Neuromuscular Analysis as a Guideline in designing Shared Control

The challenges in designing human-machine interaction have been around for decades: how to combine the intelligence and creativity of humans with the precision and strength of machines? It is well known that manual control tasks are prone to human errors. The conventional engineering solution is to either fully automate a (sub)task or to support the human with alerting systems. Both approaches have inherent limitations, widely described in literature (e.g., Pritchett, 2001; Sheridan, 2002). Recently, an alternative solution is receiving increased attention: that of shared control. In the shared control paradigm, an intelligent system continually shares the control authority with the human controller. The idea behind shared control is to keep the human operator in the direct manual control loop, while providing continuous support. Shared control has been investigated for a wide range of applications, for example during the direct control of automobiles (e.g., Griffiths & Gillespie, 2005; Mulder et al., 2008a&b) and aircraft (e.g., Goodrich et al., 2008), or during tele-operated control to support gripping (Griffin et al., 2005), surgery (e.g, Kragic et al., 2005), micro-assembly (e.g, Basdogan et al., 2007) or the steering of unmanned aerial vehicles (e.g., Mung et al., 2009).