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This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle(More)
Nonlinearities have been observed in the isometric EMG-force relationship. However, these are generally not included when using EMG-driven Hill-type muscle models that account for muscle activation dynamics. In this paper, we present a formulation for a one-parameter transformation model (i.e., A-model) that accounts for the type of physiological(More)
The external knee adduction moment during walking and stair climbing has a characteristic double hump pattern. The magnitude of the adduction moment is associated with the development and progression of medial compartment knee osteoarthritis (OA). There is an inverse relationship between the magnitude of the second peak adduction moment and foot progression(More)
Ultrasonography was used to measure pennation angle and electromyography (EMG) to record muscle activity of the human tibialis anterior (TA), lateral gastrocnemius (LG), medial gastrocnemius (MG), and soleus (SOL) muscles during graded isometric ankle plantar and dorsiflexion contractions done on a Biodex dynamometer. Data from 8 male and 8 female subjects(More)
An EMG-driven virtual arm is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The virtual arm incorporates the major muscles spanning the elbow joint and is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). It is able to estimate joint moments and the(More)
PURPOSE This paper presents a forward dynamic neuromusculoskeletal model that can be used to estimate and predict joint moments and muscle forces. It uses EMG signals as inputs to the model, and joint moments predicted are verified through inverse dynamics. The aim of the model is to estimate or predict muscle forces about a joint, which can be used to(More)
Computational models that predict internal joint forces have the potential to enhance our understanding of normal and pathological movement. Validation studies of modeling results are necessary if such models are to be adopted by clinicians to complement patient treatment and rehabilitation. The purposes of this paper are: (1) to describe an electromyogram(More)
Individuals following stroke exhibit altered muscle activation and movement patterns. Improving the efficiency of gait can be facilitated by knowing which muscles are affected and how they contribute to the pathological pattern. In this paper we present an electromyographically (EMG) driven musculoskeletal model to estimate muscle forces and joint moments.(More)
Relative motion plots are the most prevalent method for displaying interjoint coupling. The method, however, is limited when amplitude and timing comparisons of like data are of interest. Another limitation of relative motion plots is that the second parameter (e.g., angle) is included at the expense of a continuous time reference. In this paper, we present(More)
The purpose of this study was to develop a biomechanical model to estimate anterior tibial translation (ATT), anterior shear forces, and ligament loading in the healthy and anterior cruciate ligament (ACL)-deficient knee joint during gait. This model used electromyography (EMG), joint position, and force plate data as inputs to calculate ligament loading(More)