Abstract: Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone. In some embodiments, a method comprises: detecting a crash event on a crash device; extracting multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features, wherein at least one multimodal feature is a rotation rate about a mean axis of rotation; and determining that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Abstract: Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). In some embodiments, a method comprises: detecting, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Abstract: The present invention relates to a computer-implemented method for computing a neuromarker of Alzheimer's disease comprising the steps of obtaining at least one spectral feature from EEG signals of a subject; obtaining at least one Riemannian distance between a spatiofrequential covariance matrix computed from the EEG signals of said subject and at least one reference spatiofrequential covariance matrix; and combining said at least one spectral feature and said at least one Riemannian distance in a mathematical function. The present invention also relates to a method for self-paced modulation of EEG signals of a subject in order to alleviate symptoms of Alzheimer's disease using the predictive neuromarkers of Alzheimer's disease.

Abstract: Apparatus for elevating and supporting an associated riding lawn mower which includes a hand truck assembly having a generally inverted U-shaped frame attached to an axle cooperating with first and second axially spaced wheels carried thereon and having a generally planar tongue for supporting an associated objects to be moved; and a carriage assembly dimensioned and configured for mounting on the hand truck assembly, the carriage assembly wheels have supports for engaging the respective first and second wheels of the associated riding lawn mower, the carriage assembly is supported on the hand truck assembly by means of an opening in the carriage assembly engaging the tongue of the hand truck assembly whereby placement of the associated lawnmower on the supports of the carriage assembly while engaged with the hand truck assembly followed by rotational movement of the frame of the hand truck assembly from a generally vertical position to a generally horizontal position results in the elevation of the associate