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In human movement, the large number of system degrees of freedom at different levels of analysis of the system, joints, muscles, motor units, cells etc, naturally affords complexity and adaptability in action. It also leads to variability in movement and its outcome, even in intentional efforts to reproduce the same movement or action goal. An example is continuous isometric force output to a constant force level where the amount and structure of force variability changes with information available, force level and individual differences. In this paper we model the control of isometric force production with piece-wise linear stochastic maps of multiple time scales. At the core of our model is a piecewise linear function, depending on three parameters that can be estimated from the observed data that is perturbed by additive Gaussian noise at a given level. The result of the stochastic forcing is that outside of a threshold interval the system behaves like a discrete Ornstein-Uhlenbeck process and inside it performs a Brownian motion. The model is shown to simulate the basic findings of the structure of human force variability that decreasing variability is correlated with increased dynamical complexity as measured with the "Approximate Entropy (ApEn)" statistic.

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

Now growing at a rate of over 5% per annum, the $3 billion ‘alternative health therapies’ business is now positioned in the top ten growth industries in Australia. With poor regulation of both therapeutic goods and the unregistered therapists who promote them, cancer patients may well be putting their health at risk when they place their faith in many so-called ‘natural’ or ‘traditional’ treatments. With a focus on what complementary therapists refer to as ‘energy medicine’ and ‘nutritional medicine’, this chapter explores the risks and benefits of some of the more popular alternative health-care choices. While investigating their histories, it outlines what influences cancer patients to try these unproven therapies, and the conflict and contrast in information relating to the claims made for them and the conclusions of evidence-based research. Although there are a number of complementary therapies that are of benefit to some patients, both during and after their cancer treatments, ‘natural’ does not always equal ‘safe’, may be expensive and may even compromise their health. More patients now want a greater say in their choices of treatment, and selecting complementary therapies that may help is another of the many challenges faced in trying to make informed choices, as we navigate along our individual roads on our journeys to recovery.