Neuromuscular disorders and injuries such as cerebral palsy and stroke often result in foot-drop which can result in a person having great difficulty walking. Ankle foot orthoses (AFOs) or splints have been prescribed for many years now to limit the range of motion of the ankle, provide the patients with support and assist with rehabilitation. However the majority of AFOs require a long, labour-intensive manufacturing process which results in unacceptable waiting times for children that are rapidly growing and patients with varying conditions. This research proposes a new approach to AFO manufacturing that utilizes digital and additive manufacturing technologies to customise the fit and form to an individual. By implementing an interchangeable carbon fibre spring at the ankle joint the design will result in a stronger, more comfortable, more flexible AFO that can adaptively constrain ankle movement for various different activities. Three iterations of AFO design have been developed and tested to validate their efficacy. A custom machine has been designed and constructed in order to empirically test stiffness values for the AFO and allow for optimal AFO geometry based on input parameters. This machine has proven the structural integrity of the final AFO design. Progress has been made in automating parts of the design process which will significantly reduce labour requirements and hence manufacturing delay times.

Electromyography (EMG) based interfaces are the most common solutions for the control of robotic, orthotic, prosthetic, assistive, and rehabilitation devices, translating myoelectric activations into meaningful actions. Over the last years, a lot of emphasis has been put into the EMG based decoding of human intention, but very few studies have been carried out focusing on the continuous decoding of human motion. In this work, we present a learning scheme for the EMG based decoding of object motions in dexterous, in-hand manipulation tasks. We also study the contribution of different muscles while performing these tasks and the effect of the gender and hand size in the overall decoding accuracy. To do that, we use EMG signals derived from 16 muscle sites (8 on the hand and 8 on the forearm) from 11 different subjects and an optical motion capture system that records the object motion. The object motion decoding is formulated as a regression problem using the Random Forests methodology. Regarding feature selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 10-fold cross validation procedure is used for model assessment purposes and the feature variable importance values are calculated for each feature. This study shows that subject specific, hand specific, and object specific decoding models offer better decoding accuracy that the generic models.

The excellent compliance and large range of motion of soft actuators controlled by fluid pressure has lead to strong interest in applying devices of this type for biomimetic and human-robot interaction applications. However, in contrast to soft actuators fabricated from stretchable silicone materials, conventional technologies for position sensing are typically rigid or bulky and are not ideal for integration into soft robotic devices. Therefore, in order to facilitate the use of soft pneumatic actuators in applications where position sensing or closed loop control is required, a soft pneumatic bending actuator with an integrated carbon nanotube position sensor has been developed. The integrated carbon nanotube position sensor presented in this work is flexible and well suited to measuring the large displacements frequently encountered in soft robotics. The sensor is produced by a simple soft lithography process during the fabrication of the soft pneumatic actuator, with a greater than 30% resistance change between the relaxed state and the maximum displacement position. It is anticipated that integrated resistive position sensors using a similar design will be useful in a wide range of soft robotic systems.

Ionic polymer metal composites (IPMCs) are actuators that lend themselves well to microfluidic applications due to their lightweight, flexibility, ability to tailor their geometry, as well as the capability to be miniaturized and implanted into microelectro-mechanical systems devices. The major issue with implementing IPMCs into such devices is the ability to control their actuation and, hence, their reliability over a long period of time. This paper presents a novel iterative feedback tuning (IFT) algorithm that tunes the system online using experimental data during normal system operation. The controller adaptively tunes the highly nonlinear and time varying IPMC for a newly proposed micropump. This demonstrates the ability of the system to have a reliable performance over a long period of time without the need of any offline tuning or system identification. The system was run for 20 controller updates. This corresponds to 10 and 20 min of operation for the 0.1 and 0.05 Hz reference inputs, respectively. 100 and 300 μm amplitudes were tested to demonstrate the ability of the system to adaptively tune to different input signals. Experimental results show the newly proposed IFT algorithm has successfully tuned the controller to achieve up to 92% better performance when compared with a conventional model-based tuned controller.