The Complexity of Human Walking: A Knee Osteoarthritis Study

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space

Malaysia attempts in reduce carbon dioxide (CO2) emission and sequestration in bio-concrete system; a future direction

In Malaysia, upbringing the production of mussel is one of the most second important aquaculture The greenhouse gasses increase in this century especially carbon dioxide (CO2) compare to the previous centuries due to the increase of anthropogenic activities in all countries around the world [1][2][3]. The high concentration CO2 in the atmospheric cause a catastrophic environmental issues such as; global warming, change in rainfall, rise of sea level and climatic changes.

Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics

Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process (gP) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our gP approach achieves high levels of performance (RMSE of 8°/s and ρ = 0.79). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural hand movements. gP autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that gP autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements

Decomposing sensorimotor variability changes in ageing and their connection to falls in older people

The relationship between sensorimotor variability and falls in older people has not been well investigated. We developed a novel task having shared biomechanics of obstacle negotiation to quantify sensorimotor variability related to locomotion across age. We found that sensorimotor variability in foot placement increases continuously with age. We then applied sensory psychophysics to pinpoint the visual and somatosensory systems associated with sensorimotor variability. We showed increased sensory variability, specifically increased proprioceptive variability, the vital cause of more variable foot placement in older people (greater than 65 years). Notably, older participants relied more on the vision to judge their own foot’s height compared to the young, suggesting a shift in multisensory integration strategy to compensate for degenerated proprioception. We further modelled the probability of tripping-over based on the relationship between sensorimotor variability and age and found a correspondence between model prediction and community-based data. We reveal increased sensorimotor variability, modulated by sensation precision, a potentially vital mechanism of raised tripping-over and thus fall events in older people. Analysis of sensorimotor variability and its specific components, may have the utility of fall risk and rehabilitation target evaluation