Ypma, A. (1995) Neural control of artificial human walking. Master's Thesis / Essay, Artificial Intelligence.
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Abstract
The ultimate goal of much research in biomedical engineering is the construction of an artificial walking system, i.e. a system which enables paraplegics to walk again, using Functional Electrical Stimulation (FES) and biofeedback. Infotronic at Hengelo (NL) produces a system for measurement of certain body signals that originate from human walking, UltraFlex, which is subsequently used to acquire data from normal human walking. We try to obtain a solution for the inverse dynamics problem in human walking by means of neural networks. Once a network has learned the right instantaneous mapping, it has to be extended to make time—lagged mappings, i.e. to predict future muscle activation on the basis of past activation and movement information. A neural network is expected to predict the human EMG signal better than classical statistical predictors. This advantage will become even auspicious when EMG arising with FES has to be predicted: muscle fatigue and disturbances cause a non—stationary signal the predictor has to adapt to. A major item in neural system design is the particular choice of network dimension and data preprocessing that leads to a satisfactory solution for the problem at hand. We propose a structured approach based on the relation between certain signal characteristics and network architecture. A relation is found between a signal's correlation time and its generalization performance with tapped delay—lines of a certain dimension. This criterion and two benchmark criteria are applied in the prediction of the human EMG. For a synthetic EMG—equivalent, no conclusions can be drawn w.r.t. the suitability of all three design criteria (whether prediction is performed using conventional or neural techniques). For a real segment of EMG, a predictor order somewhere in the middle of correlation time and Final Prediction Error values seems suitable, especially when statistical and movement features are added. Hence, the suitability of the correlation time criterion can be doubted for complexer signals exhibiting nonlinearities (like the EMG). Ultimately, a neural 400—lag predictor is obtained, that tracks the "amount of muscle activity" conveniently, whereas linear predictors of comparable order show poor tracking performance.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Artificial Intelligence |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:29 |
| Last Modified: | 15 Feb 2018 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8805 |
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