Investigation of intersegmental coordination patterns in human walking.

BACKGROUND: Intersegmental coordination between thigh, shank, and foot plays a crucial role in human gait, facilitating stable and efficient human walking. Limb elevation angles during the gait cycle form a planar manifold describes the by the planar covariation law, a recognized fundamental aspect of human locomotion. RESEARCH QUESTION: How does the walking speed, age, BMI, and height, affect the size and orientation of the intersegmental coordination manifold and covariation plane? METHODS: This study introduces novel metrics for quantifying intersegmental coordination, including the mean radius of the manifold, rotation of the manifold about the origin, and the orientation of the plane with respect to the coordinate planes. A statistical investigation is conducted on a publicly available human walking dataset for subjects aged 19-67 years, walking at speeds between 0.18 and 2.3 m s-1 to determine correlations of the proposed quantities. We used two sample t-test and ANOVA to find statistical significance of changes in the metrics with respect to gender and walking speed, respectively. Regression analysis was used to establish relationships between the introduced metrics and walking speed. RESULTS: High correlations are observed between walking speed and the computed metrics, highlighting the sensitivity of these metrics to gait characteristics. Conversely, negligible correlations are found for demographic parameters like age, body mass index (BMI), and height. Male and female groups exhibit no practically significant differences in any of the considered metrics. Additionally, metrics tend to increase in magnitude as walking speed increases. SIGNIFICANCE: This study contributes numerical metrics to characterize ISC of lower limbs with respect to walking speed along with regression models to estimate these metrics and related kinematic quantities. These findings hold significance for enhancing clinical gait analysis, generating optimal walking trajectories for assistive devices, prosthetics, or rehabilitation, aiming to replicate natural gaits and improve the functionality of biomechanical devices.

Intersegmental coordination in human slip perturbation responses.

Intersegmental coordination (ISC) of lower limbs and planar covariation law (PCL) are important phenomena observed in biomechanics of human walking and other activities. Gait perturbations tend to cause deviation from the expected ISC pattern thus violating PCL. We used a data set of seven subjects, who experienced unexpected slips, to investigate and characterize the evolution of ISC during slip recoveries and falls. We have analyzed and presented the development of ISC patterns, encompassing the step preceding the slip initiation and duration of slip until it stops. The results show that the ISC patterns during slip recovery deviate considerably from the normal walking patterns. A newly proposed Euclidian distance-based metric (EDM) was used to quantify the deviation from the normal walking ISC pattern during four slip recoveries and three falls evaluated at gait events such as slip start, foot strike, and peak height of the swing foot. The timing of gait events after slip, pattern of EDM, placement of the feet after slip and temporal patterns of each limb angle have been presented. This initial investigation provides insight into the ISC during slip recovery which highlights the human natural recovery trajectories during such perturbations. The observed patterns of the ISC trajectories during slip can be used for the design of human-inspired controllers for exoskeleton devices that can provide external assistance to human subjects during balance recovery.

Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles.

Improper manual material handling (MMH) techniques are shown to lead to low back pain, the most common work-related musculoskeletal disorder. Due to the complex nature and variability of MMH and obtrusiveness and subjectiveness of existing hazard analysis methods, providing systematic, continuous, and automated risk assessment is challenging. We present a machine learning algorithm to detect and classify MMH tasks using minimally-intrusive instrumented insoles and chest-mounted accelerometers. Six participants performed standing, walking, lifting/lowering, carrying, side-to-side load transferring (i.e., 5.7 kg and 12.5 kg), and pushing/pulling. Lifting and carrying loads as well as hazardous behaviors (i.e., stooping, overextending and jerky lifting) were detected with 85.3%/81.5% average accuracies with/without chest accelerometer. The proposed system allows for continuous exposure assessment during MMH and provides objective data for use with analytical risk assessment models that can be used to increase workplace safety through exposure estimation.

Balance Recoverability and Control of Bipedal Walkers With Foot Slip.

Low-friction foot/ground contacts present a particular challenge for stable bipedal walkers. The slippage of the stance foot introduces complexity in robot dynamics and the general locomotion stability results cannot be applied directly. We relax the commonly used assumption of nonslip contact between the walker foot and the ground and examine bipedal dynamics under foot slip. Using a two-mass linear inverted pendulum model, we introduce the concept of balance recoverability and use it to quantify the balanced or fall-prone walking gaits. Balance recoverability also serves as the basis for the design of the balance recovery controller. We design the within- or multi-step recovery controller to assist the walker to avoid fall. The controller performance is validated through simulation results and robustness is demonstrated in the presence of measurement noises as well as variations of foot/ground friction conditions. In addition, the proposed methods and models are used to analyze the data from human walking experiments. The multiple subject experiments validate and illustrate the balance recoverability concept and analyses.

Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning.

We present two frameworks for design optimization of a multi-chamber pneumatic-driven soft actuator to optimize its mechanical performance. The design goal is to achieve maximal horizontal motion of the top surface of the actuator with a minimum effect on its vertical motion. The parametric shape and layout of air chambers are optimized individually with the firefly algorithm and a deep reinforcement learning approach using both a model-based formulation and finite element analysis. The presented modeling approach extends the analytical formulations for tapered and thickened cantilever beams connected in a structure with virtual spring elements. The deep reinforcement learning-based approach is combined with both the model- and finite element-based environments to fully explore the design space and for comparison and cross-validation purposes. The two-chamber soft actuator was specifically designed to be integrated as a modular element into a soft robotic pad system used for pressure injury prevention, where local control of planar displacements can be advantageous to mitigate the risk of pressure injuries and blisters by minimizing shear forces at the skin-pad contact. A comparison of the results shows that designs achieved using the deep reinforcement based approach best decouples the horizontal and vertical motions, while producing the necessary displacement for the intended application. The results from optimizations were compared computationally and experimentally to the empirically obtained design in the existing literature to validate the optimized design and methodology.

Gradient optimization of multi-layered density-graded foam laminates for footwear material design.

Several sports-related injuries and orthopedic treatments need the necessity of corrective shoes that can assuage the excessive pressure on sensitive locations of the foot. In the present work, we study the mechanical and energy absorption characteristics of density-graded foams designed for shoe midsoles. The stress-strain responses of polyurea foams with relative densities (nominal density of foam divided by the density of water) of 0.095, 0.23, and 0.35 are obtained experimentally and used as input to a semi-analytical model. Using this model, three-layered foam laminates with various gradients are designed and characterized in terms of their weight, strength, and energy absorption properties. We show that, in comparison with monolithic foams, significant improvement in strength and energy absorption performance can be achieved through density gradation. Our findings also suggest that there is not a single gradient that offers a superior combination of strength, energy absorption, and weight. Rather, an optimal gradient depends on the plantar location and pressure. Depending on the magnitude of the local plantar pressure, density gradients that lead to the highest specific energy absorption are identified for normal walking and running conditions.

Shoe-Floor Interactions in Human Walking With Slips: Modeling and Experiments.

Shoe-floor interactions play a crucial role in determining the possibility of potential slip and fall during human walking. Biomechanical and tribological parameters influence the friction characteristics between the shoe sole and the floor and the existing work mainly focus on experimental studies. In this paper, we present modeling, analysis, and experiments to understand slip and force distributions between the shoe sole and floor surface during human walking. We present results for both soft and hard sole material. The computational approaches for slip and friction force distributions are presented using a spring-beam networks model. The model predictions match the experimentally observed sole deformations with large soft sole deformation at the beginning and the end stages of the stance, which indicates the increased risk for slip. The experiments confirm that both the previously reported required coefficient of friction (RCOF) and the deformation measurements in this study can be used to predict slip occurrence. Moreover, the deformation and force distribution results reported in this study provide further understanding and knowledge of slip initiation and termination under various biomechanical conditions.