Residual kinetic imaging: a versatile interface for prosthetic control

Phillips, Sam L.; Craelius, William

doi:10.1017/s0263574704001298

Cited by 61 publications

(44 citation statements)

References 45 publications

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“…Although we report on only nondisabled subjects, the results may apply to the transradial amputee population, since previous studies have shown that the amplitude of single-finger volitions can by extracted via FMG [15,17]. Test conditions were limited to the arm and wrist in a fixed neutral position in the horizontal plane.…”

Section: Validity Of Studymentioning

confidence: 99%

“…This pose simulates a common working condition, and moderate departures from this posture are unlikely to significantly alter the waveform characteristics of the FMG signal. Moreover, in prosthetic applications, pronation/supination of the wrist is not an issue, and the FMG interface can adapt well to different arm conditions by calibration upon donning [14][15].…”

Section: Validity Of Studymentioning

confidence: 99%

“…The ability of FMG to resolve hand postures and individual finger motions has previously been demonstrated in transradial amputees, some of whom were >15 years postamputation and had relatively poor residua [14][15]17]. More recently, it has been shown that six different grip types can be classified with considerable accuracy when FMG is used to measure intact forearms [21].…”

Section: Taskmentioning

confidence: 99%

“…Volitions concerning the hand, such as individual finger flexions, are encoded in the forearm FMG as characteristic force images that can be readily decoded for multifinger biomimetic control of robotic and virtual hands by amputees [1,[13][14][15][16]. To date, FMG is the only neuromuscular imaging modality that has demonstrated simultaneous multifunctional and multiple degrees-of-freedom control of prosthetic hands [13][14][15]17], including multifinger operation by amputees. Previous results with amputees suggested the potential of FMG as a register of force magnitude volition [13].…”

Section: Introductionmentioning

confidence: 99%

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Pressure signature of forearm as predictor of grip force

Wininger

Kim

Craelius

2008

JRRD

111

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Abstract-We studied the relationship between grip force and external forearm pressure in nondisabled subjects using force myography (FMG). FMG uses a sensorized cuff surrounding the forearm to register the distributed mechanical force, detecting pressure on the sensors generated by the volumetric changes of the underlying musculo-tendinous complex. Each of nine nondisabled subjects donned the FMG cuff and applied grip forces to a cylindrical dynamometer; grip forces ranged from 0% to 100% of the subjects' maximum voluntary contraction. The cuff was positioned with seven force sensors located on both the anterior and posterior surfaces of the proximal forearm, but no attempt was made to match sensor placement with particular muscles or sites. Grip prediction was simply encoded as the rectified sum of the FMG sensor outputs. During grip and release cycles, the FMG waveforms of each subject correlated closely with his or her force waveforms (r > 0.89). FMG also correlated highly with the timing of grip onset and release (intraclass correlation coefficient (ICC(A,2)) = 0.99) and time to peak (ICC(A,2) = 0.91), with negligible lag. These results demonstrate that when applied to the forearm, FMG represents a grip force signature that may be useful for near-real-time proportional control of upper-limb prosthetic devices.

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Section: Validity Of Studymentioning

confidence: 99%

Section: Validity Of Studymentioning

confidence: 99%

Section: Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pressure signature of forearm as predictor of grip force

Wininger

Kim

Craelius

2008

JRRD

111

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show abstract

“…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%

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%.

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Liquid Metal Composites‐Enabled Real‐Time Hand Gesture Recognizer with Superior Recognition Speed and Accuracy

Chen,

Tao,

Chang

et al. 2024

Advanced Science

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Prosthetic hands play a vital role in restoring forearm functionality for patients who have suffered hand loss or deformity. The hand gesture intention recognition system serves as a critical component within the prosthetic hand system. However, accurately and swiftly identifying hand gesture intentions remains a challenge in existing approaches. Here, a real‐time motion intention recognition system utilizing liquid metal composite sensor bracelets is proposed. The sensor bracelet detects pressure signals generated by forearm muscle movements to recognize hand gesture intent. Leveraging the remarkable pressure sensitivity of liquid metal composites and the efficient classifier based on the optimized recognition algorithm, this system achieves an average offline and real‐time recognition accuracy of 98.2% and 92.04%, respectively, with an average recognition speed of 0.364 s. Thus, this wearable system shows advantages in superior recognition speed and accuracy. Furthermore, this system finds applications in master‐slave control of prosthetic hands in unmanned scenarios, such as electrically powered operations, space exploration, and telemedicine. The proposed system promises significant advances in next‐generation intent‐controlled prosthetic hands and robots.

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Residual kinetic imaging: a versatile interface for prosthetic control

Cited by 61 publications

References 45 publications

Pressure signature of forearm as predictor of grip force

Pressure signature of forearm as predictor of grip force

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

Liquid Metal Composites‐Enabled Real‐Time Hand Gesture Recognizer with Superior Recognition Speed and Accuracy

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