Browsing by Keyword "Neural network"
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Item Classification of muscle twitch response using ANN: Application in multi-pad electrode optimization(2010) Malešević, Nebojša; Popović, Lana; Bijelić, Goran; Kvaščev, Goran; MercadoIn this paper we present a method for optimization of spatial selectivity of multi-pad electrode during transcutaneous Functional Electrical Stimulation (FES). The presented method is based on measurent of individual muscle twitches using Micro-Electro-Mechanical Systems (MEMS) accelerometers positioned on hand, while stimulating with low frequency electrical stimulation via pads within multi-pad electrode. When elicited, wrist or fingers flexion/extension produce different, characteristic wave shapes of acceleration, by using trained Artificial Neural Network (ANN) we can detect these characteristic signals and detect correlation of each pad and activated muscle beneath. Results presented in this paper show high degree of accurate classification of the elicited movement in inter-subject testing.Item Extracting muscle synergy patterns from EMG data using autoencoders(Springer Verlag, 2016) Spüler, Martin; Irastorza-Landa, Nerea; Sarasola-Sanz, Andrea; Ramos-Murguialday, Ander; Villa, Alessandro E.P.; Masulli, Paolo; Rivero, Antonio Javier Pons; Medical TechnologiesMuscle synergies can be seen as fundamental building blocks of motor control. Extracting muscle synergies from EMG data is a widely used method in motor related research. Due to the linear nature of the methods commonly used for extracting muscle synergies, those methods fail to represent agonist-antagonist muscle relationships in the extracted synergies. In this paper, we propose to use a special type of neural networks, called autoencoders, for extracting muscle synergies. Using simulated data and real EMG data, we show that autoencoders, contrary to commonly used methods, allow to capture agonist-antagonist muscle relationships, and that the autoencoder models have a significantly better fit to the data than others methods.Item A framework for adapting online prediction algorithms to outlier detection over time series(2022-11-28) Iturria, Alaiñe; Labaien, Jokin; Charramendieta, Santi; Lojo, Aizea; Del Ser, Javier; Herrera, Francisco; IAThis study introduces a novel framework that eases the adoption of any online prediction algorithm for outlier detection over time series data. The proposed framework comprises both streaming data normalization and online anomaly scoring and identification based on prediction errors. To demonstrate the utility of the proposed framework, a novel neural-network-based online time series anomaly detection algorithm called EORELM-AD is developed by implementing the steps of the proposed framework over an ensemble of online recurrent extreme learning machines. Extensive experiments on well-known benchmark datasets for time series outlier detection are presented and discussed, yielding two main conclusions. First, the performance of the proposed EORELM-AD detector is competitive in comparison to several state-of-the-art outlier detection algorithms. Second, the proposed framework is a useful tool for adapting an online time series prediction algorithm to outlier detection.Item A specific neural network used on a portable system for classifying activities in ambulatory monitoring(2006) Fourty, N.; Guiraud, D.; Fraisse, P.; Perolle, G.; Etxeberria, I.; SGOur modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people living longer at their own home, ensuring the necessary vigilance and security at the lower cost possible, some tele-assistance systems are already available commercially. This article presents a specific neural network used on a portable system for classifying activities in ambulatory monitoring. After more precisions about this specific neural network in the second part we will present some results from our prototype stemming from gerontologic institute Ingema.