2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC) 2013
DOI: 10.1109/brc.2013.6487548
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Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array

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Cited by 17 publications
(12 citation statements)
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“…Statistical models apply probability theory to learn patterns in data and are currently more often employed in position-aware prosthesis control research than deep learning neural network alternatives [3]. Some researchers have collected EMG data across multiple limb positions to inform statistical classifiers [9], [12], [13], [19], [20], while others have added positional information (quaternions or accelerometer data) to take limb orientation into account [9]- [13]. Statistical regressors have not been as extensively explored as classifiers in device control literature [24].…”
mentioning
confidence: 99%
“…Statistical models apply probability theory to learn patterns in data and are currently more often employed in position-aware prosthesis control research than deep learning neural network alternatives [3]. Some researchers have collected EMG data across multiple limb positions to inform statistical classifiers [9], [12], [13], [19], [20], while others have added positional information (quaternions or accelerometer data) to take limb orientation into account [9]- [13]. Statistical regressors have not been as extensively explored as classifiers in device control literature [24].…”
mentioning
confidence: 99%
“…They proposed training the classifier with dynamic activities to reduce both of these effects. Boschmann and Platzner used a 96-channel highdensity electrode array and showed that training the classifier in multiple positions (three positions in their work) in combination with an increased number of EMG channels helped reduce the effect of limb position variation on classification accuracy [21].…”
Section: Introductionmentioning
confidence: 99%
“…This indicates that appropriate feature representation makes the classification task a linear problem. Hence, recent trend seems to be towards classifiers that are simple to implement, fast to train, and meet real-time constraints, such as the liner discriminant analysis (LDA) [5], [49]- [51], support vector machines (SVM) [52]- [54], Extreme learning machines (ELM) [55], hidden Markov models (HMM) [56]- [58], random forest (RF), and k-nearest neighbors (kNN). Amongst these classifiers, the LDA scheme is the most widely adopted in the field of myoelectric control.…”
Section: E Movement Intent Decodingmentioning
confidence: 99%