Locomotion prediction of bipedal walking in monkeys

Frontiers in …, 2009

The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain–machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real time. Linear decoders extracted 3D coordinates of leg joints and leg muscle electromyograms from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again.

Frontiers in Human Neuroscience, 2015

Neuroscientists researching locomotion take a top-down approach by elucidating highlevel cortical control circuits. In contrast, biomechanists prefer to focus on structural and mechanical aspects of the legged movement apparatus. We posit that studying interplay between neural coordination and legged biomechanics can yield crucial insight into (a) motor control and (b) human leg morphology. Physiological facts indicate that muscle actuator state (activation, length and velocity) is key to this neural-structural interplay. Here we present a novel model-based framework to resolve individual muscle state and describe neural-structural interactions in normal gait. We solve the inverse problem of using kinematic, kinetic and electro-myographic data recorded on healthy humans during level-ground,self-selected speed walking to estimate state of three major ankle muscles. Our approach comprises of two steps. First, we estimate neurally-controlled muscle activity from EMG data by building on statistical and mechanistic methods in the literature. Second, we perform a system ID on a mechanistic (Hill-type) model of the three muscles to find tendon morphological parameters governing evolution of muscle length and velocity. We implement the parameter identification as an optimization based on the hypothesis that neural control and lower limb morphology have co-evolved for optimal metabolic economy of natural walking. We cross-validate our framework against independent datasets, and find good model-empirical ankle torque agreement (R 2 = 0.96).

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000

Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra-and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was 0 68 0 08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis.

Experimental Brain Research, 2007

Motor imagery (MI) is widely used to study cognitive aspects of the neural control of action. Prior studies were mostly centred on hand and arm movements. Recently a few studies have used imagery tasks to explore the neurophysiology of human gait, but it remains unclear how to ascertain whether subjects actually perform imagery of gait as requested. Here we describe a new experimental protocol to quantify imagery of gait, by behaviourally distinguishing it from visual imagery (VI) processes and by showing its temporal correspondence with actual gait. Fourteen young healthy subjects performed two imagery tasks and an actual walking (AW) task. During both imagery tasks subjects were sitting on a chair and faced a computer screen that presented photographs of walking trajectories. During one task (MI), subjects had to imagine walking along the walking trajectory. During the other task (VI), subjects had to imagine seeing a disc moving along the walking trajectory. During the AW task, subjects had to physically walk along the same walking trajectory as presented on the photographs during the imagery tasks. We manipulated movement distance by changing the length of the walking trajectory, and movement difficulty by changing the width of the walking trajectory. Subjects reported onset and offset of both actual and imagined movements with a button press. The time between the two button presses was taken as the imagined or actual movement time (MT). MT increased with increasing path length and decreasing path width in all three tasks. Crucially, the effect of path width on MT was significantly stronger during MI and AW than during VI. The results demonstrate a high temporal correspondence between imagined and AW, suggesting that MI taps into similar cerebral resources as those used during actual gait. These results open the possibility of using this protocol for exploring neurophysiological correlates of gait control in humans.

Journal of Neuroscience Methods, 2012

S. Chabardès). neurodegenerative diseases, movement disorders and after spinal cord trauma.

2015

Grasso, R., L. Bianchi, and F. Lacquaniti. Motor patterns for several kinds of movement transformations, such as translahuman gait: backward versus forward locomotion. J. Neurophysiol. tion and rotation in space, amplitude and time scaling, load-80: 1868-1885, 1998. Seven healthy subjects walked forward ing, etc. A number of these properties was described for (FW) and backward (BW) at different freely chosen speeds, while point-to-point (e.g., reaching) (see Soechting and Flanders their motion, ground reaction forces, and electromyographic 1991) and continuous (e.g., drawing and handwriting) (see (EMG) activity from lower limb muscles were recorded. We con-Lacquaniti 1989) movements of the arm. Motor patterns of sidered the time course of the elevation angles of the thigh, shank, arm movements pertain to the domain of either kinematics and foot segments in the sagittal plane, the anatomic angles of the or kinetics. In the kinematic domain, the spatial trajectories hip, knee, and ankle joints, the vertical and longitudinal ground reaction forces, and the rectified EMGs. The elevation angles were and velocity profiles both of the hand and of limb joints are the most reproducible variables across trials in each walking direcconserved (with appropriate scaling in amplitude and time) tion. After normalizing the time course of each variable over the under wide changes in movement size, speed, and load (Atgait cycle duration, the waveforms of all elevation angles in BW keson and Hollerbach 1985; gait were essentially time reversed relative to the corresponding Soechting and Lacquaniti 1981). In the kinetic domain, joint waveforms in FW gait. Moreover, the changes of the thigh, shank, torque (Atkeson and Hollerbach 1985) and muscle activity and foot elevation covaried along a plane during the whole gait Flanders and Herrmann 1992; cycle in both FW and BW directions. Cross-correlation analysis al. 1997) profiles can be decomposed in a set of basic waverevealed that the phase coupling among these elevation angles is forms, a weighted combination of which accounts for movemaintained with a simple reversal of the delay on the reversal of walking direction. The extent of FW-BW correspondence also ments with different speeds and loads. was good for the hip angle, but it was smaller for the knee and Reversal of direction represents a special kind of moveankle angles and for the ground reaction forces. The EMG patterns ment transformation that may help to get an insight into the were drastically different in the two movement directions as was internal representations of motor patterns for some classes the organization of the muscular synergies measured by crossof movements. Not every movement can be reversed; for correlation analysis. Moreover, at any given speed, the mean EMG instance, handwriting, hand gesturing, and speech are unidiactivity over the gait cycle was generally higher in BW than in rectional. Reversible movements, on the other hand, may FW gait, suggesting a greater level of energy expenditure in the display hysteresis. Thus, in pointing back-and-forth between former task. We argue that conservation of kinematic templates across gait reversal at the expense of a complete reorganization of two spatial loci, the trajectory of both the hand and limb muscle synergies does not arise from biomechanical constraints joints may differ considerably in the two movement direcbut may reflect a behavioral goal achieved by the central networks tions and so do the joint torque profiles and muscle patterns involved in the control of locomotion.

2005

Gait analysis under nonlinear view point-applications to rehabilitation and sport 133 3.6.2 Application of a synergetic approach to equitation 3.6.3 Gait pattern of the ataxic horse compared to the gait pattern of normal and sedated horses 3.7 Medicine / Rehabilitation 3.7.1 An optoelectronic 3D study of ankle and foot active range of motion in healthy young adults. 3.7.2 An optoelectronic study of the three-dimensional hip movements during treadmill walking. 3.7.3 Dissociation Between Time and Force Aspects of Dynamic Standing Balance 3.7.4 Increasing noise improves signal-noise ratio in motor learning 3.7.5 Effects of psychomotor training on locomotion in old age 4 Authors 142

Journal of Bionic Engineering, 2006

The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive capabilities and potential applications in clinical biomechanics, rehabilitation engineering and biomimetic robotics. In this paper, the various methods that have been used to synthesize human walking are reviewed from an engineering viewpoint. This involves a wide spectrum of approaches, from simple passive walking theories to large-scale computational models integrating the nervous, muscular and skeletal systems. These methods are roughly categorized under four headings: models inspired by the concept of a CPG (Central Pattern Generator), methods based on the principles of control engineering, predictive gait simulation using optimisation, and models inspired by passive walking theory. The shortcomings and advantages of these methods are examined, and future directions are discussed in the context of providing insights into the neural control objectives driving gait and improving the stability of the predicted gaits. Future advancements are likely to be motivated by improved understanding of neural control strategies and the subtle complexities of the musculoskeletal system during human locomotion. It is only a matter of time before predictive gait models become a practical and valuable tool in clinical diagnosis, rehabilitation engineering and robotics.