上海泰克尔体育科技有限责任公司

Shanghai Teker Sports Technology Co., Ltd.
 
Computational Biomechanics
Computational biomechanics involves data mining of biomechanical data, which often includes deep learning, machine learning, or various complex computations. There is typically no built-in computation functionality in commercial software for these purposes, so researchers often need to replicate data mining methods from research papers. For example, assessing the stability of human postural control from a nonlinear dynamics perspective, or using non-negative matrix factorization methods to analyze electromyography (EMG) data to obtain spatial and temporal patterns of muscle activation, which can be used to evaluate muscle coordination patterns. Computational biomechanics delves into the foundational aspects of biomechanics research, requiring researchers to have a theoretical background in calculus, linear algebra, and differential equations, and proficiency in at least one programming language, such as Python, MATLAB, R, etc. Conducting research in this field often demands a significant investment in learning and skill development.
Nonlinear Dynamics
Nonlinear dynamics is a vital and active branch of the disciplines of dynamics and control theory, primarily focusing on the qualitative and quantitative laws of various motion modes and evolutionary processes in nonlinear systems. It is particularly concerned with the transitions between different motion modes and the complexity of systems' long-term behavior. Given the highly nonlinear characteristics of the human body, nonlinear dynamics has seen many applications in the field of biomechanics in recent years. Common theories in nonlinear dynamic systems include entropy analysis originating from chaos theory and Lyapunov stability theory.
Entropy analysis evaluates the response of the central nervous system (CNS) to external disturbances through "complexity," as entropy values reflect the disorder level of biological systems. In another sense, the level of disorder also indicates the level of activity. The image on the right demonstrates the decline in the CNS's response capability to external disturbances with increasing age. The maximum Lyapunov exponent, on the other hand, reflects the stability of dynamic systems. The image on the right shows a strange attractor formed during walking.
Our has the capability to process and analyze clients' data using various nonlinear dynamics methods.
As age increases, the complexity of the Central Nervous System decreases.
Lyapunov Stability in Gait
Machine learning has been widely applied in numerous fields in recent years, and biomechanics is no exception. Machine learning offers new perspectives for secondary data mining. For instance, non-negative matrix factorization techniques can be used to process electromyography (EMG) data to extract muscle coordination patterns. Principal Component Analysis (PCA) can reduce the dimensions of continuous numerical data to extract more useful information. Generative Adversarial Networks (GANs) can be employed for data set augmentation, particularly useful when it is challenging to collect extensive data from patients with certain diseases.
Our can develop various machine learning algorithms tailored to users' needs for processing biomechanical data, creating customized script programs for this purpose.
Machine Learning
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