A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition.

Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.

Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human-machine system. However, the question remains of what can be accomplished with just one sensor.

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements.

Estimating Compressive and Shear Forces at L5-S1: Exploring the Effects of Load Weight, Asymmetry, and Height Using Optical and Inertial Motion Capture Systems.

This study assesses the agreement of compressive and shear force estimates at the L5-S1 joint using inertial motion capture (IMC) within a musculoskeletal simulation model during manual lifting tasks, compared against a top-down optical motion capture (OMC)-based model. Thirty-six participants completed lifting and lowering tasks while wearing a modified Plug-in Gait marker set for the OMC and a full-body IMC set-up consisting of 17 sensors. The study focused on tasks with variable load weights, lifting heights, and trunk rotation angles.

Plantar threshold sensitivity assessment using an automated tool-Clinical assessment comparison between a control population without type 2 diabetes mellitus, and populations with type 2 diabetes mellitus, with and without neuropathy symptoms.

Diabetic peripheral neuropathy is often classified as a loss of sensation in the extremities, particularly in elderly populations. The most common diagnosis technique is with the use of the hand-applied Semmes-Weinstein monofilament. This study's first aim was to quantify and compare sensation on the plantar surface in healthy and type 2 diabetes mellitus populations with the standard Semmes-Weinstein hand-applied methodology and a tool that automates this approach.

Cognitive dual-task alters Local Dynamic Stability of lower extremity during common movements.

A dual-task paradigm is most commonly used in the field of biomechanics to understand the effect of multi-tasking or cognitive load on motor performance. The Local Dynamic Stability (LDS) is most commonly used to quantify motor performance, but there are still several unknown effects of this metric with varied task conditions and cognitive demands. Therefore, this study used motion capture to collect biomechanical data from 28 healthy collegiate participants during a walk and jog task both with and without a semantic fluency task to investigate the effects of task speed, limb dominance, and semantic fluency on LDS.

Local dynamic stability of the lower-limb as a means of post-hoc injury classification.

Since most sporting injuries occur at the lower extremity (50% to 66%) and many of those injuries occur at the knee (30% to 45%), it is important to have robust metrics to measure risk of knee injury. Dynamic measures of knee stability are not commonly used in existing metrics but could provide important context to knee health and improve injury screening effectiveness. This study used the Local Dynamic Stability (LDS) of knee kinematics during a repetitive vertical jump to perform a post-hoc previous injury classification of participants.

EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee.

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future.

Knee Valgus Versus Knee Abduction Angle: Comparative Analysis of Medial Knee Collapse Definitions in Female Athletes.

The knee valgus angle (KVA) is heavily researched as it has been shown to correlate to anterior cruciate ligament (ACL) injuries when measured during jumping activities. Many different methods of KVA calculation are often treated as equivalent. The purpose of this study is to elucidate differences between these commonly used angles within and across activities to determine if they can indeed properly be treated as equivalent.

Effects of age and obesity on trunk kinetics and kinematics during dominant side one-handed carrying.

The proportions of older and obese people are increasing in both the general and working populations worldwide. Older and obese individuals are more susceptible to work-related musculoskeletal disorders (MSDs) in comparison with healthy, younger individuals. Manual material handling (MMH) is associated with the development of work-related MSDs.

Trunk muscle activity among older and obese individuals during one-handed carrying.

Manual material handling (MMH) is associated with the development of work-related musculoskeletal disorders (MSDs). One-handed carrying is a particularly challenging form of MMH. Age and obesity have been increasing among the general and working populations in the United States and worldwide.

Relationship Between Knee Mechanics and Time Since Injury in ACL-Deficient Knees Without Signs of Osteoarthritis.

Background: There is increasing evidence that kinematic changes after anterior cruciate ligament (ACL) injury can influence the risk for premature osteoarthritis. However, kinematics can change over time, and the factors influencing those changes remain unknown but potentially important.

Hypothesis/purpose: The purpose of this study was to perform gait analysis on a population of ACL-deficient (ACLD) subjects without knee osteoarthritis after considerable time had elapsed since their injuries.

Three-dimensional knee moments of ACL reconstructed and control subjects during gait, stair ascent, and stair descent.

Changes in knee mechanics following anterior cruciate ligament reconstruction (ACLR) have been implicated as a contributor to the development of premature osteoarthritis (OA). However, changes in ambulatory loading in this population have not been well documented. While the magnitude of the external knee moment vector is a major factor in loading at the knee, there is not a comprehensive understanding of the changes in the individual components of the vector following ACL reconstruction.