Posts Tagged stride
It looks like Mark Priest is walking down the hall using arm crutches, but from his perspective, he says “I’m trying to chase Pac man. Keep my head steady, keep a nice cadence.”
Priest is putting the visor technology to the test. “The concept is to improve my gait and my stride by keeping a certain speed.”
The technology he is using could revolutionize the way people with catastrophic injuries are helped.
“I have a spinal cord injury at level T-9 and T-12 of the vertebrae,” Priest explained.
The Inspire Lab, headed by Doctor Randy Trumbower, focuses on helping people move again. This year, his team of scientists created a new technology using augmented reality.
Trumbower said, “Augmented reality is a mix between what’s real and what’s not real…It’s a game changer for sure.”
That means computer generated images are superimposed over the real environment. For example, a patient can follow a Pac man around the room at changing speeds.
“Different grades, steps, obstacles, things that you maybe wouldn’t experience in a traditional therapy setting,” said Preist.
It also means that this therapy can be targeted. “The thing that is attractive about this particular technology is that it extends those benefits in a way that is more personalized,” said Trumbower.
And ultimately it allows a patient to use that technology anywhere.
Priest said, “My goal is to improve my walking and get off the use of Loftstrand crutches and just be more independent in my day to day living.”
While Priest can only use the visor for short periods, he has still seen improvements. Friday was the first time he tried it without crutches.
“I’m really excited to see the advancement of this technology and how it can help.”
The lab is still in the early stages with the technology, but the promise is there and the work continues.
Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent αand the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that αand D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures.
The stride interval of normal human walking is the time period between consecutive heel strikes of the same foot . For more than two decades, a line of research focused on the understanding of the nature of the subtle variations observed in stride intervals and the origin of typical long-range structures in these variations. Today, these investigations are of paramount importance since they could provide a better understanding of the physiological mechanisms involved in normal human walking and in alterations observed in clinical practice. The nature of these stride interval variations could arise either from noisy neural processes that result in errors in the motor output or from alterations in the motor command that account for balance instabilities .
Normal gait is characterized by the presence of autocorrelations in the stride interval when considering walking on a sufficiently long time scale [1, 3]. The origin of these autocorrelations may be attributed to neural central pattern generators (CPGs) [1, 3] or a super CPG coupled to a forced Van der Pol oscillator , and/or to the biomechanics of walking [5, 6]. For many years, gait analysis has been studied with classical methods adopting biomechanical models in which variability was not of interest. More recent techniques derived from chaos theory are well adapted to analyse time series that exhibit long-range autocorrelation. Importantly, they treat variability as a meaningful interpretable signal. Since the pioneering works of Hausdorff et al. [1, 3], long-range autocorrelations in time series are estimated by the Hurst or fractal exponent (α). A fractal, introduced in 1975 by the French mathematician Benoît Mandelbrot (1924–2010) , is defined as a geometrical structure that has a regular or an uneven shape repeated over all scales of measurement. It is characterized by a fractal dimension (D) greater than the spatial dimension of the structure . A famous example of such object is a snow flake. Objects that are statistically self-similar—parts of it show the same statistical properties at many scales—exhibit strong autocorrelation. The Hurst exponent α is a statistical measure of long-term memory of time series (see e.g.  for a review) and is usually associated to fractal-like behaviour. In particular, the peculiar behavior of the stride interval may be referred to as “fractal behavior” .
The theoretical model of optimal movement complexity  is based on the complementary concepts of predictability and complexity. Nature let us find optimal behavior in terms of skills and variability through evolution. As proposed by Lipsitz and Glodberger in their pioneering work, the optimal state of a biological system may be characterized by chaotic temporal variations in the steady state output that correspond to maximal complexity . Any deviation from healthy state, like senescence and disease, causes a loss in complexity (see also ). Too few practice results in high disorder (randomness, no predictability) and excessive practice leads to high order (periodic signal, maximal predictability). Adaptation of a system to external stimuli is maximal only at an intermediate state of predictability. Furthermore, a signal from a dynamical system also holds some inherent complexity. A decrease of complexity of a physiological system results from either a reduction in the number of structural components or an alteration in the coupling function between these components. For instance, a joint can become rigid with senescence, hence decreasing the degree of freedom of the system and consequently, its complexity. A holistic approach to study these mechanisms requires to associate specific measurements to these two concepts. The Hurst exponent α captures part of the story and is well suited to reflect predictability. While the Minkowski fractal dimension Dprovides good measurability of the “apparent rugosity” of fractals  and reflects complexity. Note that the quantification of a concept such as complexity may not be linked to a unique measure; entropy-related measures have also been shown to be relvant in that domain . Here, we use these parameters to complement the usual quantification of autocorrelation α in unusual and perturbed gait conditions in an attempt to probe adaptability in the framework of the model of optimal movement complexity .
As of today, the vast majority of studies explored autocorrelation in the stride interval during natural forward walking. In one notable exception however, Bollens et al.  also tested backward walking in a small sample of young healthy subjects. The authors did not find significant differences in long-range autocorrelation between both walking directions. However, backward walking measures revealed to be more sensitive than forward walking measures to classify elderly fallers compare to non-fallers . The study of backward walking under the perspective of fractal analyses is therefore promising to provide more reliable predictive index of fallers, as previously proposed for forward walking . Backward walking is also frequently used in sports and in rehabilitation settings, and a better understanding of the variability of stride interval in this condition is needed since it is believed that backward walking is at least partly controlled by specialized neural circuits .
The vestibular system provides an essential sensory contribution to the maintenance of balance during human walking . Individuals with vestibular disorders show a decreased walking stability accompanied by an increased risk to fall . Therefore, perturbing the vestibular system of healthy subjects with galvanic vestibular stimulation (GVS) is a well targeted mean to probe gait: it is standardized, well tolerated by subjects, generated by currently affordable electrostimulators, and easy to implement when a large number of stride intervals are recorded with an instrumented treadmill. The use of GVS is also an increasingly common clinical intervention on locomotion [20–22].
Previously, autocorrelations in stride interval time series have been identified not only in healthy young adults  but also in children  and elderly , and even—although significantly modified—in several neurodegenerative conditions. In particular, the cases of Huntington’s disease , amyotrophic lateral sclerosis , and Parkinson’s disease have been studied [26, 27], with a hope of connecting the observed modifications of fractal behavior to some relevant evaluation of the risk of falling . Here, we hypothesize that the combined effects of walking direction and the application of GVS on long-range autocorrelations in the stride interval could enhance the sensitivity of fractal analysis to identify impaired gait. We measured α and D during forward and backward walking, with and without the application of binaural and monaural GVS. We speculate that these two indexes should be able to capture differences between experimental conditions and therefore provide better indexes to classify patients.[…]
Robotic support has gained more and more interest in rehabilitation of human haptic behavior, e.g. after stroke. First types of rehabilitation robots were intended to replace repetitive movements performed by a physiotherapist by guiding the patient along a physiological reference trajectory. The robot has the advantage of an accurate and repetitive movement while being resistant to any type of fatigue.
New understanding of motor learning shows that active participation of the patient is an essential element of rehabilitation success. A rehabilitation robot should therefore be just as cooperative as the physiotherapist and enhance the patient’s activity. That means that they should only support the patient if needed. It has also been shown that perturbations such as increasing the error in the patient’s movement can progress the rehabilitation procedure more quickly than only “guiding” the patient to perform the correct movement. This form of therapy has some limitations however, if the patient is not able to apply the necessary forces for the movement. In this case the robot should give appropriate support, for example by providing partial weight support of the patient’s arm if the patient is not able to support their own weight. This simulated weightlessness is able to compensate for muscle disabilities and increase the range of motion during training sessions.
Furthermore, a rehabilitation robot can support the patient during specific tasks by recognizing movement deficiencies and disabilities. The robot supports as much as needed and as little as possible. Such a controller has been implemented in the armrobot ARMin (Figure 1, left). While the user is playing a ping-pong game, the robot is able to support the user as much as needed. In human gait rehabilitation, controller design is more restricted for the sake of security. In the Lokomat (Figure 1, right), path controlling is employed to ensure safe and still self-motivated walking. The path controlling method provides a tunnel for joint angles within which the patient can move. As soon as the patient exceeds the pre-set path trajectory limits, the robot pushes the patient back into the right direction. Figure 2 illustrates and explains the concept of path controlling. Another concept is employed in virtual model control (VMC) which aims at maximum patient activity and only supports selectively chosen characteristics such as length or height of the patient’s stride.
All of those control strategies require the robot to assist-as-needed. The assistance can be interpreted as a virtual helping hand. These virtually created worlds are able to display different forms, from free user-performed movements (no help) to resistance against “wrong” user movements (support), or even guiding the patient through their movement completely. In case of the patient being able to self-perform movements correctly, ideally, the robot should not be felt. This behavior is called transparency.
In addition to movement support, a rehabilitation robot is able to display a virtual world which the user can interact with. This is used for simulating activities if daily living (ADL) such as cooking. The representation of a virtual environment requires the possibility of displaying different virtual objects. Especially hard objects are important. Such requirements for the control of a hard environment differ a lot from those for the control of a free, transparent environment. Two different actuator and controller concepts are optimal to be employed to display a hard or soft environment respectively. The two strategies are called impedance and admittance control and will be the central part of this exercise.
Furthermore, we have to make sure that the human-robot-interaction is safe and secure, i.e. the robot should also be able to navigate a totally passive patient. Therefore, the actuators must fulfill some requirements on power and torque. This includes high transmission ratios, which additionally increase the reflected inertia of the drives. High robot inertia lowers the reachable transparency of the robot. Another important point is backdriveability, which makes the robot movable when the robot is not powered at all. This is an important fact e.g. for the case of an emergency stop.
To sum up, the design and choice of the hardware as well as the software implementation should balance each other. The robot has to bring enough forces and moments to support the patient. A strong (therefore heavy) robot arm is well able to display a hard virtual object such as a wall. On the other hand, the robot should be backdriveable and therefore be as lightweight as possible to easily display transparency. Inertia and mass of a strong (heavy) motor in the system make it difficult to display free environment such as air. Besides the choice of the hardware, the choice of the control strategy is an important fact, too. We will focus on two different strategies of how to display a virtual environment and discuss the concepts of impedance and admittance control.
[ARTICLE] The effects of a progressive resistance training program on walking ability in patients after stroke: a pilot study – Full Text PDF
[Purpose] The purpose of this study was to evaluate the effects of a progressive resistance training (PRT) program on the walking ability of chronic stroke patients with hemiparesis following chronic stroke.
[Subjects and Methods] The participants of this study were fifteen hemiplegic patients. The main outcomes measured for this study were the peak torque of the knee extensor; the gait ability as measured by electric gait analysis of walking speed, walking cycle, affected side stance phase, affected side stride length, symmetry index of stance phase, and symmetry index of stride length; and 10-m walking speed; and the Berg balance scale test.
[Results] Walking speed and affected side stride length significantly increased after the PRT program, and 10-m walking time significantly decreased after RPT in stroke patients.
[Conclusion] These results suggest that the progressive resistance training program may, in part, improve the stride of the affected side leg of stroke patients after stroke and also positively impact walking speed.