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Handheld haptic devices are often limited in rendering capability, as compared to traditional grounded devices. Strenuous design criteria on weight, size, power consumption, and the ungrounded nature of handheld devices, can drive designers to prioritize actuator force or torque production over other components of dynamic range like bandwidth, transparency, and the range of stable impedances. Hybrid actuation, the use of passive and active actuators together, has the potential to increase the dynamic range of handheld haptic devices due to the large passive torque capability, the stabilizing effects of passive actuators, the high bandwidth of conventional DC servomotors, and the synergy between actuators. However, to date the use of hybrid actuation has been limited due to the highly nonlinear torque characteristics of available passive actuators that result in poor rendering accuracy. This paper describes a hybrid actuation approach and novel control topology which aims to solve actuation challenges associated with nonlinear passive actuators in hybrid and handheld haptic devices. The performance of the device is assessed experimentally, and the approach is compared to existing handheld devices. 

Hybrid actuation approaches for haptic interfaces generally suffer from asymmetry in active and passive torque capabilities. This paper describes the design of a high-performance balanced hybrid haptic device, which addresses the asymmetry by combining a high-power, low-impedance active compliant actuation (series-elastic actuator) with energy absorbing high-force passive actuation in parallel with a fast, low-power secondary active actuation. We describe the actuation, design and control approaches and experimentally validate the approach with a one degree-of-freedom testbed. The performance is compared with active only approach and results show significant improvements in stability and rendering range of the device. 

Abstract We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test, and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects.