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In recent years, musculoskeletal computation has become a widely used tool to investigate joint and muscle forces within the human body. However, the issue of muscle fatigue is not considered adequately in most models and is a challenging task. One aspect that needs to be examined is the interaction of muscles during an exhausting task. Therefore, an experimental study was designed to analyze the changes of back muscle recruitment pattern during such exercises.
In this study 38 subjects (27 male, 11 female, height = 177±8.5 cm, weight = 74.0±13.6 kg) participated. Each subject had to perform three static and three dynamic exhausting exercises where the back muscles were loaded with subject specific forces using a dynamometer adapter especially designed for the trunk muscles. To collect the muscle activity, twelve surface electromyography sensors were applied on the back, and four on the abdominal muscles. Muscle activity and fatigue were analyzed by calculating the maximum voluntary contraction normalized signal and the median frequency. At first the fatigue of m. erector spinae and m. multifidi was analyzed, since these muscles carry the main load during the exercises. Subsequently the activity of the m. trapezius, m. rectus abdominis and m. obliquus externus were investigated to determine recruitment patterns. To gain more detailed information of these patterns a numerical model was built using the AnyBody Modeling System™. Analyzing the measurements, we can observe an increasing muscle activity during isokinetic exercises while the force is constant. Since the activity in the simulation is defined as the current force output divided by the strength of the muscle, the strength parameter was scaled down based on the measured data, assuming a linear force – activity correlation, and using a numerical algorithm considering the influence of cross talk.
The results show, that changes in recruitment pattern can be divided into three major subgroups. Prior to total exhaustion, some of the subjects show additional activation of muscles in the trapezius region, while other subjects show an additional activation of abdominal muscles, increasing the intra-abdominal pressure which supports the spine. In the third group an activation in both regions can be observed.
The numerical simulations show an increasing activity of abdominal muscles as well as muscles in the upper back. Especially the m. latissimus dorsi shows a significantly higher activity.
The results lead to the conclusion that prior to total exhaustion, additional muscles are recruited to support the main muscles. It was shown that abdominal muscles are activated to support back muscles by pressurizing the trunk cavity to delay total exhaustion as long as possible. In conclusion, the results show that changes in muscle recruitment pattern need to be considered when introducing muscle fatigue to musculoskeletal models.
The development of biomimetic robots has gained research interest in the last years as it may both help under-standing processes of motion execution in biological systems as well as developping a novel generation of intelligent and energy efficient robots. However, exact model generation that builds up on observations and robot design is very time intensive. In this paper we present a novel pipeline for co-development of biomimetic hardware and simulation models based on biological Computer Tomography (CT) data. For this purpose we exploit State of the Art rapid prototyping technologies such as 3D Printing and the Neurorobotics Platform for musculoskeletal simulations in virtual environments. The co-development integrates both advantages of virtual and physical experimental models and is expected to increase development speed of controllers that can be tested on the simulated counterpart before application to a printed robot model. We demonstrate the pipeline by generating a one year old infant model as a musculoskeletal simulation model and a print-in-place 3D printed skeleton as a single movable part. Even though we hereonly introduce the initial body generation and only a first testsetup for a modular sensory and control framework, we can clearly spot advantages in terms of rapid model generation and highly biological related models. Engineering costs are reducedand models can be provided to a wide research community for controller testing in an early development phase.
PURPOSE:
The purpose of this study was to find a suitable method of labelling cartilage samples for the measurement of distraction distances in biomechanical testing.
METHODS:
Samples of bovine cartilage were labelled using five different methods: hydroquinone and silver nitrate (AgNO3), potassium permanganate (KMnO4) with sodium thiosulphate (Na2S2O3), India ink, heat, and laser energy. After the labelling, we analysed the cartilage samples with regard to cytotoxity by histochemical staining with ethidiumbromide homodimer (EthD-1) and calcein AM. Furthermore, we tested cartilages labelled with India ink and heat in a T-peel test configuration to analyse possible changes in the mechanical behaviour between marked and unlabelled samples.
RESULTS:
Only the labelling methods with Indian ink or a heated needle showed acceptable results in the cytotoxity test with regard to labelling persistence, accuracy, and the influence on consistency and viability of the chondrocytes. In the biomechanical T-peel configuration, heat-labelled samples collapsed significantly earlier than unlabelled samples.
CONCLUSION:
Labelling bovine cartilage samples with Indian ink in biomechanical testing is a reliable, accurate, inexpensive, and easy-to-perform method. This labelling method influenced neither the biomechanical behaviour nor the viability of the tissue compared to untreated bovine cartilage.
Biomechanik des Alterns
(2016)