Assistive Technology
Dataset for validation of a new magnetic field-based wearable breathing sensor (MAG), which uses the movement of the chest wall as a surrogate measure of respiratory activity. Based on the principle of variation in magnetic field strength with the distance from the source, this system explores Hall effect sensing, paired with a permanent magnet, embedded in a chest strap.
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Abstract: Recent advances in computer vision and deep learning are allowing researchers to develop automated environment recognition systems for robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms (e.g., convolutional neural networks) for recognizing the human walking environment.
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Previous studies of robotic leg prostheses and exoskeletons with regenerative actuators have focused almost exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical work and power during stand-to-sit movements using inverse dynamics to estimate the biomechanical energy theoretically available for electrical energy regeneration and storage. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were experimentally measured.
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