A Proposal to Classify Ways of Walking Patterns Using Spiking Neural Networks

Mares, Karen Fabiola; Baltazar, Rosario; Casillas, Miguel Ángel; Zamudio, Víctor; Lemus, Lenin

doi:10.1007/978-3-319-71008-2_8

Cited by 2 publications

(1 citation statement)

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“…In [16], SNN model is used to model balance brain interface using EEG pattern data. Author in [17] applied SNN model to recognizes different types walking pattern. The evaluation of SNN model also able to classify motor imagery EEG signals [18] due to robustness in pattern recognition compared to other classifiers like Linear Discriminant, Feed Forward Neural Network and Radial Basis Function.…”

Section: Related Workmentioning

confidence: 99%

Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network

Suriani¹,

Rashid²

2021

IJMLC

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Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.

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Section: Related Workmentioning

confidence: 99%