Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction. Despite decades of research, it remains unclear how the human central nervous system (CNS) coordinates activity of a large number of muscles during movement. Numerous studies suggest that the CNS activates muscles in groups to reduce the complexity required to control each individual muscle when performing a movement 1,2. The coordination of muscles activated in synchrony is commonly referred to as muscle synergy 3. Indirect evidence suggests that muscle synergies reside in the brainstem and/or spinal cord and follow a modular organization 4-6. Muscle synergies are regarded as low dimensional units that produce complex activation patterns for a group of muscles, typically recorded via electromyography (EMG), during performance of a task 4,6. Synergies can be observed at cortical 7 or spinal 8 levels, suggesting a high degree of cooperation within the CNS's structural hierarchy 9. Understanding the organisation of muscle synergies may help elucidate the neurological mechanisms that underpin a multitude of neurological conditions, including stroke 10-12 , cerebral palsy 13,14 , spinal cord injury 15 , and Parkinson's Disease 16. Factorisation methods use recorded and processed EMG signals, from here referred to as muscle activation patterns, to quantify muscle synergies. A number of different factorisation methods have been used to extract muscle synergies from muscle activation patterns during dynamic tasks. The four most commonly used factorisation methods reported in the literature between 1999-2018 are non-negative matrix factorisation (NMF) 13,14,17-20 (62.28%), principal component analysis (PCA) 21-24 (23.11%), independent component analysis (ICA) 25,26 (3.22%), and factor analysis (FA) 27,28 (2.15%) (Literature search in Supplementary material). Applying a factorisation method to a set of muscle activation patterns yields two compon...

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Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

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