Identification of a force-sensing resistor for tactile applications

Saadeh, Mohammad; Trabia, Mohamed B.

doi:10.1177/1045389x12463462

Cited by 16 publications

(14 citation statements)

References 15 publications

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“…They exhibit a considerable decrement of electrical resistance when subjected to mechanical stress [1]. They are therefore of extensive use as force-sensing resistors (FSR) in robotic links, biomechanical diagnosis, touchpads, touchscreens, and industrial processes, which are demanding force-sensing accuracy with less space consumption at reduced cost [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel and Inexpensive Approach for Force Sensing Based on FSR Piezocapacitance Aimed at Hysteresis Error Reduction

et al. 2018

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Force-sensing resistors (FSRs) are inexpensive alternatives to load cells. They are suitable for applications where noninvasive devices are needed to measure force, stress, or pressure. However, they have been proved to be hysteresis prone and offer nonrepeatable readings due to their highly voltage-dependent electrical resistance. A piezocapacitive effect has been found as an alternative phenomenon that is able to offer force-dependent readings of capacitance with less hysteresis error. Also, this capacitance is not dependent on voltage, which also improves repeatability in force measurements. Since measuring capacitance is more expensive than resistance, the least costly conditioning circuitry is desired. An inexpensive alternative using an LM555 that oscillates depending on capacitance is here presented. Hysteresis and repeatability errors have been reduced for a widespread-used force-sensing resistor brand.

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Section: Introductionmentioning

confidence: 99%

A Novel and Inexpensive Approach for Force Sensing Based on FSR Piezocapacitance Aimed at Hysteresis Error Reduction

et al. 2018

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“…Thus, the Interlink’s FSR possessing the lowest response time in the data sheet (see Table 1 ) and in experiments [ 16 ] was chosen to perform the FMG data collection in this work. A typical Interlink Electronics’s FSR sensor consists of a top carbon-based ink layer and a bottom conductive substrate layer with a spacer adhesive located in the middle of the two layers [ 17 ]. Therefore, during FMG collection, as the hand exerts a force, the corresponding muscles on the arm produce a deformation on the skin’s surface.…”

Section: Related Workmentioning

confidence: 99%

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Barioul

Kanoun

2023

Sensors

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The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.

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“…For automatic lumbar morphing support, the Baud Rate used was 1000, with a respective frequency of 25 Hz. As suggested in [56], from their study of a fast FFT analysis for the FSR's voltage response it was determined that frequencies higher than 40 Hz should be filtered out.…”

Section: Performancementioning

confidence: 99%

Pressure sensing and control of an aircraft passenger seat

Campos¹

2021

Preprint

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The premise of this work is to address aircraft seat comfort. This thesis presents the development of an automatic morphing backrest used to reduce pressure experienced by the passenger from the seat. Uncomfortable, high surface pressure zones on the backrest can be alleviated by decentralizing the occupant’s weight. The improved pressure distribution is intended to decrease discomfort during flight while taking different comfort/discomfort models into consideration. Pressure distribution data from the embedded sensor mat is used to compute the seat’s cushion deflection and corresponding backrest contour caused by the passenger’s weight. The surfaces of interest - the passenger’s back and the seat, are modelled and discretized. The discretized surface contact pressure is integrated into the hyperelastic contact model to determine the loading profile. From this, the current pressure distribution and the cushion’s surface change are computed and used in the control system to create the corresponding actuation of the surface.

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Identification of a force-sensing resistor for tactile applications

Cited by 16 publications

References 15 publications

A Novel and Inexpensive Approach for Force Sensing Based on FSR Piezocapacitance Aimed at Hysteresis Error Reduction

A Novel and Inexpensive Approach for Force Sensing Based on FSR Piezocapacitance Aimed at Hysteresis Error Reduction

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Pressure sensing and control of an aircraft passenger seat

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