Abstract
Due to recent heart attacks on humans, it is necessary to predict heart graphs of humans during running positions. On the other hand, hip and knee joints should be analyzed to predict walking and running conditions. Therefore, in this experimental works, hip, knee, and heart attacks are analyzed in experimentally. After experimental measurement, a proposed neural network is employed to predict hip, knee, and heart attack behavior of humans with walking and running stages.
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- b :
-
Recovery rate of chemical energy per unit mass
- b j :
-
Bias of the jth neuron in the hidden layer
- b k :
-
Bias of the kth neurons in the output layer
- c j :
-
Center of each hidden unit
- d :
-
Distal end of link
- e :
-
Actual reserves of chemical energy per unit mass
- F j :
-
Joint force
- f o :
-
Maximum propulsive per unit mass
- g :
-
Gravitational acceleration
- h :
-
Maximal vertical displacement
- I :
-
Moment of inertia
- m :
-
Mass of human body
- N :
-
Iteration number
- n I :
-
Number of neurons in the input layer
- n H :
-
Number of neurons in the hidden layer
- n O :
-
Number of neurons in the output layer
- P :
-
Electrical potential
- p :
-
Proximal end of link
- r j :
-
Radius vector of the jth hidden unit
- t :
-
Time
- T :
-
Joint torque
- v j :
-
Velocity of joint center
- \( \dot{\theta } \) :
-
Angular velocity
- α :
-
Momentum term
- μ :
-
Efficiency of transforming the chemical energy
- η :
-
Propulsive force setting parameter
- λ:
-
Learning rate
- τ :
-
Damping coefficient constant
- ϑ :
-
Speed of runner
- φ :
-
Monotonic function
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Acknowledgments
This research results consisted of a part of project FBA-10-2916. The authors wish to express their thanks to Erciyes University for supporting this project.
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Yildirim, Ş., Eski, İ. & Polat, Y. Design of adaptive neural predictor for failure analysis on hip and knee joints of humans. Neural Comput & Applic 23, 73–87 (2013). https://doi.org/10.1007/s00521-012-1211-z
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DOI: https://doi.org/10.1007/s00521-012-1211-z