Implementation and Validation of a Detailed 3D Inverse Dynamics Lower Extremity Model for Gait Analysis Applications Based on Optimization Technique

The goal of this research work was to introduce the whole process of developing and validating a 3D lower extremity musculoskeletal model and to test the ability of the model to predict the muscles recruitment of the different muscles involved in human locomotion as well as determining the corresponding forces and moments generated around the different joints in the lower extremity. Therefore the model can be applied in one of the important fields of orthopaedics which is joint replacement; the case study used in such application is the total knee replacement. The knee reaction forces were compared to the pattern obtained by Harrington (1992), where the hip moment components (Flexion/extension, internal/external, and abduction/adduction) were all compared to the patterns obtained from the Hip98 data base. It was shown in the different graphs of joints forces and moments that the model was able to produce very close results when comparing pattern and magnitude to the literature data. Thus, this 3D biomechanical model is sophisticated enough to be used for surgery evaluation such as in total knee replacement, where the damaged cartilage and bone are removed from the surface of the knee joint and replaced with a man-made. The case study of the second part of the research work presented involved the comparison of the gait pattern between two main knee joint types, Metallic and Allograft knee joints against normal subjects (Control group). A total of fifteen subjects participated in this study, five subjects in each group. It was concluded that based on the study conducted and the statistical evidence obtained that the introduced model can be used for applications that involves joint surgeries such as knee replacement that ultimately can be utilized in surgery evaluation

Using vicon bodybuilder and plug-in-gait to generate L5/S1 angles, forces and moments

Currently the most widely used and accepted Vicon model is the Plug-in-Gait (PIG). Unfortunately, the PIG output value for the lumbar section only provides the angle difference between the thorax and the pelvis. Because the PIG is so widely used by advanced biomechanical analysts for defining body segment kinematics for aerospace and other industries, it would be a great advantage to be able to enhance the fidelity of the PIG model outputs by attaching body regions, such as spinal sections. Thus, the work explained in this paper describes how a virtual lumbar segment which generates model angle output values for the L5/S1 can be easily added to the PIG body segment definition, using the BodyBuilder (BB) for Biomechanics programming language. The methodology described in this paper utilizes the original PIG marker set and does not require any additional markers. Furthermore, the same method and model can provide L5/S1 forces and moments

Careful and accurate placement of avionics boxes during maintenance of flight hardware

Because a key driver to the cost of launching aerospace hardware into space is its weight, the design of the flight hardware has many trade-offs to reduce the overall weight of the flight vehicle. These trade-offs usually result in reduced work space for maintaining and repairing the flight hardware. One very common ground replaceable flight hardware in aerospace launch and crewed vehicles are the avionics box. Which during the installation of these avionics boxes they must be accurately and carefully placed to reduce damage to hardware, such as the cold plate, wiring, etc. The focus of this paper is to describe the results of a study set out to determine how accurately and carefully a human can place an avionics box in restricted space

The effect of high-heeled shoe design on lower extremity kinetics, kinematics, and electromyography

Many studies have investigated the differences in gait patterns with increasing heel height. The purpose of this study was to study the differences in gait patterns when wearing two high-heeled shoes (9 cm) deigns versus barefoot. Changes in lower extremity kinetics, kinematics and integrated electromyography (IEMG) were explored on 15 female college students (19–31 years). Increased vertical ground reaction forces during both early and late stance were recorded when wearing high-heeled shoes. Also, an increase in the IEMG values of soleus and lateral head of the gastrocnemius muscles were noted during stance, while medial head of the gastrocnemius EMG values decreased in both types of high-heeled shoes. IEMG of tibialis anterior was also decreased throughout swing phase due to more plantar flexed foot position

Development and validation of a three dimensional dynamic biomechanical lifting model for lower back evaluation for careful box placement

One of the major causes to low back injury is the box lifting activity, thus for many years biomechanics has been utilized by designers for ergonomic evaluations of the box lifting activity which includes the placement of the box. More recently these ergonomic investigations have focused on the careful placement of the box. The AnyBody (AB) biomechanical models and optimization within the AB software system in conjunction with motion capture has been shown to obtain adequate estimates of joint reaction forces of the body. To date there has not been a dynamic 3D box lifting model developed and validated for carefully placing a box using the AB modeling system and motion capture. Thus the focus of this paper is on the development, verification and validation of a box lifting full body model for lower back evaluations for a dynamic lifting activity for carefully placing a box on a shelf. [Display omitted] •A biomechanical model was developed for the activity of placing a box accurately on a shelf.•The model was validated by comparing the predicted muscle activity to the measured muscle activity.•The model was applied to a 30″ and 50″ shelf height showing that the 30″ shelf height imposes more stress to the lower back

Short-Term Electrical Peak Demand Forecasting in a Large Government Building Using Artificial Neural Networks

The power output capacity of a local electrical utility is dictated by its customers’ cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility’s bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs), provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA), linear regression, and multivariate adaptive regression splines (MARSplines) and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE) of 3.9% as compared to the SMA, linear regression, and MARSplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME) of 8.2% as compared to the SMA, linear regression, and MARSplines AMEs of 26.2%, 45.1%, and 22.5% respectively