Mechanical Engineering - Research Publications
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ItemNo Preview AvailableOn Active Disturbance Rejection Control for Unmanned Tracked Ground Vehicles with Nonsmooth Disturbances(WORLD SCIENTIFIC PUBL CO PTE LTD, 2023-01-01)This paper proposes robust controllers for a class of unmanned tracked ground vehicles (UTGVs), which are built to autonomously clean carryback or spillage from the conveyor belts used in the mining industry. The UTGV, a nonholonomic system in its nature, needs to follow a given path in a harsh environment with large uncertainties due to the time-varying mass and inertia when the UTGV loads and unloads as well as unknown frictions and flatness of the ground. Moreover, the input constraints coming from motors do exist. It is usually hard to design robust controllers for such complex systems. By utilizing the available autonomous driving system, which is designed to be compatible with the existing remote motion controller in unmanned systems to generate autonomous ability, this paper uses the off-the-shelf motion planner to calculate desired linear and angular velocities based on the given path and sensor perceptions. Consequently, the control design can be simplified as two decoupled linear time-invariant scalar dynamic systems with uncertainties, making the active disturbance rejection controller (ADRC) applicable. By carefully designing the parameters of ADRC with the help of an extended state observer (ESO), it is shown that the proposed ADRC and ESO can achieve good tracking performance in the presence of input saturation and can handle nonsmooth disturbances. The proposed simulation results and experimental results support the theoretical findings.
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ItemFrequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces(FRONTIERS MEDIA SA, 2022-12-21)OBJECTIVE: Multi-frequency steady-state visual evoked potential (SSVEP) stimulation and decoding methods enable the representation of a large number of visual targets in brain-computer interfaces (BCIs). However, unlike traditional single-frequency SSVEP, multi-frequency SSVEP is not yet widely used. One of the key reasons is that the redundancy in the input options requires an additional selection process to define an effective set of frequencies for the interface. This study investigates systematic frequency set selection methods. METHODS: An optimization strategy based on the analysis of the frequency components in the resulting multi-frequency SSVEP is proposed, investigated and compared to existing methods, which are constructed based on the analysis of the stimulation (input) signals. We hypothesized that minimizing the occurrence of common sums in the multi-frequency SSVEP improves the performance of the interface, and that selection by pairs further increases the accuracy compared to selection by frequencies. An experiment with 12 participants was conducted to validate the hypotheses. RESULTS: Our results demonstrated a statistically significant improvement in decoding accuracy with the proposed optimization strategy based on multi-frequency SSVEP features compared to conventional techniques. Both hypotheses were validated by the experiments. CONCLUSION: Performing selection by pairs and minimizing the number of common sums in selection by pairs are effective ways to select suitable frequency sets that improve multi-frequency SSVEP-based BCI accuracies. SIGNIFICANCE: This study provides guidance on frequency set selection in multi-frequency SSVEP. The proposed method in this study shows significant improvement in BCI performance (decoding accuracy) compared to existing methods in the literature.
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ItemNo Preview AvailableInvestigating User Volitional Influence on Step Length in Powered Exoskeleton Designed for Users with SCI(IEEE, 2022-01-01)Volitional movement from users of assistive lower limb exoskeletons may be exploited to increase the controlled variability in the movements of a human-exoskeleton system. This may in turn allow these devices to handle the variability encountered in the terrain of everyday life. This study aimed to investigate the degree to which users can volitionally influence step length, when using an assistive exoskeleton designed for users with spinal cord injury (SCI) running a fixed robotic exoskeleton trajectory. An experiment was conducted to investigate the accessible range of step lengths when five able-bodied participants and one participant with SCI piloted a user-balanced exoskeleton. Participants were asked to take steps as large as possible ("large") and as small as possible ("small"), with the able-bodied individuals asked to minimise use of their leg muscles, with step length of each step measured. Surface electromyography (sEMG) data were collected on major leg muscles of the able-bodied subjects to monitor their muscle activities with a novel processing method introduced to facilitate discussion in the context of users with SCI. The results demonstrate that a user can intentionally manipulate the resulting step length, with every participant having significantly different large and small step sizes (p < 0.05). However, large variations were observed between individuals in terms of absolute step lengths and difference between large and small steps. Moreover, the range of step length (normalised by the leg length) ranged from 0.237 to 0.375 for the able-bodied subjects and 0.245 for the individual with SCI. Although positive correlation was present between the sEMG data and resulting step lengths, the result was not statistically significant (p > 0.05).
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ItemNo Preview AvailableExploring the Utility of Crutch Force Sensors to Predict User Intent in Assistive Lower Limb Exoskeletons(IEEE, 2022-01-01)The adoption of assistive lower limb exoskeletons in built environments is reliant on the further development of these devices to handle the varied conditions experienced in everyday life. The required development includes more varied and flexible gait patterns, but also appropriate user interfaces to enable fluid gait. This work explores the properties of an algorithm used to predict user intent based on sensors onboard a user-balanced robotic exoskeleton system. Specifically, classification algorithms built with different input data sets are compared - with varying detail of the interaction forces between the crutches and the ground, and the duration of the data sample used to make the prediction. Data were collected with one able-bodied participant using an exoskeleton, training three independent classifiers corresponding to different exoskeleton states. The results indicate the value of including information about the interaction forces between the crutches and the ground in improving prediction accuracy, with increasing prediction window also generally resulting in an increase in prediction accuracy. Whilst no categorical recommendation can be made with respect to either parameter, these results provide a baseline which can be used in conjunction deliberate consideration of the costs associated with implementation.
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ItemTechnology-assisted assessment of spasticity: a systematic review(BMC, 2022-12-09)BACKGROUND: Spasticity is defined as "a motor disorder characterised by a velocity dependent increase in tonic stretch reflexes (muscle tone) with exaggerated tendon jerks". It is a highly prevalent condition following stroke and other neurological conditions. Clinical assessment of spasticity relies predominantly on manual, non-instrumented, clinical scales. Technology based solutions have been developed in the last decades to offer more specific, sensitive and accurate alternatives but no consensus exists on these different approaches. METHOD: A systematic review of literature of technology-based methods aiming at the assessment of spasticity was performed. The approaches taken in the studies were classified based on the method used as well as their outcome measures. The psychometric properties and usability of the methods and outcome measures reported were evaluated. RESULTS: 124 studies were included in the analysis. 78 different outcome measures were identified, among which seven were used in more than 10 different studies each. The different methods rely on a wide range of different equipment (from robotic systems to simple goniometers) affecting their cost and usability. Studies equivalently applied to the lower and upper limbs (48% and 52%, respectively). A majority of studies applied to a stroke population (N = 79). More than half the papers did not report thoroughly the psychometric properties of the measures. Analysis identified that only 54 studies used measures specific to spasticity. Repeatability and discriminant validity were found to be of good quality in respectively 25 and 42 studies but were most often not evaluated (N = 95 and N = 78). Clinical validity was commonly assessed only against clinical scales (N = 33). Sensitivity of the measure was assessed in only three studies. CONCLUSION: The development of a large diversity of assessment approaches appears to be done at the expense of their careful evaluation. Still, among the well validated approaches, the ones based on manual stretching and measuring a muscle activity reaction and the ones leveraging controlled stretches while isolating the stretch-reflex torque component appear as the two promising practical alternatives to clinical scales. These methods should be further evaluated, including on their sensitivity, to fully inform on their potential.
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ItemEffect Of Arm Deweighting Using End-Effector Based Robotic Devices On Muscle Activity.(IEEE, 2018)Deweighting of the limb is commonly performed for patients with a neurological injury, such as stroke, as it allows these patients with limited muscle activity to perform movements. Deweighting has been implemented in exoskeletons and other multi-contact devices, but not on an end-effector based device with single contact point between the assisting robot and the human limb being assisted. This study inves-tigates the effects of deweighting using an end-effector based device on healthy subjects. The muscle activity of five subjects was measured in both static postures and dynamic movements. The results indicate a decrease in the activity of muscles which typically act against gravity - such as the anterior deltoid and the biceps brachii - but also suggest an increase in activity in muscles which act with gravity - such as the posterior deltoid and the lateral triceps. This can be explained by both the change in required muscle-generated torques and a conscious change in approach by the participants. These observations have implications for neurorehabilitation, particularly with respect to the muscle activation patterns which are trained through rehabilitation exercises.
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ItemPromoting clinical best practice in a user-centred design study of an upper limb rehabilitation robot(Taylor & Francis, 2021-01-01)Purpose: Despite their promise to increase therapy intensity in neurorehabilitation, robotic devices have not yet seen mainstream adoption. Whilst there are a number of contributing factors, it is obvious that the treating clinician should have a clear understanding of the objectives and limitations of robotic device use. This study sought to explore how devices can be developed to support a clinician in providing clinical best practice. Methods and Materials: A user-centred design study of a robotic device was conducted, involving build-then-use iterations, where successive iterations are built based on feedback from the use cycle. This work reports results of an analysis of qualitative and quantitative data describing the use of the robotic device in the clinical sessions, and from a focus group with the treating clinicians. Results and Conclusions: The data indicated that use of the device did not result in patient goal-setting and may have resulted in poor movement quality. Therapists expected a higher level of autonomy from the robotic device, and this may have contributed to the above problems. These problems can and should be addressed through modification of both the study design and device to provide more explicit instructions to promote clinical best practice. Implications for Rehabilitation: • Encouraging clinical best practice when using evaluating prototype devices within a clinical setting is important to ensure that best practice is maintained - and can be achieved through both study and device design • Support from device developers can significantly improve the confidence of therapists during the use of that device in rehabilitation, particularly with new or prototype devices • End effector-based robotic devices for rehabilitation show potential for a wide variety of patient presentations and capabilities.
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ItemEvaluating Rehabilitation Progress Using Motion Features Identified by Machine Learning(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-04-01)Evaluating progress throughout a patient's rehabilitation episode is critical for determining the effectiveness of the selected treatments and is an essential ingredient in personalised and evidence-based rehabilitation practice. The evaluation process is complex due to the inherently large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients' progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that uses monotonicity and trendability is proposed to evaluate kinematic features. The data used to develop the feature selection technique consist of kinematic data from robot-aided rehabilitation for a population of stroke patients. The selected kinematic features allow for human variations across a population of patients as well as over the sequence of rehabilitation sessions. The study is based on data records pertaining to 41 stroke patients using three different robot assisted exercises for upper limb rehabilitation. Consistent with the literature, the results indicate that features based on movement smoothness are the best measures among 17 kinematic features suitable to evaluate rehabilitation progress.
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ItemNo Preview AvailableA Practical Post-Stroke Elbow Spasticity Assessment Using an Upper Limb Rehabilitation Robot: A Validation Study(IEEE, 2022-07)Spasticity is a motor disorder characterised by a velocity-dependent increase in muscle tone, which is critical in neurorehabilitation given its high prevalence and potential negative influence among the post-stroke population. Accurate measurement of spasticity is important as it guides the strategy of spasticity treatment and evaluates the effectiveness of spasticity management. However, spasticity is commonly measured using clinical scales which may lack objectivity and reliability. Although many technology-assisted measures have been developed, showing their potential as accurate and reliable alternatives to standard clinical scales, they have not been widely adopted in clinical practice due to their low usability and feasibility. This paper thus introduces an easy-to-use robotic based measure of elbow spasticity and its evaluation protocol. Preliminary results collected with one post-stroke patient and one healthy control subject are presented and demonstrate the feasibility of the approach.
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ItemFlexible mechanical metamaterials enabling soft tactile sensors with multiple sensitivities at multiple force sensing ranges(NATURE PORTFOLIO, 2021-12-16)The majority of existing tactile sensors are designed to measure a particular range of force with a fixed sensitivity. However, some applications require tactile sensors with multiple task-relevant sensitivities at multiple ranges of force sensing. Inspired by the human tactile sensing capability, this paper proposes a novel soft tactile sensor based on mechanical metamaterials which exhibits multiple sensitivity regimes due to the step-by-step locking behaviour of its heterogenous multi-layered structure. By tuning the geometrical design parameters of the collapsible layers, each layer experiences locking behaviour under different ranges of force which provides different sensitivity of the sensor at different force magnitude. The integration of a magnetic-based transduction method with the proposed structure results in high design degrees of freedom for realising the desired contact force sensitivities and corresponding force sensing ranges. A systematic design procedure is proposed to select appropriate design parameters to produce the desired characteristics. Two example designs of the sensor structure were fabricated using widely available benchtop 3D printers and tested for their performance. The results showed the capability of the sensor in providing the desired characteristics in terms of sensitivity and force range and being realised in different shapes, sizes and number of layers in a single structure. The proposed multi-sensitivity soft tactile sensor has a great potential to be used in a wide variety of applications where different sensitivities of force measurement is required at different ranges of force magnitudes, from robotic manipulation and human-machine interaction to biomedical engineering and health-monitoring.