In this paper, a surgical robot platform with a novel concentric connector joint (CCJ) is presented. The surgical robot is a parallel robot platform comprised of multiple struts, arranged in a geometrically stable array, connected at their end points via the CCJ. The CCJ joints have near-perfect concentricity of rotation around the node point, which enables the tension and compression forces of the struts to be resolved in a structurally-efficient manner. The preliminary feasibility tests, modeling and simulations were introduced.

Objective: Elucidating the role of structural mechanisms in the knee can improve joint surgeries, rehabilitation, and understanding of biped locomotion. Identification of key features, however, is challenging due to limitations in simulation and in-vivo studies. In particular the coupling of the patellofemoral and tibio-femoral joints with ligaments and its impact on
joint mechanics and movement is not understood. We investigate this coupling experimentally through the design and testing of a robotic sagittal plane model.

Methods: We constructed a sagittal plane robot comprised of: 1) elastic links representing cruciate ligaments; 2) a bi-condylar joint; 3) a patella; and 4) actuator hamstrings and quadriceps. Stiffness and geometry were derived from anthropometric data.  10-110 degree squatting tests were executed at speeds of 0.1-0.25Hz over a range of anterior cruciate ligament (ACL) slack lengths.

Results: Increasing ACL length compromised joint stability, yet did not impact quadriceps mechanical advantage and force required for squat. The trend was consistent through varying
condyle contact point and ligament force changes.

Conclusion: The geometry of the condyles allows the ratio of quadriceps to patella tendon force to compensate for contact point changes imparted by the removal of the ACL. Thus the system maintains a constant mechanical advantage.

Significance: The investigation uncovers critical features of human knee biomechanics. Findings contribute to understanding of knee ligament damage, inform procedures for knee surgery and orthopaedic implant design, and support design of trans-femoral prosthetics and walking robots. Results further demonstrate the utility of robotics as a powerful means of studying human joint biomechanics.

Velostat is a low-cost, low-profile electrical bagging material with piezoresistive properties, making it an attractive option for in-socket pressure sensing. The focus of this research was to explore the suitability of a Velostat-based system for providing real-time socket pressure profiles. The prototype system performance was explored through a series of bench tests to determine properties including accuracy, repeatability and hysteresis responses, and through participant testing with a single subject. The fabricated sensors demonstrated mean accuracy errors of 110 kPa and significant cyclical and thermal drift effects of up to 0.00715 V/cycle and leading to up to a 67% difference in voltage range respectively. Despite these errors the system was able to capture data within a prosthetic socket, aligning to expected contact and loading patterns for the socket and amputation type. Distinct pressure maps were obtained for standing and walking tasks displaying loading patterns indicative of posture and gait phase. The system demonstrated utility for assessing contact and movement patterns within a prosthetic socket, potentially useful for improvement of socket fit, in a low cost, low profile and adaptable format.  However, Velostat requires significant improvement in its electrical properties before proving suitable for accurate pressure measurement tools in lower limb prosthetics

 Understanding biomass pyrolysis is important for biofuel production and fire safety. Inverse modelling is an increasingly used technique to find values for the kinetic parameters that control pyrolysis. The quality of this inverse modelling depends on, in order of importance, the quality of the experimental data, the kinetic model, and the optimisation method used. Unlike the two former components, the optimisation method chosen, i.e. the combination of algorithm and objective function, is rarely discussed in the literature. This work compares the accuracy and efficiency of five commonly used advanced algorithms (Genetic Algorithm, AMALGAM, Shuffled Complex Evolution, Cuckoo Search, and Multi-Start Nonlinear Program) and a simple algorithm (Random Search) to find the kinetic parameters for cellulose and wood pyrolysis at the microscale via thermogravimetric measurements in the literature. These algorithms are combined with seven objective functions comprising concentrated and dispersed functions. The results show that for cellulose (simple chemistry) the use of an advanced optimisation algorithm is unnecessary, since a simple algorithm achieves similarly high accuracy with higher efficiency (40% to 350% faster). However, for wood (complex chemistry) a combination of an advanced algorithm and a concentrated function greatly improves accuracy. Among the 25 possible combinations we investigated for wood, Shuffled Complex Evolution with mean square error objective function performed best with 0.91% error in mass loss rate and 0.88 × 1013 CPU time. These findings can guide the selection of the optimal optimisation method to use in inverse modelling of kinetic parameters, improving accuracy and efficiency.

Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms -bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter-and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity out-of-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and learning algorithms. The sensor system is composed of a force-sensor, three inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson’s Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closed-loop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.

The Mechanical Muscle Activity with Real-time Kinematics project aims to develop a device incorporating wearable sensors for arm rehabilitation following stroke. These will record kinematic activity using inertial measurement units and mechanical muscle activity. The gold standard for measuring muscle activity is electromyography; however, mechanomyography offers an appropriate alterative for our home-based rehabilitation device. We have patent filed a new laboratory-tested device that combines an inertial measurement unit with mechanomyography. We report on the validity and reliability of the mechanomyography against electromyography sensors.

Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in real-world derivation of global orientation. Magnetic field fluctuations and inefficient sensor fusion still inhibit deployment. We introduce a new algorithm, an Extended Complementary Filter (ECF), to derive 3D rigid body orientation from inertial sensing suites addressing these challenges. The ECF combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters. We present a complete formulation of the algorithm, including an extension to address the challenge of orientation accuracy in the presence of fluctuating magnetic fields. Performance is tested under a variety of conditions and benchmarked against the commonly used gradient decent (GDA) inertial sensor fusion algorithm. Results demonstrate improved efficiency, with the ECF achieving convergence 30% faster than standard alternatives. We further demonstrate an improved robustness to sources of magnetic interference in pitch and roll and to fast changes of orientation in the yaw direction. The ECF has been implemented at the core of a wearable rehabilitation system tracking movement of stroke patients for home telehealth. The ECF and accompanying magnetic disturbance rejection algorithm enables previously unachievable real-time patient movement feedback in the form of a full virtual human (avatar), even in the presence of magnetic disturbance. Algorithm efficiency and accuracy have also spawned an entire commercial product line released by the company x-io. We believe the ECF and accompanying magnetic disturbance routines are key enablers for future widespread use of wearable systems with the capacity for global orientation tracking.

Fetal movements (FM) are an important factor in the assessment of fetal health. However,
there is currently no reliable way to monitor FM outside clinical environs. While extensive research
has been carried out using accelerometer-based systems to monitor FM, the desired accuracy of
detection is yet to be achieved. A major challenge has been the diculty of testing and calibrating
sensors at the pre-clinical stage. Little is known about fetal movement features, and clinical trials
involving pregnant women can be expensive and ethically stringent. To address these issues, we
introduce a novel FM simulator, which can be used to test responses of sensor arrays in a laboratory
environment. The design uses a silicon-based membrane with material properties similar to that of a
gravid abdomen to mimic the vibrations due to fetal kicks. The simulator incorporates mechanisms
to pre-stretch the membrane and to produce kicks similar to that of a fetus. As a case study, we
present results from a comparative study of an acoustic sensor, an accelerometer, and a piezoelectric
diaphragm as candidate vibration sensors for a wearable FM monitor. We find that the acoustic sensor
and the piezoelectric diaphragm are better equipped than the accelerometer to determine durations,
intensities, and locations of kicks, as they have a significantly greater response to changes in these
conditions than the accelerometer. Additionally, we demonstrate that the acoustic sensor and the
piezoelectric diaphragm can detect weaker fetal movements (threshold wall displacements are less
than 0.5 mm) compared to the accelerometer (threshold wall displacement is 1.5 mm) with a trade-off
of higher power signal artefacts. Finally, we find that the piezoelectric diaphragm produces better
signal-to-noise ratios compared to the other two sensors in most of the cases, making it a promising
new candidate sensor for wearable FM monitors. We believe that the FM simulator represents a key
development towards enabling the eventual translation of wearable FM monitoring garments.

Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.