterity level [11,12]. In this context, different methods such as the mechanomyography [13], sonomyography [14], optical myography [15,16], and the force myography (FMG) [17] have been proposed in order to overcome the drawbacks presented by the myoelectric case.…”

Section: Introductionmentioning

confidence: 99%

Optical Fiber Force Myography Sensor for Identification of Hand Postures

Fujiwara

Suzuki

2018

Journal of Sensors

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A low-cost optical fiber force myography sensor for noninvasive hand posture identification is proposed. The transducers are comprised of 10 mm periodicity silica multimode fiber microbending devices mounted in PVC plates, providing 0.05 N −1 sensitivity over~20 N range. Next, the transducers were attached to the user forearm by means of straps in order to monitor the posterior proximal radial, the anterior medial ulnar, and the posterior distal radial muscles, and the acquired FMG optical signals were correlated to the performed gestures using a 5 hidden layers, 20-neuron artificial neural network classifier with backpropagation architecture, followed by a competitive layer. The overall results for 9 postures and 6 subjects indicated a 98.4% sensitivity and 99.7% average accuracy, being comparable to the electromyographic approaches. Moreover, in contrast to the current setups, the proposed methodology allows the identification of poses characterized by different configurations of fingers and wrist joint displacements with the utilization of only 3 transducers and a simple interrogation scheme, being suitable to further applications in human-computer interfaces.

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First Decade of Interfacial Iontronic Sensing: From Droplet Sensors to Artificial Skins

et al. 2020

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Over the past decade, a brand‐new pressure‐ and tactile‐sensing modality, known as iontronic sensing has emerged, utilizing the supercapacitive nature of the electrical double layer (EDL) that occurs at the electrolytic–electronic interface, leading to ultrahigh device sensitivity, high noise immunity, high resolution, high spatial definition, optical transparency, and responses to both static and dynamic stimuli, in addition to thin and flexible device architectures. Together, it offers unique combination of enabling features to tackle the grand challenges in pressure‐ and tactile‐sensing applications, in particular, with recent interest and rapid progress in the development of robotic intelligence, electronic skin, wearable health as well as the internet‐of‐things, from both academic and industrial communities. A historical perspective of the iontronic sensing discovery, an overview of the fundamental working mechanism along with its device architectures, a survey of the unique material aspects and structural designs dedicated, and finally, a discussion of the newly enabled applications, technical challenges, and future outlooks are provided for this promising sensing modality with implementations. The state‐of‐the‐art developments of the iontronic sensing technology in its first decade are summarized, potentially providing a technical roadmap for the next wave of innovations and breakthroughs in this field.

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Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map

Cited by 98 publications

References 18 publications

High-density force myography: A possible alternative for upper-limb prosthetic control

High-density force myography: A possible alternative for upper-limb prosthetic control

Optical Fiber Force Myography Sensor for Identification of Hand Postures

First Decade of Interfacial Iontronic Sensing: From Droplet Sensors to Artificial Skins

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