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Figure 7 | Journal of NeuroEngineering and Rehabilitation

Figure 7

From: A Dynamic Neuro-Fuzzy Model Providing Bio-State Estimation and Prognosis Prediction for Wearable Intelligent Assistants

Figure 7

Example simulation of the "adaptive model" of Table 2. For this simulation, the following four simple rules were used, one for each state: IF WeightSession is intense & (Diet is good & Hypertrophy is Low) THEN Hypertrophy is high & higher IF (GenActLevel is low & AerobicSess is not intense) or Injury is bad THEN Atrophy is high & higher IF WeightSession is intense & AerobicSess is not intense & FiberComp is low THEN Fibercomp is high & higher IF Hypertrophy is high & (WeightSession is not intense & Diet is not good & AerobicSess not intense THEN MuscMass is high & higher Since only one input, state, rule, etc can be shown in an image (user can easily toggle between them), others are described here. At the start the client has states that reflect a sedentary lifestyle. Inputs reflect that he gradually increases his general activity level (this is the input that happens to be shown), improves his diet, and starts a weight-training program. This continues for three weeks through the end of February, at which time he stops the weight training and starts an aerobic training program. However, on his fourth aerobic event, he gets injured and his activity decreases. The hypertrophy and atrophy states are viewed as bioprocesses that are always somewhat present, and compete with each other. Of the four states, the hypertrophy state is shown (lower left), and we see an initial rise and a subsequent mild effect of each weight training session. After these inputs stop the state falls a bit. The atrophy state follows the shape of the atrophy rule, which is shown (upper right). Notice that with increases in various activities, atrophy rule firing decreases until the injury occurs. The output (predicted strength) is assumed a weighted function of all states, and the "outcome" Fmax (which could have also been viewed as an output) is a weighted function of the predicted strength and some of the states. Both show increases with these lifestyle changes, then the start of a decrease after the injury.

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