Model Of Human Locomotion Research Articles

Validation of Metabolic Models for Estimation of Energy Expenditure During Isolated Concentric and Eccentric Muscle Contractions.

Musculoskeletal modeling uses metabolic models to estimate energy expenditure of human locomotion. However, accurate estimation of energy expenditure is challenging, which may be due to uncertainty about the true energy cost of eccentric and concentric muscle contractions. The purpose of this study was to validate three commonly used metabolic models, using isolated isokinetic concentric and eccentric knee extensions/flexions. Five resistance-trained adult males (25.6 ± 2.4 year, 90.6 ± 7.5 kg, 1.81 ± 0.09 m) performed 150 repetitions at four different torques in a dynamometer. Indirect calorimetry was used to measure energy expenditure during these muscle contractions. All three models underestimated the energy expenditure (compared with indirect calorimetry) for up to 55.8% and 78.5% for concentric and eccentric contractions, respectively. Further, the coefficient of determination was in general low for eccentric contractions (R2 < 0.46) indicating increases in the absolute error with increases in load. These results show that the metabolic models perform better when predicting energy expenditure of concentric contractions compared with eccentric contractions. Thus, more knowledge about the relationship between energy expenditure and eccentric work is needed to optimize the metabolic models for musculoskeletal modeling of human locomotion.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.

Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming

Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.

A model for the transfer of control from the brain to the spinal cord through synaptic learning.

The spinal cord is essential to the control of locomotion in legged animals and humans. However, the actual circuitry of the spinal controller remains only vaguely understood. Here we approach this problem from the viewpoint of learning. More precisely, we assume the circuitry evolves through the transfer of control from the brain to the spinal cord, propose a specific learning mechanism for this transfer based on the error between the cord and brain contributions to muscle control, and study the resulting structure of the spinal controller in a simplified neuromuscular model of human locomotion. The model focuses on the leg rebound behavior in stance and represents the spinal circuitry with 150 muscle reflexes. We find that after learning a spinal controller has evolved that produces leg rebound motions in the absence of a central brain input with only three structural reflex groups. These groups contain individual reflexes well known from physiological experiments but thought to serve separate purposes in the control of human locomotion. Our results suggest a more holistic interpretation of the role of individual sensory projections in spinal networks than is common. In addition, we discuss potential neural correlates for the proposed learning mechanism that may be probed in experiments. Together with such experiments, neuromuscular models of spinal learning likely will become effective tools for uncovering the structure and development of the spinal control circuitry.

Kinematics of lower limbs during walking are emulated by springy walking model with a compliantly connected, off-centered curvy foot

The dynamics of the center of mass (CoM) during walking and running at various gait conditions are well described by the mechanics of a simple passive spring loaded inverted pendulum (SLIP). Due to its simplicity, however, the current form of the SLIP model is limited at providing any further information about multi-segmental lower limbs that generate oscillatory CoM behaviors and their corresponding ground reaction forces. Considering that the dynamics of the CoM are simply achieved by mass-spring mechanics, we wondered whether any of the multi-joint motions could be demonstrated by simple mechanics. In this study, we expand a SLIP model of human locomotion with an off-centered curvy foot connected to the leg by a springy segment that emulates the asymmetric kinematics and kinetics of the ankle joint. The passive dynamics of the proposed expansion of the SLIP model demonstrated the empirical data of ground reaction forces, center of mass trajectories, ankle joint kinematics and corresponding ankle joint torque at various gait speeds. From the mechanically simulated trajectories of the ankle joint and CoM, the motion of lower-limb segments, such as thigh and shank angles, could be estimated from inverse kinematics. The estimation of lower limb kinematics showed a qualitative match with empirical data of walking at various speeds. The representability of passive compliant mechanics for the kinetics of the CoM and ankle joint and lower limb joint kinematics implies that the coordination of multi-joint lower limbs during gait can be understood with a mechanical framework.

Preferred gait and walk-run transition speeds in ostriches measured using GPS-IMU sensors.

The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait-speed distributions with previous laboratory measures of gait dynamics and energetics. We also measured the walk-run and run-walk transition speeds and compared them with those reported for humans. We found that ostriches prefer to walk remarkably slowly, with a narrow walking speed distribution consistent with minimizing cost of transport (CoT) according to a rigid-legged walking model. The dimensionless speeds of the walk-run and run-walk transitions are slower than those observed in humans. Unlike humans, ostriches transition to a run well below the mechanical limit necessitating an aerial phase, as predicted by a compass-gait walking model. When running, ostriches use a broad speed distribution, consistent with previous observations that ostriches are relatively economical runners and have a flat curve for CoT against speed. In contrast, horses exhibit U-shaped curves for CoT against speed, with a narrow speed range within each gait for minimizing CoT. Overall, the gait dynamics of ostriches moving freely over natural terrain are consistent with previous lab-based measures of locomotion. Nonetheless, ostriches, like humans, exhibit a gait-transition hysteresis that is not explained by steady-state locomotor dynamics and energetics. Further study is required to understand the dynamics of gait transitions.

Pedestrian motion direction estimation using simulated automotive MIMO radar

Micro-Doppler-based target classification capabilities of the automotive radars can provide high reliability and short latency to the future active safety automotive features. A large number of pedestrians surrounding vehicle in practical urban scenarios mandate prioritization of their treat level. Classification between relevant pedestrians that cross the street or are within the vehicle path and those that are on the sidewalks and move along the vehicle rout can significantly minimize a number of vehicle-to-pedestrian accidents. This work proposes a novel technique for a pedestrian direction of motion estimation which treats pedestrians as complex distributed targets and utilizes their micro-Doppler (MD) radar signatures. The MD signatures are shown to be indicative of pedestrian direction of motion, and the supervised regression is used to estimate the mapping between the directions of motion and the corresponding MD signatures. In order to achieve higher regression performance, the state of the art sparse dictionary learning based feature extraction algorithm was adopted from the field of computer vision by drawing a parallel between the Doppler effect and the video temporal gradient. The performance of the proposed approach is evaluated in a practical automotive scenario simulations, where a walking pedestrian is observed by a multiple-input-multiple-output (MIMO) automotive radar with a 2D rectangular array. The simulated data was generated using the statistical Boulic-Thalman human locomotion model. Accurate direction of motion estimation was achieved by using a support vector regression (SVR) and a multilayer perceptron (MLP) based regression algorithms. The results show that the direction estimation error is less than $10^{\circ}$ in $95\%$ of the tested cases, for pedestrian at the range of $100$m from the radar.

Where are you going? Using human locomotion models for target estimation

To explore virtual environments that are larger than the available physical tracking space by real walking, it is necessary to use so-called redirected walking. Redirection techniques allow the user to explore an unlimited virtual environment in a limited tracking space by introducing a small mismatch between a user's real and virtual movement, thus preventing the user from colliding with the physical walls of the tracking space. Steering algorithms are used to select the most suitable redirection technique at any given time, depending on the geometry of the real and virtual environment. Together with prediction of a user's future walking path, these algorithms select the best redirection strategy by an optimal control scheme. In this paper, a new approach for the prediction of a person's locomotion target is presented. We use various models of human locomotion together with a set of possible targets to create a set of expected paths. These paths are then compared to the real path the user already traveled to calculate the probability of a certain target being the one the user is heading for. A new approach for comparing paths with each other is introduced and is compared to three others. For describing the human's path to a given target, four different models are used and compared. To gather data for the comparison of the models against the real path, a user study was conducted. Based on the results of the user study, the paper concludes with a discussion on the prediction performance of the different approaches.

A Perturbation Mechanism for Investigations of Phase-Dependent Behavior in Human Locomotion.

Bipedal locomotion is a popular area of study across multiple fields (e.g., biomechanics, neuroscience and robotics). Different hypotheses and models have tried explaining how humans achieve stable locomotion. Perturbations that produce shifts in the nominal periodic orbit of the joint kinematics during locomotion could inform about the manner in which the human neuromechanics represent the phase of gait. Ideally, this type of perturbation would modify the progression of the human subject through the gait cycle without deviating from the nominal kinematic orbits of the leg joints. However, there is a lack of publicly available experimental data with this type of perturbation. This paper presents the design and validation of a perturbation mechanism and an experimental protocol capable of producing phase-shifting perturbations of the gait cycle. The effects of this type of perturbation on the gait cycle are statistically quantified and analyzed in order to show that a clean phase shift in the gait cycle was achieved. The data collected during these experiments will be publicly available for the scientific community to test different hypotheses and models of human locomotion.

Toward Balance Recovery With Leg Prostheses Using Neuromuscular Model Control.

Lower limb amputees are at high risk of falling as current prosthetic legs provide only limited functionality for recovering balance after unexpected disturbances. For instance, the most established control method used on powered leg prostheses tracks local joint impedance functions without taking the global function of the leg in balance recovery into account. Here, we explore an alternative control policy for powered transfemoral prostheses that considers the global leg function and is based on a neuromuscular model of human locomotion. We adapt this model to describe and simulate an amputee walking with a powered prosthesis using the proposed control, and evaluate the gait robustness when confronted with rough ground and swing leg disturbances. We then implement and partially evaluate the resulting controller on a leg prosthesis prototype worn by a nonamputee user. In simulation, the proposed prosthesis control leads to gaits that are more robust than those obtained by the impedance control method. The initial hardware experiments with the prosthesis prototype show that the proposed control reproduces normal walking patterns qualitatively and effectively responds to disturbances in early and late swing. However, the response to midswing disturbances neither replicates human responses nor averts falls. The neuromuscular model control is a promising alternative to existing prosthesis controls, although further research will need to improve on the initial implementation and determine how well these results transfer to amputee gait. This paper provides a potential avenue for future development of control policies that help to improve amputee balance recovery.

Coupling effect analysis between the central nervous system and the CPG network with proprioception

SUMMARYHuman rhythmic movement is generated by central pattern generators (CPGs), and their application to robot control has attracted interest of many scientists. But the coupling relationship between the central nervous system and the CPG network with external inputs is still not unveiled. According to biological experiment results, the CPG network is controlled by the neural system; in other words, the interaction between the central nervous system and the CPG network can control human movement effectively. This paper offers a complex human locomotion model, which illustrates the coupling relationship between the central nervous system and the CPG network with proprioception. Based on Matsuoka's CPG model (K. Matsuoka, Biol. Cybern. 52(6), 367–376 (1985)), the stability and robustness of the CPG network are analyzed with external inputs. In order to simulate the coupling relationship, the Radial Basis Function (RBF) neural network is used to simulate the cerebral cortex, and the Credit-Assignment Cerebellar Model Articulation Controller algorithm is employed to realize the locomotion mode conversion. A seven-link biped robot is chosen to simulate the walking gait. The main discoveries include: (1) the output of a new CPG network, which is stable and robust, can be treated as proprioception. Proprioception provides the central nervous system with the information about all joint angles; (2) analysis on a new locomotion model reveals that the cerebral cortex can modulate CPG parameters, leading to adjustment in walking gait.

On Inverse Optimal Control Problems of Human Locomotion: Stability and Robustness of the Minimizers

In recent papers, models of human locomotion by means of optimal control problems have been proposed. In this paradigm, the trajectories are assumed to be solutions of an optimal control problem whose cost has to be determined. The purpose of the present paper is to analyze the class of optimal control problems defined in this way. We prove strong convergence results for their solutions, on the one hand, for perturbations of the initial and final points (stability), and, on the other hand, for perturbations of the cost (robustness).

A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives

Human locomotion has been described as being generated by an impulsive (burst-like) excitation of groups of musculotendon units, with timing dependent on the biomechanical goal of the task. Despite this view being supported by many experimental observations on specific locomotion tasks, it is still unknown if the same impulsive controller (i.e., a low-dimensional set of time-delayed excitastion primitives) can be used as input drive for large musculoskeletal models across different human locomotion tasks. For this purpose, we extracted, with non-negative matrix factorization, five non-negative factors from a large sample of muscle electromyograms in two healthy subjects during four motor tasks. These included walking, running, sidestepping, and crossover cutting maneuvers. The extracted non-negative factors were then averaged and parameterized to obtain task-generic Gaussian-shaped impulsive excitation curves or primitives. These were used to drive a subject-specific musculoskeletal model of the human lower extremity. Results showed that the same set of five impulsive excitation primitives could be used to predict the dynamics of 34 musculotendon units and the resulting hip, knee and ankle joint moments (i.e., NRMSE = 0.18 ± 0.08, and R2 = 0.73 ± 0.22 across all tasks and subjects) without substantial loss of accuracy with respect to using experimental electromyograms (i.e., NRMSE = 0.16 ± 0.07, and R2 = 0.78 ± 0.18 across all tasks and subjects). Results support the hypothesis that biomechanically different motor tasks might share similar neuromuscular control strategies. This might have implications in neurorehabilitation technologies such as human-machine interfaces for the torque-driven, proportional control of powered prostheses and orthoses. In this, device control commands (i.e., predicted joint torque) could be derived without direct experimental data but relying on simple parameterized Gaussian-shaped curves, thus decreasing the input drive complexity and the number of needed sensors.

Crime Modeling with Lévy Flights

The UCLA burglary hotspot model, introduced in [M. B. Short, M. R. D'Orsogna, V. B. Pasour, G. E. Tita, P. J. Brantingham, A. L. Bertozzi, and L. B. Chayes, Math. Models Methods Appl. Sci., 18 (2008), pp. 1249--1267], models the formation of hotspots of criminal activity. In this paper, we extend the UCLA model to incorporate a more realistic model of human locomotion. The movement of the criminal agents follows a biased Lévy flight with step sizes distributed according to a power-law distribution. The biased Brownian motion of the original model is then derived as a special case. Starting with an agent-based model, we derive its continuum limit. This consists of two equations and involves the fractional Laplacian operator. A numerical method based on the fast Fourier transform is used to simulate the continuum model; these simulations compare favorably with the direct numerical simulations of the agent-based model. A Turing-type analysis is performed to estimate how the instability of the homogeneous steady state, as well as the expected number of hotspots, depends on the system parameters and especially the exponent of the underlying power law. The assumptions of the underlying agent-based model naturally lead to a separation of scales of the diffusion coefficients in the continuum limit. Using these assumptions, we asymptotically construct the leading-order profile of the localized hotspot of criminal activity.

Neural oscillators triggered by loading and hip orientation can generate activation patterns at the ankle during walking in humans

Spinal pattern generators (SPGs), which are neural networks without a central input from the brain may be responsible for controlling locomotion. In this study, we used neural oscillators to examine the rhythmic patterns generated at the ankle during walking. Seven healthy male subjects were requested to walk at their normal self-selected speed on a treadmill. Force measurements acquired from pressure insoles, electromyography and kinematic data were captured simultaneously. The SPG model consisted of a simple oscillator made up of two neurons; one neuron will activate an ankle extensor and the other will activate an ankle flexor. The outputs of the oscillator represented the muscle activation of each muscle. A nonlinear least squares algorithm was used to determine a set of parameters that would optimise the differences between model output and experimental data. Insole forces and hip angles of six consecutive strides were used as inputs to the model, which generated outputs that closely fitted experimental data. Our results showed that it is possible to reproduce muscle activations using neural oscillators. A close correlation between simulated and measured muscle activations indicated that spinal control should not be underestimated in models of human locomotion.

The Metabolic Cost of Locomotion; Muscle by Muscle

In this issue of Exercise and Sport Sciences Reviews (ESSR), Umberger and Rubenson (1) explain and advocate for two new techniques that estimate the metabolic energy consumed by individual muscles during locomotion. These techniques (quantifying muscle blood flow using injectable microspheres and computer simulation of muscle mechanics/energetics) are providing insights unobtainable from measurements made at the whole-organism level. These techniques are important steps toward a comprehensive and more detailed understanding of the energetics of human walking and running in health and disease. Since the 1890s, scientists have measured whole-body oxygen consumption rates for humans and other animals walking on treadmills. Such classic techniques are analogous to measuring the overall gas mileage of an automobile using the car's odometer and recording how many gallons it takes to refill the gas tank. If a driver notes that they need to fill up after covering fewer miles than usual, the obvious conclusion is that the car has developed a problem (perhaps a leaky fuel injector). To identify which cylinder has the problem, a mechanic would need to open the hood and measure the gasoline flow at each injector or attach a scanner to the car's computer port. The new tools presented by Umberger and Rubenson are allowing scientists to make analogous measurements on humans and other animals while they walk or run unencumbered. These tools can provide an accounting for when and how much metabolic energy is being consumed on a muscle-by-muscle basis. These new tools are needed because unlike many other areas in physiology, there are no good animal models for the integrative process of human, bipedal locomotion. Standard laboratory animals, such as mice, rats, and dogs, are all quadrupedal. Our closest primate relatives (chimps, gorillas, and orangutans) are not skilled bipedal walkers or runners. Some birds are quite adept at bipedal gait, but unlike healthy humans, birds walk on their toes with their heels high in the air and with little movement at the hip joint. Thus, for studying locomotion, a hybrid approach, using nonhuman animals to validate computer models of human locomotion, can be a productive option. However, these new tools come with their own sets of assumptions and limitations. Muscle blood flow seems to be well correlated with oxygen consumption, but the true rate of metabolic energy consumption varies with substrate usage (i.e., the carbohydrate/fat fuel blend), and blood flow alone does not quantify substrate utilization. Furthermore, although blood flow measurements can approximate how much energy is consumed by specific muscles, it cannot identify which biomechanical task is consuming energy. This is because some individual muscles perform multiple tasks, and some muscles have actions at multiple joints. For example, the gastrocnemius muscle in humans contributes to body-weight support and forward propulsion of the whole body and also helps to launch the leg into the swing phase. In addition, the gastrocnemius has actions at the ankle and the knee, but one cannot deduce from blood flow which action is consuming energy. Computer simulations of gait have unveiled much about the biomechanics of locomotion at each joint and at each muscle. However, these simulations still rely on good old-fashioned electromyographic recordings for apportioning muscle forces, a process that itself is far from exact. Finally, even when the individual muscle mechanics are known, predicting the resulting metabolic energy consumption involves adapting models from in vitro muscle experiments on nonhuman animals. Thus, if the goal is to reliably quantify the energy consumed by individual muscles in humans, we have a quandary. Direct measures of individual muscle-tendon forces and the work produced/absorbed have been obtained for running birds and other nonhuman mammals but are only possible for muscles with accessible, distinct tendons. Direct measurement of muscle-tendon force and work is too invasive for routine use in humans. Similarly, individual muscle blood flow measures using microspheres are not possible in humans because they involve sacrificing the animal to dissect and quantify the number of microspheres in each muscle. Essentially, we cannot measure muscle forces or energetics directly in humans, and we lack sophisticated computer models of nonhuman animal locomotion. Fortunately, computer simulations of the mechanics and muscle actions involved in nonhuman animal locomotion are in development. Such biomechanical models can then be validated using direct measurements of muscle-tendon forces and length changes. Validating these models for other species will improve computer simulations of human locomotion mechanics and increase our confidence therein. Furthermore, the validity of computer-simulated muscle energetics will be testable on nonhuman animals using microsphere-based muscle blood flow measures. A well-validated, integrative computer simulation of human gait energetics is probably a decade away, but as Umberger and Rubenson suggest, we are well on our way. Rodger Kram Christopher J. Arellano Jason R. Franz Locomotion Lab University of Colorado Boulder, CO

A Muscle-Reflex Model That Encodes Principles of Legged Mechanics Produces Human Walking Dynamics and Muscle Activities

While neuroscientists identify increasingly complex neural circuits that control animal and human gait, biomechanists find that locomotion requires little control if principles of legged mechanics are heeded that shape and exploit the dynamics of legged systems. Here, we show that muscle reflexes could be vital to link these two observations. We develop a model of human locomotion that is controlled by muscle reflexes which encode principles of legged mechanics. Equipped with this reflex control, we find this model to stabilize into a walking gait from its dynamic interplay with the ground, reproduce human walking dynamics and leg kinematics, tolerate ground disturbances, and adapt to slopes without parameter interventions. In addition, we find this model to predict some individual muscle activation patterns known from walking experiments. The results suggest not only that the interplay between mechanics and motor control is essential to human locomotion, but also that human motor output could for some muscles be dominated by neural circuits that encode principles of legged mechanics.

A Fuzzy Dead Reckoning Algorithm for a Personal Navigator

: A concept design of a fuzzy Dead Reckoning (DR) algorithm for a personal navigator (PN) is introduced here. The PN system prototype includes a range of self-contained sensors such as GPS, accelerometer, gyroscope, magnetometer, digital barometer, and step sensors. In addition, a human locomotion model is considered as a navigation sensor, with the step length (SL) and step direction (SD) as primary parameters. The major focus of this paper is on DR navigation supported by human dynamics during GPS signal blockages. It is demonstrated that in the absence of GPS, the other sensors can sense the body locomotion in terms of its dynamics and geometry that represent an implicit function of SL and SD. A practical implementation of the DR system based on human dynamics is a fuzzy logic Knowledge-Based System (KBS). This paper discusses the design and implementation of the KBS, followed by its performance evaluation in the indoor environments.

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3–5 m CEP (circular error probable) 50%.

Efficient Bipedal Robots Based on Passive-Dynamic Walkers

Passive-dynamic walkers are simple mechanical devices, composed of solid parts connected by joints, that walk stably down a slope. They have no motors or controllers, yet can have remarkably humanlike motions. This suggests that these machines are useful models of human locomotion; however, they cannot walk on level ground. Here we present three robots based on passive-dynamics, with small active power sources substituted for gravity, which can walk on level ground. These robots use less control and less energy than other powered robots, yet walk more naturally, further suggesting the importance of passive-dynamics in human locomotion.