A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.

A patient support apparatus, such as a bed, cot, stretcher, etc., for supporting a patient includes an exit detection system with multiple user-selectable modes of operation that each have different sensitivity levels for triggering an exit alert. The exit detection system also includes one or more non-user selectable modes of operation that are automatically implemented in response to a triggering action. For example, a transition mode may be automatically implemented when the user attempts to switch from a first user-selectable mode to a different user selectable mode, or a motion mode may be automatically implemented when movement of one or more components of the patient support apparatus occurs. In the transition mode, the exit detection system may use a least restrictive sensitivity level. In the motion mode, the exit detection system may inhibit exit alerts and/or change the criteria for issuing the exit alert.

A system for monitoring animal vitals can include a housing, a controller and one or more sensors for sensing vital signs. The housing can be configured to couple to one or more locations on an animal. A system can be adapted for monitoring two or more vital signs simultaneously and for conveying vital sign information to a user. A system can be adapted for alerting a user to the presence or absence of one or more conditions. A system can include a plurality of sensor units and can be adapted for monitoring a plurality of patients simultaneously.

A method described and device to rapidly detect the accident-related removal of a mobile user terminal from a holder and to generate corresponding information which characterizes the fall or crash related to the accident. Rotation-rate sensor variables and acceleration variables of the mobile user terminal are acquired. A rotation or twisting of the mobile user terminal is detected in that the currently acquired and/or integrated rotation-rate sensor variables are compared to past values and/or threshold values. A movement of the mobile user terminal is detected on the basis of the currently acquired and/or integrated acceleration variables. A comparison with past values and/or threshold values may optionally also be carried out. The determination of an in particular accident-related crash of the vehicle and/or the fall of the mobile user terminal out of the holder then takes place due to the detected rotation and the detected movement.

The present invention relates to a control device for controlling an operation of an aerosol nebulizer system (30), said aerosol nebulizer system (30) comprising an aerosol generator (31) for nebulizing a liquid and a plurality of sensor units (38a, 38b, 38c, 38d, 38e), said control device (20) comprising: a communication unit (21), configured to establish a wireless communication connection and to perform data transmission with the aerosol nebulizer system (30), a sensor data control unit (22), configured to trigger a selection of sensor data related to the sensor units (38a, 38b, 38c, 38d, 38e) to be wirelessly transmitted to the control device (20).

A head guard is provided. The head guard includes one or more sensors as part of an sensory input and communications system. The head guard wirelessly communicates data to remote computing devices for intelligent data collection.

A user, such as an elderly person, may be assisted by an assistance device in his or her caregiving environment that operates in conjunction with one or more server computers. The assistance device may execute a schedule of assistance actions where each assistance action is associated with a time and is executed at that time to assist the user. An assistance action may present an input request to a user, process a voice input of the user, and analyze the voice input to determine that the voice input corresponds to a negative response event, a positive response event, or a non-response event. Based on the categorization of one or more voice inputs as negative response events, positive response events, or non-response events, it may be determined to notify a caregiver of the user, for example where the user has not responded to a number of assistance actions.

The present invention relates to a computer-implemented system and method for preventing users from bending their necks too far forward or past thirty degrees away from vertical during viewing of a handheld electronic device. The system uses a computer-implemented application installed in the device that determines flexion of the user's neck and compares with a threshold to determine a ‘tech neck’ condition. The application generates an alert displayed on the screen of the device to indicate the ‘tech neck’ condition and further, can pause or hang the display when the ‘tech neck’ condition is continued to be determined. The system and method assist in preventing injuries, strains and disabilities to both children and adults that use electronic devices for extended periods of time.

According to the present disclosure, a method of monitoring a patient on an operating room table is provided. The method comprises receiving image data captured by at least one image capture device; and determining a position of the patient relative to the operating table in dependence on the image data. A corresponding system, computer program and non-transitory memory are also provided.

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.