BACKGROUND AND PURPOSE:

Stroke, predominantly a condition of older age, is a major cause of acquired disability in the global population and puts an increasing burden on health care resources. Clear evidence for the importance of intensity of therapy in optimizing functional outcomes is found in animal models, supported by neuroimaging and behavioral research, and strengthened by recent meta-analyses from multiple clinical trials. However, providing intensive therapy using conventional treatment paradigms is expensive and sometimes not feasible because of social and environmental factors. This article addresses the need for cost-effective increased intensity of practice and suggests potential benefits of telehealth (TH) as an innovative model of care in physical therapy.

SUMMARY OF KEY POINTS:

We provide an overview of TH and present evidence that a web-supported program, used in conjunction with constraint-induced therapy (CIT), can increase intensity and adherence to a rehabilitation regimen. The design and feasibility testing of this web-based program, "LifeCIT," is presented. We describe how wearable sensors can monitor activity and provide feedback to patients and therapists. The methodology for the development of a wearable device with embedded inertial and mechanomyographic sensors, algorithms to classify functional movement, and a graphical user interface to present meaningful data to patients to support a home exercise program is explained.

RECOMMENDATIONS FOR CLINICAL PRACTICE:

We propose that wearable sensor technologies and TH programs have the potential to provide most-effective, intensive, home-based stroke rehabilitation.

This study presents (1) the design and validation of a wear- able sensor suite for the unobtrusive capture of heterogeneous signals indicative of the primary symptoms of Parkinson’s disease; tremor, bradykinesia and muscle rigidity in upper extremity movement and (2) a model to characterise these signals as they relate to the symptom sever- ity as addressed by the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS).

The sensor suite and detection algorithms managed to distinguish between the non-mimicked and mimicked MDS-UPDRS tests on healthy subjects (p ≤ 0.15), for all the primary symptoms of Parkinson’s disease. Future trials will be conducted on Parkinsonian subjects receiving deep brain stimulation (DBS) therapy. Quantifying symptom severity and cor- relating severity ratings with DBS treatment will be an important step to fully automate DBS therapy.

The key determinant to a fetus maintaining its health is through adequate perfusion and oxygen transfer mediated by the functioning placenta. When this equilibrium is distorted, a number of physiological changes, including reduced fetal growth, occur to favor survival. Technologies have been developed to monitor these changes with a view to prolong intrauterine maturity while reducing the risks of stillbirth. Many of these strategies involve complex interpretation, for example Doppler ultrasound for fetal blood flow and computerized analysis of fetal heart rate changes. However, even with these modalities of fetal assessment to determine the optimal timing of delivery, fetal movements remain integral to clinical decision-making. In high-risk cohorts with fetal growth restriction, the manifestation of a reduction in perceived movements may warrant an expedited delivery. Despite this, there has been little evolution in the development of technologies to objectively evaluate fetal movement behavior for clinical application. This review explores the available literature on the value of fetal movement analysis as a method of assessing fetal wellbeing, and demonstrates how interdisciplinary developments in this area may aid in the improvement of clinical outcomes.

Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain–machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). Approach. LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. Main results. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. Significance. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a relatively stable and computationally inexpensive control signal. This has the potential to extend invasive BMI, based on recordings within the motor cortex, by providing additional information from subcortical regions.

The majority of robotic vehicles that can be found today are bound to operations within a single media (i.e. land, air or water). This is very rarely the case when considering locomotive capabilities in natural systems. Utility for small robots often reflects the exact same problem domain as small animals, hence providing numerous avenues for biological inspiration. This paper begins to investigate the various modes of locomotion adopted by different genus groups in multiple media as an initial attempt to determine the compromise in ability adopted by the animals when achieving multi-modal locomotion. A review of current biologically inspired multi-modal robots is also presented. The primary aim of this research is to lay the foundation for a generation of vehicles capable of multi-modal locomotion, allowing ambulatory abilities in more than one media, surpassing current capabilities. By identifying and understanding when natural systems use specific locomotion mechanisms, when they opt for disparate mechanisms for each mode of locomotion rather than using a synergized singular mechanism, and how this affects their capability in each medium, similar combinations can be used as inspiration for future multi-modal biologically inspired robotic platforms.

This paper describes the design, fabrication, experimental testing and performance optimization of the morphology of a flapping wing for use on a robot capable of aerial and aquatic modes of locomotion. The focus of the optimization studies is that of wing design for aquatic propulsion. Inspiration for the research stems from numerous avian species which use a flapping wing for the dual purpose of locomotion (propulsion) in both air and water. The main aim of this research is to determine optimal kinematic parameters for marine locomotion that maximize nondimensionalized performance measures (e.g., propulsive efficiency), derived from analysis of avian wing morphing mechanisms that balance competing demands of both aerial and aquatic movement. Optimization of the kinematic parameters enables the direct comparison between outstretched (aerial) and retracted (aquatic) wing morphologies and permits trade-off studies in the design space for future robotic vehicles. Static foils representing the wing in both an extended and retracted orientation have been manufactured and subsequently subjected to testing over a range of kinematics. Details of the purpose built 2 degree-of-freedom (dof) flapping mechanism are presented. The gathered results enable validation of previously developed numerical models as well as quantifying achievable performance measures. This research focuses on the mechanical propulsive efficiencies and thrust coefficients as key performance measures whilst simultaneously considering the required mechanical input torques and the associated thrust produced.

This paper presents a biologically inspired architecture for rapid real-time control of autonomous or semi-autonomous vehicles based on a neural model of the escape response of the American cockroach, Periplaneta americana.  The architecture fuses exteroceptive and proprioceptive inputs in a manner similar to the insect to produce commands for collision avoidance and, in some cases, orientation for target strike.  It functions as a reflexive subsystem that integrates smoothly with higher-level planning and behavioral control systems.  The performance of the reflex is demonstrated in simulation and in hardware experiments on both air and ground vehicles, even in the presence of noisy, false or disruptive sensor data.  (AWARDED: BEST PAPER IN JOURNAL, 2012)

Dielectric elastomer electroactive polymers are an emerging class of actuation technology which is inherently compliant and capable of large actuation stresses and strains. Despite promising performance characteristics, their fabrication has been inhibited by two significant factors: (i) the requirement for consistently thin dielectric layers, to minimise activation voltages; (ii) automated production of multilayered configurations, to increase the actuation power. This paper presents a robust, low-cost fabrication technique that overcomes these issues by utilising optimised spray deposition. Spray deposition of silicone dielectric elastomer actuators (DEAs) offers numerous benefits including scalability, flexibility for different DEA configurations and multilayered assembly with a high degree of automation. A predictive model based on the Gaussian distribution is used to characterise the profile of deposited elastomer layers for principal fabrication parameters. This model enables individual dielectric layers to be composed from multiple parallel depositions, which greatly increases scalability as demonstrated by fabricated DEA films with planar dimensions from 25 mm(2) to over 10,000 mm(2). Using the predictive model, a new figure of merit is introduced for analyzing DEA film profiles by considering the estimated mean Maxwell stress that is feasible for a specific dielectric breakdown strength. The analysis suggests that compared to a single deposition, a film composed of four parallel depositions will increase the maximum characteristic DEA dimension by an order of magnitude, while producing a comparable mean Maxwell stress. A significant advantage of the presented spray deposition technique is the semi-automated layering process, creating stratified solid-state actuators. By eliminating the stacking of layers from the fabrication process, inherent electrical isolation, good layer-to-layer bonding and capacity for more complex 3D geometries is achieved. A proof-of-concept multilayer unimorph and stack DEA is presented to validate the fabrication technique through static and dynamic displacement tests.

A strategy is introduced to rank and select principal component transform (PCT) and discrete cosine transform (DCT) transform coefficient features to overcome the curse of dimensionality frequently encountered in implementing multivariate signal classifiers due to small sample sizes. The criteria considered for ranking include the magnitude, variance, interclass separation, and classification accuracies of the individual features. The feature ranking and selection strategy is applied to overcome the dimensionality problem, which often plagues the implementation and evaluation of practical Gaussian signal classifiers. The applications of the resulting PCT- and DCT-Gaussian signal classification strategies are demonstrated by classifying single-channel tongue-movement ear-pressure signals and multichannel event-related potentials. Through these experiments, it is shown that the dimension of the feature space can be decreased quite significantly by means of the feature ranking and selection strategy. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank-ordered set, a smaller set of features that give the best classification accuracies. Results show that the PCT- and DCT-Gaussian classifiers yield higher classification accuracies than those reported in previous classification studies on the same signal sets. Among the combinations of the two transforms and four feature selection criteria, the PCT-Gaussian classifiers using the maximum magnitude and maximum variance selection criteria gave the best classification accuracies across the two sets of classification experiments. Most noteworthy is the fact that the multivariate Gaussian signal classifiers developed in this paper can be implemented without having to collect a prohibitively large number of training signals simply to satisfy the dimensionality conditions. Consequently, the classification strategies can be beneficial for designing personalized human-machine interface signal classifiers for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities.