The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period. Two variations of principal component analysis (PCA) were applied to electromyography (EMG) data collected during a myoelectric task. Candid covariance-free incremental PCA (CCIPCA) detected task-specific muscle synergies with high accuracy using minimal amounts of data. Our findings offer a real-time solution towards optimizing calibration periods.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175614 | DOI Listing |
Sensors (Basel)
November 2021
College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia.
Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs.
View Article and Find Full Text PDFNeural Netw
November 2021
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA; Cognitive Science Program, Michigan State University, East Lansing, MI, 48824, USA; Neuroscience Program, Michigan State University, East Lansing, MI, 48824, USA.
Traditionally, learning speech synthesis and speech recognition were investigated as two separate tasks. This separation hinders incremental development for concurrent synthesis and recognition, where partially-learned synthesis and partially-learned recognition must help each other throughout lifelong learning. This work is a paradigm shift-we treat synthesis and recognition as two intertwined aspects of a lifelong learning agent.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2020
The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period.
View Article and Find Full Text PDFNeural Comput
November 2012
IDSIA, SUPSI, USI, Galleria 2, Manno-Lugano 6928, Switzerland.
We introduce here an incremental version of slow feature analysis (IncSFA), combining candid covariance-free incremental principal components analysis (CCIPCA) and covariance-free incremental minor components analysis (CIMCA). IncSFA's feature updating complexity is linear with respect to the input dimensionality, while batch SFA's (BSFA) updating complexity is cubic. IncSFA does not need to store, or even compute, any covariance matrices.
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