Abstract: A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.

Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

Abstract: A system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals.

Abstract: A system for passively predicting and detecting falls using one or more dual-polarized Doppler radars and machine learning algorithms. The system is typically implemented for use in predicting or detecting falls in older adults and may be connected with various systems that can alert emergency services or hospice personnel in the event of a fallen individual. Furthermore, the system overcomes conventional radar systems by integrating vertical and horizontal micro-Doppler signatures into a combined signature which is analyzed by machine learning algorithms to correctly and expeditiously predict and detect a variety of human movements. The system also finds applications wherever micro-Doppler signals may be generated such as predicting or detecting behaviors or movements over time to detect and predict the onset of diseases and other disabilities.

Abstract: The invention is directed to a promoter, designated MuB, sequences which hybridize to same and functional fragments thereof. The regulatory element of the invention provide improved expression in plants of operably linked nucleotide sequences. Expression vectors with the regulatory element is the subject of the invention, which may further include an operably linked nucleotide sequence. The invention is further directed to transformed plant tissue including the nucleotide sequence and to transformed plants and seeds thereof. The regulatory element is useful for driving gene or antisense expression or the like for the purpose of imparting agronomically useful traits such as, but not limited to, increase in yield, disease resistance, insect resistance, herbicide tolerance, drought tolerance and salt tolerance in plants.