Mitigating Threshold Effects in Human Control by Stochastic Resonance With Fractional Colored Noise

In industrial applications, mechanical and physiological thresholds may limit the capability of human manipulating machine via control devices, such as joysticks and steering wheels. These thresholds can result in loss of information in the control signals that are kept below the threshold of detection of the device or the human operator. One approach to mitigate these effects is stochastic resonance, i.e., by injecting additive noise into a signal to raise its energy content over the threshold of detection. Though this noise partially corrupts the signal, it can increase the detectability of the signal by the control device. This article provides, for the first time, research towards using stochastic resonance to improve human performance in control tasks. In particular, it shows that using adaptive colored noise can improve the detectability of the steering control signals recorded from human participants. The approach converts a signal processing task to an optimization problem, where particle swarm optimization is employed to obtain the optimal color (or spectral exponent) of the injected additive noise, generated through an intelligent technique with fractional order filters. The results have shown that the proposed method improves the detectability of subthreshold steering control signals. This method can be widely applicable to other industrial domains, such as energy harvesting and enhancing sensory perception.