Human balance augmentation via a supernumerary robotic tail.

Humans are intrinsically unstable in quiet stance from a rigid body system viewpoint; however, they maintain balance thanks to neuro-muscular sensory properties whilst still exhibiting postural sway characteristics. This work intro-duces a one-degree-of-freedom supernumerary tail for balance augmentation in the sagittal plane to negate anterior-posterior postural sway. Simulations showed that the tail could success-fully balance a human with impaired ankle stiffness and neural control. Insights into tail design and control were made; namely, to minimise muscular load the tail must have a significant component in the direction of the muscle, mounting location of the tail is significant in maximising inertial properties for balance augmentation and that adaptive control of the tail will be best suited for different loads held by a wearer.

Estimation of Tool-Tissue Forces in Robot-Assisted Minimally Invasive Surgery Using Neural Networks.

A new algorithm is proposed to estimate the tool-tissue force interaction in robot-assisted minimally invasive surgery which does not require the use of external force sensing. The proposed method utilizes the current of the motors of the surgical instrument and neural network methods to estimate the force interaction. Offline and online testing is conducted to assess the feasibility of the developed algorithm. Results showed that the developed method has promise in allowing online estimation of tool-tissue force and could thus enable haptic feedback in robotic surgery to be provided.