Walking on the bright side: Associations between affect, depression, and gait.

BACKGROUND: Psychomotor change is a core symptom of depression and one of the criteria in diagnosing depressive disorders. Research suggests depressed individuals demonstrate deviations in gait, or walking, compared to non-depressed controls. However, studies are sparse, often limited to older adults and observational gait assessment. It is also unclear if gait changes are due to dysregulation of affect, a core feature of depression. The current study addressed this gap by investigating the relation between positive and negative affect, depressive symptom severity, and gait in young adults. METHODS: Using three-dimensional motion capture, gait parameters (velocity, stride length, and step time) were attained from 90 young adults during a task where they walked ten meters at their own pace overground in a laboratory for ten minutes. Self-report measures of mood and affect were collected. RESULTS: On average, the study population reported high negative and low positive affect. Contrary to our hypotheses, hierarchical regressions demonstrated no significant associations between gait parameters and affective or depressive symptoms (ps>.05). CONCLUSIONS: Our findings do not support a relation between affective symptoms and gait parameters. The results may indicate age-dependent gait pathology or that other symptoms of depression may influence gait more strongly than affect. They may also reflect an observational bias of gait changes in depressed young adults, one that is unsupported by objective data. Replication is warranted to further examine whether affective symptomology is embodied via gait differences in young adults.

Parameterizing Human Locomotion Across Quasi-Random Treadmill Perturbations and Inclines.

Previous work has shown that it is possible to use a mechanical phase variable to accurately quantify the progression through a human gait cycle, even in the presence of disturbances. However, mechanical phase variables are highly dependent on the behavior of the body segment from which they are measured, which can change with the human's task or in response to different disturbances. In this study, we compare kinematic parameterization methods based on time, thigh phase angle, and tibia phase angle with motion capture data obtained from ten able-bodied subjects walking at three inclines while experiencing phase-shifting perturbations from a split-belt instrumented treadmill. The belt, direction, and timings of perturbations were quasi-randomly selected to prevent anticipatory action by the subjects and sample different types of perturbations. Statistical analysis revealed that both phase parameterization methods are superior to time parameterization, with thigh phase angle also being superior to tibia phase angle in most cases.

A PID Controller Approach to Explain Human Ankle Biomechanics across Walking Speeds.

Lower-limb robotic prostheses and exoskeletons depend on controllers to function in synchrony with their users. Recent advancements in control technology permit embodiment and more intuitive control for the user. In this study, we utilize a control engineering perspective to propose a phase-dependent muscle-driven proportional, integral, and derivative (PID) controller to regulate human ankle joint trajectories across walking speeds. We calculated the correlation coefficients that relate the tibialis and gastrocnemius muscle activation to the ankle joint angle error, integral of the error, and rate of change of the error between an average ankle joint trajectory and the ankle angle at two walking speeds: 1.5 m/s and 2.0 m/s. We noted that preswing (PSW) was the only gait period that had high absolute values for the correlation coefficients (> 0.7) across all three relationships. Other gait periods had varying high and low correlation coefficients across the different relationships. These results present a promising justification to utilize the classic control technique in a non-conventional manner. A phase-dependent and muscle-driven PID controller influenced by the PSW phase may be used to modulate the ankle joint trajectory with muscle activation across walking speeds in lower-limb robotic prostheses and exoskeletons.

Obstacle Recognition using Computer Vision and Convolutional Neural Networks for Powered Prosthetic Leg Applications.

In this work we combine computer vision and a machine learning algorithm, Convolutional Neural Networks (CNNs), to identify obstacles that powered prosthetic leg users might encounter during walking. Our motivation is that powered prosthetic legs could react in synchronicity with their users by recognizing and anticipating the terrain in front of them. We focus on identifying stairs and doors that are within the visual field of a person. To achieve this, we used a compact CNN architecture to optimize image processing for real-time applications. We built and tested a wearable system prototype that included a camera mounted on a pair of glasses and a single-board computer. The prototype was used by able-bodied users to collect and label obstacle and non-obstacle videos, which were used later to train the CNN. In validation, the system was able to recognize around 90% of obstacles across different indoor and outdoor scenarios. The accuracy achieved and the practicality of the prototype shows the potential of computer vision and machine learning in the field of powered prosthetic legs.

Modeling the Kinematics of Human Locomotion Over Continuously Varying Speeds and Inclines.

Powered knee and ankle prostheses can perform a limited number of discrete ambulation tasks. This is largely due to their control architecture, which uses a finite-state machine to select among a set of task-specific controllers. A non-switching controller that supports a continuum of tasks is expected to better facilitate normative biomechanics. This paper introduces a predictive model that represents gait kinematics as a continuous function of gait cycle percentage, speed, and incline. The basis model consists of two parts: basis functions that produce kinematic trajectories over the gait cycle and task functions that smoothly alter the weight of basis functions in response to task. Kinematic data from 10 able-bodied subjects walking at 27 combinations of speed and incline generate training and validation data for this data-driven model. Convex optimization accurately fits the model to experimental data. Automated model order reduction improves predictive abilities by capturing only the most important kinematic changes due to walking tasks. Constraints on a range of motion and jerk ensure the safety and comfort of the user. This model produces a smooth continuum of trajectories over task, an impossibility for finite-state control algorithms. Random sub-sampling validation indicates that basis modeling predicts untrained kinematics more accurately than linear interpolation.

Continuous-Phase Control of a Powered Knee-Ankle Prosthesis: Amputee Experiments Across Speeds and Inclines.

Control systems for powered prosthetic legs typically divide the gait cycle into several periods with distinct controllers, resulting in dozens of control parameters that must be tuned across users and activities. To address this challenge, this paper presents a control approach that unifies the gait cycle of a powered knee-ankle prosthesis using a continuous, user-synchronized sense of phase. Virtual constraints characterize the desired periodic joint trajectories as functions of a phase variable across the entire stride. The phase variable is computed from residual thigh motion, giving the amputee control over the timing of the prosthetic joint patterns. This continuous sense of phase enabled three transfemoral amputee subjects to walk at speeds from 0.67 to 1.21 m/s and slopes from -2.5 to +9.0 deg. Virtual constraints based on task-specific kinematics facilitated normative adjustments in joint work across walking speeds. A fixed set of control gains generalized across these activities and users, which minimized the configuration time of the prosthesis.

Piecewise and unified phase variables in the control of a powered prosthetic leg.

Many control methods have been proposed for powered prosthetic legs, ranging from finite state machines that switch between discrete phases of gait to unified controllers that have a continuous sense of phase. In particular, recent work has shown that a mechanical phase variable can parameterize the entire gait cycle for controlling a prosthetic leg during steady rhythmic locomotion. However, the unified approach does not provide voluntary control over non-rhythmic motions like stepping forward and back. In this paper we present a phasing algorithm that uses the amputee's hip angle to control both rhythmic and non-rhythmic motion through two modes: 1) a piecewise (PW) function that provides users voluntary control over stance and swing in a piecewise manner, and 2) a unified function that continuously synchronizes the motion of the prosthetic leg with the amputee user at different walking speeds. The two phase variable approaches are compared in experiments with a powered knee-ankle prosthesis used by an above-knee amputee subject.

Real-Time Continuous Gait Phase and Speed Estimation from a Single Sensor.

Human gait involves a repetitive cycle of movements, and the phase of gait represents the location in this cycle. Gait phase is measured across many areas of study (e.g., for analyzing gait and controlling powered lower-limb prosthetic and orthotic devices). Current gait phase detection methods measure discrete gait events (e.g., heel strike, flat foot, toe off, etc.) by placing multiple sensors on the subject's lower-limbs. Using multiple sensors can create difficulty in experimental setup and real-time data processing. In addition, detecting only discrete events during the gait cycle limits the amount of information available during locomotion. In this paper we propose a real-time and continuous measurement of gait phase parameterized by a mechanical variable (i.e., phase variable) from a single sensor measuring the human thigh motion. Human subject experiments demonstrate the ability of the phase variable to accurately parameterize gait progression for different walking/running speeds (1 to 9 miles/hour). Our results show that this real-time method can also estimate gait speed from the same sensor.

A Robust Parameterization of Human Gait Patterns Across Phase-Shifting Perturbations.

The phase of human gait is difficult to quantify accurately in the presence of disturbances. In contrast, recent bipedal robots use time-independent controllers relying on a mechanical phase variable to synchronize joint patterns through the gait cycle. This concept has inspired studies to determine if human joint patterns can also be parameterized by a mechanical variable. Although many phase variable candidates have been proposed, it remains unclear which, if any, provide a robust representation of phase for human gait analysis or control. In this paper we analytically derive an ideal phase variable (the hip phase angle) that is provably monotonic and bounded throughout the gait cycle. To examine the robustness of this phase variable, ten able-bodied human subjects walked over a platform that randomly applied phase-shifting perturbations to the stance leg. A statistical analysis found the correlations between nominal and perturbed joint trajectories to be significantly greater when parameterized by the hip phase angle (0.95+) than by time or a different phase variable. The hip phase angle also best parameterized the transient errors about the nominal periodic orbit. Finally, interlimb phasing was best explained by local (ipsilateral) hip phase angles that are synchronized during the double-support period.

Characterizing and modeling the joint-level variability in human walking.

Although human gait is often assumed to be periodic, significant variability exists. This variability appears to provide different information than the underlying periodic signal, particularly about fall risk. Most studies on variability have either used step-to-step metrics such as stride duration or point-wise standard deviations, neither of which explicitly capture the joint-level variability as a function of time. This work demonstrates that a second-order Fourier series for stance joints and a first-order Fourier series for swing joints can accurately capture the variability in joint angles as a function of time on a per-step basis for overground walking at the self-selected speed. It further demonstrates that a total of seven normal distributions, four linear relationships, and twelve continuity constraints can be used to describe how the Fourier series vary between steps. The ability of the proposed method to create curves that match human joint-level variability was evaluated both qualitatively and quantitatively using randomly generated curves.

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.

Preliminary Experiments with a Unified Controller for a Powered Knee-Ankle Prosthetic Leg Across Walking Speeds.

This paper presents the experimental validation of a novel control strategy that unifies the entire gait cycle of a powered knee-ankle prosthetic leg without the need to switch between controllers for different periods of gait. Current control methods divide the gait cycle into several sequential periods each with independent controllers, resulting in many patient-specific control parameters and switching rules that must be tuned for a specific walking speed. The single controller presented is speed-invariant with a minimal number of control parameters to be tuned. A single, periodic virtual constraint is derived that exactly characterizes the desired actuated joint motion as a function of a mechanical phase variable across walking cycles. A single sensor was used to compute a phase variable related to the residual thigh angle's phase plane, which was recently shown to robustly represent the phase of non-steady human gait. This phase variable allows the prosthesis to synchronize naturally with the human user for intuitive, biomimetic behavior. A custom powered knee-ankle prosthesis was designed and built to implement the control strategy and validate its performance. A human subject experiment was conducted across multiple walking speeds (1 to 3 miles/hour) in a continuous sequence with the single phase-based controller, demonstrating its adaptability to the user's intended speed.

A Unified Parameterization of Human Gait Across Ambulation Modes.

This paper introduces a novel gait parameterization method that models gait kinematics as a continuous function of gait cycle phase, walking speed, and ground slope. Kinematic data was recorded from seven able-bodied subjects walking on a treadmill at twenty-seven combinations of walking speed and ground slope. Convex optimization was used to determine the parameters of a function of three variables that fits this experimental data. This function may be able to provide desired trajectories to a virtual constraint controller over a continuum of gait phases and ambulation modes. This could allow for a single, non-switching controller to control a prosthetic leg for a variety of tasks, avoiding many of the problems associated with the ubiquitous use of finite state machines in prosthesis control.

Unified Phase Variables of Relative Degree Two for Human Locomotion.

A starting point to achieve stable locomotion is synchronizing the leg joint kinematics during the gait cycle. Some biped robots parameterize a nonlinear controller (e.g., input-output feedback linearization) whose main objective is to track specific kinematic trajectories as a function of a single mechanical variable (i.e., a phase variable) in order to allow the robot to walk. A phase variable capable of parameterizing the entire gait cycle, the hip phase angle, has been used to control wearable robots and was recently shown to provide a robust representation of the phase of human gait. However, this unified phase variable relies on hip velocity, which is difficult to measure in real-time and prevents the use of derivative corrections in phase-based controllers for wearable robots. One derivative of this phase variable yields accelerations (i.e., the equations of motion), so the system is said to be relative degree-one. This means that there are states of the system that cannot be controlled. The goal of this paper is to offer relative degree-two alternatives to the hip phase angle and examine their robustness for parameterizing human gait.

A Perturbation Mechanism for Investigations of Phase Variables in Human Locomotion.

The concept of a phase variable, a mechanical measurement of the body's progression through the gait cycle, has been used to parameterize the leg joint patterns of autonomous bipedal robots, producing human-like gaits with robustness to external perturbations. It was recently proposed that the kinematic response of humans to a perturbation could also be parameterized by a phase variable. In order to properly study this phase variable hypothesis with human subjects, a custom perturbation mechanism was built to cause phase shifts in the gait cycle. The main goals of this study are to introduce the design of a novel perturbation mechanism and experimentally demonstrate its ability to effect phase changes during the gait cycle.

A survey of phase variable candidates of human locomotion.

Studies show that the human nervous system is able to parameterize gait cycle phase using sensory feedback. In the field of bipedal robots, the concept of a phase variable has been successfully used to mimic this behavior by parameterizing the gait cycle in a time-independent manner. This approach has been applied to control a powered transfemoral prosthetic leg, but the proposed phase variable was limited to the stance period of the prosthesis only. In order to achieve a more robust controller, we attempt to find a new phase variable that fully parameterizes the gait cycle of a prosthetic leg. The angle with respect to a global reference frame at the hip is able to monotonically parameterize both the stance and swing periods of the gait cycle. This survey looks at multiple phase variable candidates involving the hip angle with respect to a global reference frame across multiple tasks including level-ground walking, running, and stair negotiation. In particular, we propose a novel phase variable candidate that monotonically parameterizes the whole gait cycle across all tasks, and does so particularly well across level-ground walking. In addition to furthering the design of robust robotic prosthetic leg controllers, this survey could help neuroscientists and physicians study human locomotion across tasks from a time-independent perspective.