Ms-Technologies Gmbh

Shuchuan Jack Cheng

Yuan-Ming Fleming Lure

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.

System and method for patient movement detection and fall monitoring

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. Decision fusion is used to integrate classification results from feature-based classifier and deep learning AI to reach optimal decision.

System and method for Alzheimer's disease risk quantification utilizing interferometric micro-doppler radar and artificial intelligence

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.

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Potomac, MD, United States of America

Shuchuan Jack Cheng