Abstract-Soft miniaturized untethered grippers can be used to manipulate and transport biological material in unstructured and tortuous environments. Previous studies on control of soft miniaturized grippers employed cameras and optical images as a feedback modality. However, the use of cameras might be unsuitable for localizing miniaturized agents that navigate within the human body. In this paper, we demonstrate the wireless magnetic motion control and planning of soft untethered grippers using feedback extracted from B-mode ultrasound images. Results show that our system employing ultrasound images can be used to control the miniaturized grippers with an average tracking error of 0.4±0.13 mm without payload and 0.36±0.05 mm when the agent performs a transportation task with a payload. The proposed ultrasound feedback magnetic control system demonstrates the ability to control miniaturized grippers in situations where visual feedback cannot be provided via cameras.
To support stroke survivors in activities of daily living, wearable soft-robotic gloves are being developed. An essential feature for use in daily life is detection of movement intent to trigger actuation without substantial delays. To increase efficacy, the intention to grasp should be detected as soon as possible, while other movements are not detected instead. Therefore, the possibilities to classify reach and grasp movements of stroke survivors, and to detect the intention of grasp movements, were investigated using inertial sensing. Hand and wrist movements of 10 stroke survivors were analyzed during reach and grasp movements using inertial sensing and a Support Vector Machine classifier. The highest mean accuracies of 96.8% and 83.3% were achieved for single-and multiuser classification respectively. Accuracies up to 90% were achieved when using 80% of the movement length, or even only 50% of the movement length after choosing the optimal kernel per person. This would allow for an earlier detection of 300-750ms, but at the expense of accuracy. In conclusion, inertial sensing combined with the Support Vector Machine classifier is a promising method for actuation of graspsupporting devices to aid stroke survivors in activities of daily living. Online implementation should be investigated in future research.
Background: Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to estimate metabolic energy consumption to correct hormone administration levels, considerable improvements to the system can be made. Therefore, this research aimed to investigate the possibility to use the current system to identify several postures and movements. Methods: seven healthy participants took part in an experiment where sequences of postures and movements were performed to train and assess a computationally sparing algorithm. Results: Using accelerometers, one on the hip and two on the abdomen, user-specific models achieved classification accuracies of 86.5% using only the hip sensor and 87.3% when including the abdomen sensors. With additional accelerometers on the sternum and upper leg for identification, 90.0% of the classified postures and movements were correct. Conclusions: The current hardware configuration of the AP™ poses no limitation to the identification of postures and movements. If future research shows that identification can still be done accurately in a daily life setting, this algorithm may be an improvement for the AP™ to sense physical activity.
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