Neuro-fuzzy control of a robotic exoskeleton with EMG signals

By Kazuo Kiguchi, Takakazu Tanaka, T. Fukuda. IEEE transactions on fuzzy systems, 2004

382 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Rehabilitation

View Paper: https://www.semanticscholar.org/paper/0b437786ccb91292fa956266ce54c956268dbe59

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Dan Morris
2. Desney S. Tan
3. Kazuo Kiguchi
4. Ravin Balakrishnan
5. T. Scott Saponas
6. Yoshiaki Hayashi
7. J. Laviola
8. Chris Harrison

In Cognitive Neuroscience

1. Ravin Balakrishnan
2. J. Laviola
3. Chris Harrison
4. Yang Zhang
5. Andrew D. Wilson
6. Jefferson Y. Han

In Rehabilitation

1. Kazuo Kiguchi
2. Yoshiaki Hayashi

Citations in the corpus

• A survey on industrial applications of fuzzy control (2011, neutral): “Industrial applications of adaptive fuzzy control can be found in batch processes [206–210], robotics [58,137,211–213], aircrafts [214–217] or servo systems and electrical drives [218–221].”
• A Survey on Analysis and Design of Model-Based Fuzzy Control Systems (2006, neutral): “[88], [99], [149]; telecommunications [5], [45], [49], [131], [169], [343]; mechanical/robotic systems [8], [10], [18], [40], [102], [109], [118], [138], [139], [180], [182], [204], [247], [262], [284], [289], [292], [294], [314], [319]; automobile [16],”
• An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot (2012, background): “, the combination of adaptive neurocontrol and flexible fuzzy control) has been proposed for upper-limb exoskeleton robots [7]–[9].”
• Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces (2008, methodology): “To do this, we adopt a technique used in EEG work [11]: we divide the raw EMG signal into 250ms segments and treat each 250ms segment as a single sample of EMG data.”