Explaining the Alexander Technique to clinicians and scientists: Psycho-physical re-education - an introduction to cognitive-motor system-level causes of performance-related problems.

This presentation considers the scientific basis of the Alexander Technique, and presents results of experimental research into Alexander teaching methodology. The Alexander Technique, is an educational process with a scientific basis. The efficacy of the Technique is explained by a general mechanism which underlies many problems. Injury and dysfunction, with specific musculoskeletal and cognitive symptoms, can arise from misconception, the rules of neuromuscular function, lack of awareness and reinforcement (wind-up) of symptoms. Within a perception-selection-action feedback loop, misconception and lack of awareness of the consequences of maladaptive selection, ensures that these consequences are subject to destructive (positive) feedback, until the system “breaks” at the individual’s weakest point (http://dx.doi.org/10.17613/M6CN7R). This presentation provides system-level process diagrams to define the concepts of “use” and “misuse” in terms of a perception-selection-action feedback loop. “Use” is the concurrent processes of sensory analysis, response selection, motor generation and movement biomechanics acting simultaneously, and adapting through time according to their input. “Misuse” is the suboptimal processes of sensory analysis, response selection, motor generation and movement biomechanics amplified by misconception of the resulting feedback. The Alexander Technique brings about change by external, educational input into perception, and inhibition of automated responses. Practice is based on understanding the importance of the neck in regulating sensory-motor control. Problems within the domain of the Technique are associated with a pattern of movement and muscle tension that can be observed. Students are taught to observe that pattern and to use their observations as a training signal to regulate their thought and activity, and to prevent problems from occurring

Does the motor system need intermittent control?

Explanation of motor control is dominated by continuous neurophysiological pathways (e.g. trans-cortical, spinal) and the continuous control paradigm. Using new theoretical development, methodology and evidence, we propose intermittent control, which incorporates a serial ballistic process within the main feedback loop, provides a more general and more accurate paradigm necessary to explain attributes highly advantageous for competitive survival and performance

Mechanisms for human balancing of an inverted pendulum using the ankle strategy

Maintenance of upright, human balance is neurologically and biomechanically a complex process, though the ankle strategy predominates in quiet standing. This investigation seeks insight into the complex problem by studying a reduced, yet related problem of how the ankle mechanisms are used to balance a human proportioned inverted pendulum. A distinguishing feature of the task is that despite one's best efforts to control this unstable load some irreducible sway always remains. Contrary to published ideas, modulation of effective ankle stiffness was not the way that sway size was altered. Rather, position was controlled by an intermittent, neurally modulated, ballistic-like pattern of torque whose anticipatory accuracy was improved to reduce sway size. Using a model, and by direct measurement, I found the intrinsic mechanical ankle stiffness will only partially counter the "gravitational spring". Since this stiffness was substantially constant and cannot be neurally modulated, I attribute it to the foot, tendon and aponeurosis rather than the activated calf muscle fibres. Thus triceps-surae muscles maintain balance via a spring-like element which is itself generally too compliant to provide even minimal stability. I hypothesise that balance is maintained by anticipatory, ballistic-like, biasing of the series-elastic element resulting from intermittent modulation of the triceps-surae

The potential of electromyography to aid personal navigation

This paper reports on research to explore the potential for using electromyography (EMG) measurements in pedestrian navigation. The aim is to investigate whether the relationship between human motion and the activity of skeletal muscles in the leg might be used to aid other positioning sensors, or even to determine independently the path taken by a pedestrian. The paper describes an exercise to collect sample EMG data alongside leg motion data, and the subsequent analysis of this data set using machine learning techniques to infer motion from a set of EMG sensors. The sample data set included measurements from multiple EMG sensors, a camera-based motion tracking system and a foot mounted inertial sensor. The camera based motion tracking system at MMU allowed many targets on the subjects lower body to be tracked in a small (3m x 3m x 3m) volume to millimetre accuracy. Processing the data revealed a strong, but not trivial, relation-ship between leg muscle activity and motion. Each type of motion involves many different muscles, and it is not possible to conclude merely from the triggering of any single muscle that a particular type of motion has occurred. For instance, a similar set of leg muscles is involved in both forward and backward steps. It is the precise sequencing, duration and magnitude of multiple muscle activity that allows us to determine what type of motion has occurred. Preliminary analyses of the data suggest that subsets of the EMG sensors can be used to distinguish, for instance, forward motion from backward motion, and it is expected that further analysis will reveal additional correlations that will be useful in inferring the subjects motion in more detail. This paper will introduce the EMG personal navigation con-cept, describe the data collected, explore the machine learning techniques applied to the dataset, and present the results of the analysis

What is the contribution of voluntary and reflex processes to sensorimotor control of balance?

The contribution to balance of spinal and transcortical processes including the long-latency reflex is well known. The control of balance has been modelled previously as a continuous, state feedback controller representing, long-latency reflexes. However, the contribution of slower, variable delay processes has not been quantified. Compared with fixed delay processes (spinal, transcortical), we hypothesize that variable delay processes provide the largest contribution to balance and are sensitive to historical context as well as current states. Twenty-two healthy participants used a myoelectric control signal from their leg muscles to maintain balance of their own body while strapped to an actuated, inverted pendulum. We study the myoelectric control signal (u) in relation to the independent disturbance (d) comprising paired, discrete perturbations of varying inter-stimulus-interval (ISI). We fit the closed loop response, u from d, using one linear and two non-linear non-parametric (many parameter) models. Model M1 (ARX) is a generalized, high-order linear-time-invariant (LTI) process with fixed delay. Model M1 is equivalent to any parametric, closed-loop, continuous, linear-time-invariant (LTI), state feedback model. Model M2, a single non-linear process (fixed delay, time-varying amplitude), adds an optimized response amplitude to each stimulus. Model M3, two non-linear processes (one fixed delay, one variable delay, each of time-varying amplitude), add a second process of optimized delay and optimized response amplitude to each stimulus. At short ISI, the myoelectric control signals deviated systematically both from the fixed delay LTI process (M1), and also from the fixed delay, time-varying amplitude process (M2) and not from the two-process model (M3). Analysis of M3 (all fixed delay and variable delay response amplitudes) showed the variable (compared with fixed) delay process 1) made the largest contribution to the response, 2) exhibited refractoriness (increased delay related to short ISI) and 3) was sensitive to stimulus history (stimulus direction 2 relative to stimulus 1). For this whole-body balance task and for these impulsive stimuli, non-linear processes at variable delay are central to control of balance. Compared with fixed delay processes (spinal, transcortical), variable delay processes provided the largest contribution to balance and were sensitive to historical context as well as current states

Force accuracy rather than high stiffness is associated with faster learning and reduced falls in human balance

Balance requires the centre of mass to be maintained within the base of support. This can be achieved by minimising position sway (stiffness control: SC) or minimising force error (force accuracy control: FAC). Minimising sway reduces exploration of system properties, whereas minimising force error maximizes accurate mapping of the force vs position. We hypothesise that (i) FAC is associated with faster learning and fewer falls whereas (ii) SC is not. Fifteen participants used myoelectric signals from their legs to maintain balance of an actuated, inverted pendulum, to which they were strapped. Using challenging perturbations, participants were trained to maintain balance without falling within five sessions and tested before (PRE) and after (POST) training. We quantified FAC as ‘change (POST-PRE) in correlation of force with position’ and SC as ‘change in sway’. PRE training, five measures (sway, acceleration, co-contraction, effort, falls) showed no correlation with either FAC or SC. POST training, reduced fall rate, effort and acceleration correlated with FAC metric. SC correlated only with reduced sway. Unlike sway minimisation, development of force accuracy was associated with learning and reduced falls. These results support that accurate force estimation allowing movement is more relevant than stiffness to improve balance and prevent falls

Estimating skeletal muscle fascicle curvature from B-mode ultrasound image sequences

We address the problem of tracking in vivo muscle fascicle shape and length changes using ultrasound video sequences. Quantifying fascicle behavior is required to improve understanding of the functional significance of a muscle's geometric properties. Ultrasound imaging provides a noninvasive means of capturing information on fascicle behavior during dynamic movements; to date however, computational approaches to assess such images are limited. Our approach to the problem is novel because we permit fascicles to take up nonlinear shape configurations. We achieve this using a Bayesian tracking framework that is: 1) robust, conditioning shape estimates on the entire history of image observations; and 2) flexible, enforcing only a very weak Gaussian Process shape prior that requires fascicles to be locally smooth. The method allows us to track and quantify fascicle behavior in vivo during a range of movements, providing insight into dynamic changes in muscle geometric properties which may be linked to patterns of activation and intramuscular forces and pressures

Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning

Objective: To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. Methods: We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries. Results: For all segments, agreement with manual labels was Dice Coefficient (64±21%) and Hausdorff Distance (5.7±4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94±3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture. Conclusion: Using US, neck muscle shape alone discriminates dystonia from healthy controls. Significance: Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection. The dataset is available (DOI: 10.23634/MMUDR.00624643)