Publication

Level, Downhill and Uphill Walking Identification Using Neural Networks

Kamiar Aminian, Philippe Robert
1993
Journal Articles

Abstract

Body accelerations during human walking are recorded by a portable measuring device. A new method for parameterising body accelerations is introduced. The parameters are presented to a Kohonen neural network classifier and the feasibility of identification and dissociation of level and walking on a gradient is demonstrated. The most important and original aspect of this classification is its ability to identify the gradient of walking performed in free- living conditions from walking trained on a treadmill.

Official source

https://infoscience.epfl.ch/entities/publication/6361d7ea-9e25-487b-a0f2-49cbe4c4ece7

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Kamiar Aminian, Philippe Robert
1993
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/6361d7ea-9e25-487b-a0f2-49cbe4c4ece7

About this result

Related concepts (14)

Acceleration

In mechanics, acceleration is the rate of change of the velocity of an object with respect to time. Accelerations are vector quantities (in that they have magnitude and direction). The orientation of an object's acceleration is given by the orientation of the net force acting on that object. The magnitude of an object's acceleration, as described by Newton's Second Law, is the combined effect of two causes: the net balance of all external forces acting onto that object — magnitude is directly proportional to this net resulting force; that object's mass, depending on the materials out of which it is made — magnitude is inversely proportional to the object's mass.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.