A valid novel ground reaction force distribution algorithm to determine midfoot kinetics of gait with a single force plate.

Background: Understanding midfoot joint kinetics is valuable for improved treatment of foot pathologies. Segmental foot kinetics cannot currently be obtained in a standard gait lab without the use of multiple force plates or a pedobarographic plate overlaid with a force plate due to the single ground reaction force (GRF) vector.

Research Question: Can an algorithm be created to distribute the GRF into multiple segmental vectors that will allow for calculation of accurate midfoot and ankle moments?

Methods: 20 pediatric subjects (10 typically developing, 10 with foot pathology) underwent multi-segment foot gait analysis using the Milwaukee Foot Model.

Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions.

A hallmark of human locomotion is that it continuously adapts to changes in the environment and predictively adjusts to changes in the terrain, both of which are major challenges to lower limb amputees due to the limitations in prostheses and control algorithms. Here, the ability of a single-network nonlinear autoregressive model to continuously predict future ankle kinematics and kinetics simultaneously across ambulation conditions using lower limb surface electromyography (EMG) signals was examined. Ankle plantarflexor and dorsiflexor EMG from ten healthy young adults were mapped to normal ranges of ankle angle and ankle moment during level overground walking, stair ascent, and stair descent, including transitions between terrains (i.

Dynamic Simulation of Human Gait Model With Predictive Capability.

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS.

Within-socket myoelectric prediction of continuous ankle kinematics for control of a powered transtibial prosthesis.

Objective: Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees.

Approach: Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal 'prediction' interval between the EMG/kinematic input and the model's estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval.

Mechanical vibration inhibits osteoclast formation by reducing DC-STAMP receptor expression in osteoclast precursor cells.

It is well known that physical inactivity leads to loss of muscle mass, but it also causes bone loss. Mechanistically, osteoclastogenesis and bone resorption have recently been shown to be regulated by vibration. However, the underlying mechanism behind the inhibition of osteoclast formation is yet unknown.

Concept Through Preliminary Bench Testing of a Powered Lower Limb Prosthetic Device.

This paper outlines the design and testing of a powered ankle prosthesis, which utilizes a four-bar mechanism in conjunction with a spring and motor that mimics nonamputee (normal) ankle moments. This approach would enable transtibial (below the knee) amputees to walk at a normal speed with minimal energy input. The design takes into account the energy supplied by the wearer required to achieve many of the desired characteristics of a normal gait.