Naturally Supervised Learning in Manipulable Technologies

The relationship between physiological systems and modern electromechanical technologies is fast becoming intimate with high degrees of complex interaction. It can be argued that muscular function, limb movements, and touch perception serve supervisory functions for movement control in motion and touch-based (e.g. manipulable) devices/interfaces and humanmachine interfaces in general. To get at this hypothesis requires the use of novel techniques and analyses which demonstrate the multifaceted and regulatory role of adaptive physiological processes in these interactions. Neuromechanics is an approach that unifies the role of physiological function, motor performance, and environmental effects in determining human performance. A neuromechanical perspective will be used to explain the effect of environmental fluctuations on supervisory mechanisms, which leads to adaptive physiological responses. Three experiments are presented using two different types of virtual environment that allowed for selective switching between two sets of environmental forces. This switching was done in various ways to maximize the variety of results. Electromyography (EMG) and kinematic information contributed to the development of human performance-related measures. Both descriptive and specialized analyses were conducted: peak amplitude analysis, loop trace analysis, and the analysis of unmatched muscle power. Results presented here provide a window into performance under a range of conditions. These analyses also demonstrated myriad consequences for force-related fluctuations on dynamic physiological regulation. The findings presented here could be applied to the dynamic control of touch-based and movement-sensitive human-machine systems. In particular, the design of systems such as human-robotic systems, touch screen devices, and rehabilitative technologies could benefit from this research. INTRODUCTION In the last few years, there have been major advances in the commercial availability of touch-based and motion-driven devices. Devices such as the iPad, Nintendo Wii, Microsoft Kinect, and other applications have required a new way of thinking about usability and ergonomic design. Parallel developments in the area of brain-computer interfaces [1, 2], neurorehabilitation [3, 4], and myoelectric control [5] may provide clues to this issue, but have not yet become a key component of human factors research. In this paper, I will demonstrate how an approach called neuromechanics [6, 7] can be brought to bear on assessing the usability of such technologies. Neuromechanics is an approach that unifies the complexity of behavior with neurobiological outputs by looking at interactions between muscle activity and movement behavior. Examples include dynamical control strategies due to limb geometry [8] and neural mechanisms [9], which can be characterized as morphological and neural control strategies, respectively. Neuromechanical control as supervised learning The human neuromechanical system can be understood as a complex control system which can adapt to environmental stimuli. Experiments in motor learning [10] and computational