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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.