Previous methods have used pattern recognition techniques to identify categories of jaw movement [1, 2], but there are no developments on continuous movement prediction, even with a single degree of freedom. The physiological model of the masticatory system was therefore built from the ground up using the principles that were developed for the elbow model and is the first of its kind. The model is able to utilise the superficial mandibular muscles to predict continuous jaw motion with two DOFs, solely from the EMG signal activities of the muscles. The physiological model has a distinctive musculoskeletal component that has independent DOFs based on accessible EMG signal locations. The model can identify both vertical and lateral movement trajectories with average correlation coefficients as high as 0.74 for vertical movements. The physiological model of the masticatory system may be relevant to other multiple DOF joint systems, and it remains to be seen whether a similar approach of carefully considered model development depends on surface EMG signal accessibility and DOF identification.

The hybrid model consists of a multiple DOF physiological model that has independent DOFs and an artificial neural network. This is the first implementation of a model that aims to maintain continuous movement prediction and movement type recognition, using only EMG signal inputs. Existing pattern recognition techniques are only able to offer discrete states or classes of movement [3, 4], while during development, physiological models were found to be significantly affected by the multiple roles a single muscle plays in movements. The effect was an average reduction of 15.6 % in the estimated position RMSE of the EMG-driven multiple DOF physiological model of the masticatory system where the performance of the physiological model by itself was compared to the performance of the physiological model and ANN combination in the hybrid model. The hybrid model therefore provides the structure and approach towards developing more sophisticated models that are capable of handling more complex movements.

The structure and function of the mandibular muscles has been thoroughly established in the available literature [5, 6]. Numerous EMG signal studies have also been conducted on the mandibular muscles to characterise them and understand their roles in mandibular movements and function [7–9]. To obtain a complete understanding of the masticatory system, these studies have involved both superficial and deep mandibular muscles [10]. The study of the superficial mandibular muscles found that the contributions of the masseters were in closing the jaw, and this can be supported by the existing literature. The study also verified that digastric activity could be identified under the chin, with the limited amount of space for bipolar electrode placement, and used to predict jaw opening. The most significant revelation of the study was the active role the anterior temporalis muscles play in lateral movements. Through ANOVA, it was shown that the temporalis muscles could be used to predict lateral movements. These findings were used to define the musculoskeletal component of the physiological model of the masticatory system, and it is only because of this study that the two DOFs of movement were identified and implemented with independent muscle actuation groups. The study therefore revealed characteristics of the mandibular muscles from a different perspective and also serves as an example that can be applied to other multiple DOF joint systems for DOF identification and musculoskeletal model development.

In order to determine the potential issues of using a spherical wrist mechanism for the shoulder complex of an exoskeleton robot, an analysis of the 3R mechanisms used in the majority of past exoskeleton designs was carried out. This investigation highlighted major problems associated with singularity and mechanical interference of the mechanism with the user which restrict the exoskeleton’s ability to operate in the shoulder workspace. These issues are caused by the exceptionally large range of motion of the human shoulder joint and are particularly challenging to solve. Operating the mechanism at a singular configuration causes 1-DOF to be lost, making the exoskeleton unable to generate shoulder rotations about the lost axis. Furthermore, the mechanism’s ability to rotate about this axis degrades as the mechanism approaches the singular configuration. Exoskeletons developed in the past that have considered singularity issues have only attempted to shift the singular configurations to regions of the workspace that are less commonly used by the shoulder and have not considered the reduction in performance near these configurations at all. For the exoskeleton designs with shifted singular configurations, analysis of mechanical interference between the exoskeleton and the user has revealed that parts of the 3R mechanism are required to operate dangerously close to the user’s body in order to reach some regions of the shoulder workspace. Exoskeleton designs that have not shifted the singular configuration cannot raise the upper arm above the horizontal position at all as this will cause the 3R mechanism to collide with the user. In order to overcome both the singularity and mechanical interference problems, the use of a redundant 4R spherical wrist mechanism is proposed for use in the shoulder complex of an exoskeleton robot.

Previous interfacing methods were only able to identify discrete classes of movement after long training periods [11, 12], which limits the flexibility of an interface and restricts the versatility of the predicted movements. Some research groups have attempted to provide continuous profiles, but have resorted to enhancing the EMG signal by adding weight to the user’s arm [13, 14], or employing additional sensors [15, 16]. This is unsuitable, particularly in applications with less-abled users. Other research groups have limited movement to the horizontal plane, where there are fewer forces to consider and the modelling of the system is easier, but the movements have less practical implications [17, 18]. Therefore, a physiological model that only uses the EMG signal and can identify continuous movement profiles of the elbow in the sagittal plane needed to be developed. The proposed EMG-driven physiological model for the elbow joint fills these gaps by using a unique neuromusculoskeletal structure of the elbow joint. The model consists of musculotendon, musculoskeletal and kinematic components and was able to predict the random movement of multiple subjects with a 22.4° RMSE average. This performance is comparable with previous works that focused on less complex movement trajectories [19–21]. The model’s ability to handle novel movements will make it an integral element of neuromuscular interfacing approach provided that model parameters are appropriately identified and tuned.

It had been widely reported in literature that the kinematics of human ankle is complex and can vary significantly between individuals. However, since such kinematic information can be used in the adaptation of the robot’s behaviour as auxiliary variables that can be processed to adjust the robot controller parameters, this research had proposed a new online estimation algorithm based on the recursive least square filter to identify a subject-specific description of the ankle kinematics. It should be noted that while studies had been carried out to identify the kinematic parameters of a biaxial ankle kinematic model [22–24], such works had mainly utilised offline nonlinear least square methods. The proposed online estimation algorithm, driven by the need for it to be used in real-time control scheme, is therefore a new development in ankle kinematic parameter identification.

Due to its relatively simple structure, the biaxial ankle model had been identified as an ankle kinematic model that is well suited for online parameter identification. Two different online identification techniques, the extended Kalman filter (EKF) and the least mean square (LMS) method, had been considered for parameter estimation of the biaxial ankle model. A simulation-based comparison of the EKF and LMS approaches involving an ideal biaxial ankle model had shown that an EKF acting in the capacity of a recursive least square (RLS) estimator has the ability to produce the best estimation results in terms of estimation accuracy and parameter convergence. This approach had therefore been used in subsequent investigations done in this research.

As numerous studies had found the orientations of the ankle and subtalar axes in the biaxial ankle model to vary with foot orientation [25–28], the conventional biaxial ankle model with constant axis tilt angles had been extended in this research. The extension essentially allows the axis tilt angles of the biaxial model to vary with the foot configuration, and two variants of this extension had been proposed. The first version allows the axis tilt angles to vary linearly with respect to the ankle and subtalar joint displacements, while the second version varies the tilt angles according to the X and Y Euler angles used to describe the foot orientation. The feasibility of using the conventional biaxial ankle model and both the extended biaxial ankle models (with different axis tilt angle dependencies) in the RLS algorithm was also tested in a simulation study that utilised the first extended biaxial ankle model to generate the training data.

A computational ankle model in the form of a state space model had been derived in this research to facilitate the development and simulation of the robot control scheme. A three segmented rigid body model had been proposed together with the biaxial ankle kinematic constraint to reduce the computational complexity of the model, thus making it more suitable for controller simulations. Ligaments and muscle–tendon units had also been included in the model as force elements to allow monitoring of the tensions along these elements.

The developed model was validated through simulation studies involving both passive and active motions of the foot. It was found that the moment–displacement characteristic of the simulated foot along the flexion direction is largely in agreement with what is reported in literature, where larger ankle stiffness is found in the dorsiflexion direction. Simulations involving active muscular contractions also showed that activation of different muscle groups produced motion in the expected direction of motion, while also achieving movements within the typical motion limits of the human ankle. Data from an experimental trial had also been used to evaluate the validity of the developed computational model by feeding the moments computed from load cell measurements of the robot into the developed ankle model. Comparison of the simulated and actual ankle motion showed both motion trajectories to be similar in a qualitative sense for the pitch and roll directions, thus suggesting that a non-subject-specific ankle model can still be used to obtain a reasonable description of the ankle behaviour.

The computational ankle model developed in this research had been utilised in various ways to facilitate the design and implementation of the ankle rehabilitation robot. One such application is in the generation of rehabilitation trajectories. Such a problem had been studied in this research, and an optimisation-based rehabilitation trajectory generation routine which aims to minimise forces on ligaments/tendons as well as joint reaction moments had been proposed. In this context, the computational ankle model is used as a means for estimating the force and moment quantities considered in the objective function for a particular foot motion trajectory. It was found through simulation that the optimisation approach is capable of generating a trajectory with lower objective function value than the path of minimal distance between the desired start and end points. This suggests that the optimisation-based approach can potentially be used to tailor the rehabilitation trajectory according to a patient’s specific condition.

In addition to trajectory generation, the developed ankle model had also been used to facilitate the development of the interaction control scheme used in this research. Apart from its obvious use in controller simulation, it had also been used to provide the ankle stiffness properties for stability analysis of the actuator force controller. Additionally, the ankle stiffness matrices derived from the model were also used to generate a lookup table for the robot impedance adaptation scheme.

The implementation of the physiological model of the elbow joint on prototypal hardware platforms is the first step towards a physical interface between a human operator and robotic device, such as an exoskeleton or prosthesis. Such a physical interface does not exist, and there are limited existing approaches [13, 29, 30]. The neuromuscular interface uses physiological modelling principles, a modular architecture and unique communication protocols to acquire, filter and process the EMG signals. The interface was used by several subjects to successfully manipulate a single degree of freedom joint in real time, with RMSE as low as 13°, affirming the feasibility of the NI concept. The performance is comparable to that of existing exoskeletal interfaces [18, 21], and this has resulted in the filing of a provisional patent. Continued developments seek to improve the signal processing, hardware design and manufacturability, to further the commercial viability of the interface.

The proposed 4R spherical wrist mechanism is kinematically redundant, and consequently, a range of designs are possible which can achieve the entire shoulder workspace with adequate performance. In addition, the redundant 4R mechanism has infinite inverse kinematic solutions for achieving a given shoulder position. Thus, the NSGA II multi-objective optimisation algorithm was used to identify an optimal 4R design and the optimal operating configurations of the mechanism. The variables defined in the optimisation problem are used to determine the position of the base joint, the angle sizes of the three links in the 4R mechanism and the optimal operating configurations of the mechanism. The optimisation objectives are formulated with considerations for singularity, reachable workspace, interference and joint transition feasibility throughout the workspace. Analysis of performance was done for 89 uniformly distributed upper arm positions in the semi-spherical shoulder workspace. Singularity analysis was performed using the condition number of the mechanism’s configuration. To measure a 4R design’s singularity performance, the GCN was used to determine the average performance over the shoulder workspace and CN_max was used to determine the closest 4R mechanism that will operate to a singular configuration. The 4R mechanism’s ability to reach the entire shoulder workspace is guaranteed. Mechanical interference between the 4R mechanism and its components or the user was carefully analysed and prevented. The movements of the 4R mechanism’s joints were minimised to ensure they can transition feasibly and with minimal velocity throughout the shoulder workspace. An attempt was made to match the drift in the range of motion of the 4R mechanism joint that controls shoulder axial rotation to the drift in the range of motion of human shoulder axial rotation throughout the workspace.

The optimised 4R spherical wrist mechanism has been used in the design of a 5-DOF upper limb exoskeleton system. This exoskeleton is capable of performing 3-DOF spherical movements of the shoulder joint and 1-DOF movement of the elbow joint through the entire range of motion of a healthy adult’s limb. Numerous considerations were made to ensure the exoskeleton design can achieve the required range of motion with adequate performance, is feasible to develop with the available resources and is user friendly. These considerations include size, weight, range of motion, velocity, torque, mechanical interference, cost, fabrication time, joint alignment, customisation for different users, options for future upgrades, ease of use, safety and noise pollution. The exoskeleton is supported by a mechanically grounded frame behind the user so that the user does not carry the weight of the exoskeleton but is instead supported by it. Length adjustments can be made to the exoskeleton structure to fit users with different arm lengths. The exoskeleton joint that controls shoulder axial rotation is implemented using a 120° curved rail encompassing the side of the upper arm which makes the exoskeleton easy to don and prevents mechanical interference issues. Each of the five joints in the exoskeleton is actuated by a compact brushless DC motor in combination with a reduction gearbox directly mounted onto the joints. Position feedback for the five exoskeleton joints is provided by rotary magnetic encoders, and force feedback is achieved using low-cost custom-built strain gauge force sensors at the hand and upper arm HRI. Numerous safety features are incorporated into the exoskeleton including mechanical stoppers to physically prevent the joints from extending beyond their standard range of motion, software to monitor the joint positions via the magnetic encoders and prevent the motor from driving the joint beyond the range of motion limits and emergency stop buttons to allow the exoskeleton user or an external user to manually stop the exoskeleton. Simulations confirm the complete exoskeleton system does not have mechanical interference issues when operating within the human workspace. A GUI has been developed for operating the exoskeleton.

A new parallel robot had been proposed and developed in this research for the rehabilitation of ankle sprain injuries. While there are several designs of platform-based robots used in ankle rehabilitation, the end effectors of these devices are typically constrained to rotate about a pivot which does not coincide with the human ankle’s effective centre of rotation [31–35]. The design of this new robot therefore differs from most of these existing solutions in the sense that it is underactuated when the user’s lower limb is not attached to the robot. This means that when in use, the user’s lower limb will form part of the robot’s kinematic constraint, thus ensuring that the motion performed by the robot is in line with the natural ankle–foot movements. Additionally, since movement of the shank is kept to a minimal level during operation, the robot can also estimate the relative orientation of the foot with respect to the shank with greater precision, thus allowing it to provide more repeatable rehabilitation movements and serve as a better measurement tool for evaluation of the ankle’s mechanical characteristics.

The design of the device was based on workspace, singularity and force analyses which were, respectively, carried out to ascertain the motion limits of the robot, the controllability of the robot within its motion limits and the actuator forces required by the robot to produce the desired robot–foot interaction moments. By treating the ankle as a spherical joint capable of three degree of freedom rotational motion, it was found that singular regions will exist in the robot’s reachable workspace if only three actuated links are used in the mechanism. More specifically, the reachable workspace will be bisected by a “surface” of orientations with ill-conditioned manipulator Jacobians, thus making the robot more difficult to control. The remedy to this issue was found through the use of a redundantly actuated mechanism by including an additional actuating link to the mechanism, which successfully eliminated these regions of large condition number in the robot’s reachable workspace. In addition to the elimination of singularity in the workspace, the redundant actuation degree of freedom is also exploited in this work to allow regulation of the total vertical load applied to the ankle. To the best of the author’s knowledge, this feature is not yet available in existing ankle rehabilitation devices, but can be beneficial to the treatment of ankle sprains by allowing emulation of different levels of weight bearing to suit the needs of the user.

Further analyses were also conducted to investigate the impact of uncertain ankle joint centre locations on the robot’s capability to satisfy the design requirements. It was found that despite the application of conservative approaches in the workspace and singularity analyses, the desired range of motion and moment requirements are still largely satisfied by the proposed design. Additionally, it was also confirmed that this can be achieved with a good level of manipulability within the reachable workspace. The uncertainty in the ankle joint centre also presents a challenging problem for the control of the proposed ankle rehabilitation robot, particularly in the estimation of a correct foot configuration. A solution to this issue was proposed in this research through the use of redundant sensing which utilises additional pitch and roll measurements of the end effector to fully resolve the end-effector configuration of the robot.

In controlling the position of an exoskeleton, it is important to consider the velocities the joints move with in travelling to a target position to ensure the movement is smooth, comfortable and similar to normal human motion. It has been identified that typical human movements follow a minimum jerk trajectory where the rate of change in acceleration (jerk) is minimised throughout the trajectory. A trajectory planning algorithm based on the minimum jerk criterion has been developed for the joints in the exoskeleton. This requires an input of the target positions for the joints and the desired time for the joints to arrive at those positions. Implementation of this is fairly straightforward for the 1-DOF elbow joint since the movements of the exoskeleton joint translate directly to identical movements in the user’s elbow joint. However, movements of the joints in the exoskeleton’s spherical wrist shoulder mechanism have a nonlinear relationship with the movements of the user’s shoulder joint. In other words, a minimum jerk trajectory of the joints in the 4R mechanism does not generate a minimum jerk trajectory for the user’s shoulder. Therefore, the minimum jerk trajectory is determined for the shoulder movement which is then converted to the respective trajectories of the 4R joints.