Falling accidents are costly due to their prevalence in the workplace. Slipping has been known to be the main cause of falling. Understanding the motor response used to regain balance after slipping is crucial to developing intervention strategies for effective recovery. Interestingly, studies on spinalized animals and studies on animals subjected to electrical microstimulation have provided major evidence that the Central Nervous System (CNS) uses motor primitives, such as muscle synergies, to control motor tasks. Muscle synergies are thought to be a critical mechanism used by the CNS to control complex motor tasks by reducing the dimensional complexity of the system. Even though synergies have demonstrated potential for indicating how the body responds to balance perturbations by accounting for majority of the data set's variability, this concept has not been applied to slipping. To address this gap, data from 11 healthy young adults were collected and analyzed during both unperturbed walking and slipping. Applying an iterative non-negative matrix decomposition technique, four muscle synergies and the corresponding time-series activation coefficients were extracted. The synergies and the activation coefficients were then compared between baseline walking and slipping to determine shared vs. task-specific synergies. Correlation analyses found that among four synergies, two synergies were shared between normal walking and slipping. However, the other two synergies were task-specific. Both limbs were contributing to each of the four synergies, suggesting substantial inter-limb coordination during gait and slip. These findings stay consistent with previous unilateral studies that reported similar synergies between unperturbed and perturbed walking. Activation coefficients corresponding to the two shared synergies were similar between normal walking and slipping for the first 200 ms after heel contact and differed later in stance, suggesting the activation of muscle synergies in response to a slip. A muscle synergy approach would reveal the used sub-tasks during slipping, facilitating identification of impaired sub-tasks, and potentially leading to a purposeful rehabilitation based on damaged sub-functions.
Neurological disorders are the leading causes of poor balance. Previous studies have shown that biofeedback can compensate for weak or missing sensory information in people with sensory deficits. These biofeedback inputs can be easily recognized and converted into proper information by the central nervous system (CNS), which integrates the appropriate sensorimotor information and stabilizes the human posture. In this study, we proposed a form of cutaneous feedback which stretches the fingertip pad with a rotational contactor, so-called skin stretch. Skin stretch at a fingertip pad can be simply perceived and its small contact area makes it favored for small wearable devices. Taking advantage of skin stretch feedback, we developed a portable sensory augmentation device (SAD) for rehabilitation of balance. SAD was designed to provide postural sway information through additional skin stretch feedback. To demonstrate the feasibility of the SAD, quiet standing on a force plate was evaluated while sensory deficits were simulated. Fifteen healthy young adults were asked to stand quietly under six sensory conditions: three levels of sensory deficits (normal, visual deficit, and visual + vestibular deficits) combined with and without augmented sensation provided by SAD. The results showed that augmented sensation via skin stretch feedback helped subjects correct their posture and balance, especially as the deficit level of sensory feedback increased. These findings demonstrate the potential use of skin stretch feedback in balance rehabilitation.
Abstract-A catadioptric vision system combines a camera and a mirror to achieve a wide field of view imaging system. This type of vision system has many potential applications in mobile robotics. This paper is concerned with the design of a robust image-based control scheme using a catadioptric vision system mounted on a mobile robot. We exploit the fact that the decoupling property contributes to the robustness of a control method. More precisely, from the image of a point, we propose a minimal and decoupled set of features measurable on any catadioptric vision system. Using the minimal set, a classical control method is proved to be robust in the presence of point range errors. Finally, experimental results with a coarsely calibrated mobile robot validate the robustness of the new decoupled scheme.
Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).
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