Systems and methods for monitoring a positioning and vital signs of a subject are disclosed. A method includes receiving, by a processing device of a monitoring device, LWIR image data from a first imaging component of the monitoring device and NIR image data from a second imaging component of the monitoring device, determining one or more boundaries of the patient support apparatus from the NIR image data, constructing one or more virtual boundaries that correspond to the boundaries of the patient support apparatus, determining a location of the subject with respect to the virtual boundaries from at least one of the LWIR image data and the NIR image data, determining a facial temperature and a heart rate of the subject from the LWIR image data, and determining a respiration rate of the subject from at least one of the LWIR image data and the NIR image data.

A wearable device including a motion-tracking sensor can be used for tracking sleep. The data from the motion-tracking sensor can be to estimate/classify the sleep state for multiple periods and/or to determine sleep intervals. In some examples, to improve performance, sleep state classification can be performed on data within a sleep tracking session. The start of the sleep tracking session can be defined by detecting a rest state and the end of the sleep tracking session can be defined by an activity state. In some examples, to improve performance, the classified sleep states for the multiple periods can be filtered and/or smoothed. In some examples, a signal quality check can be performed for the data from the motion-tracking sensor. In some examples, the classification of the sleep states and/or the display of the results of sleep tracking can be subject to passing one or more signal quality checks.

A wearable device including a motion-tracking sensor can be used for tracking sleep. The data from the motion-tracking sensor can be to estimate/classify the sleep state for multiple periods and/or to determine sleep intervals. In some examples, to improve performance, sleep state classification can be performed on data within a sleep tracking session. The start of the sleep tracking session can be defined by detecting a rest state and the end of the sleep tracking session can be defined by an activity state. In some examples, to improve performance, the classified sleep states for the multiple periods can be filtered and/or smoothed. In some examples, a signal quality check can be performed for the data from the motion-tracking sensor. In some examples, the classification of the sleep states and/or the display of the results of sleep tracking can be subject to passing one or more signal quality checks.

In some examples, a non-transitory computer-readable medium stores executable code, which, when executed by a processor, causes the processor to receive a video of at least part of a human torso, use a neural network to produce multiple vector fields based on the video, the multiple vector fields representing movement of the human torso, and determine a respiration rate of the human torso using the multiple vector fields.

In some examples, a non-transitory computer-readable medium stores executable code, which, when executed by a processor, causes the processor to receive a video of at least part of a human torso, use a neural network to produce multiple vector fields based on the video, the multiple vector fields representing movement of the human torso, and determine a respiration rate of the human torso using the multiple vector fields.

A method of monitoring respiration with an acoustic measurement device, the acoustic measurement device having a sound transducer, the sound transducer configured to measure sound associated with airflow through a mammalian trachea, the method includes correlating the measured sound into a measurement of tidal volume and generating at least one from the group consisting of an alert and an alarm if the measured tidal volume falls outside of a predetermined range.

A patient monitoring system to help manage a patient that is at risk of falling is disclosed. The system includes a patient-worn wireless sensor that senses the patient's motion and wirelessly transmits information indicative of the sensed motion to a patient monitor. The patient monitor receives, stores, and processes the transmitted information to determine whether the patient has fallen or is about to fall. Upon such detection, the system can notify the patient's caretakers that the patient has fallen or is about to fall and therefore, is in need of immediate attention.

A patient monitoring system to help manage a patient that is at risk of falling is disclosed. The system includes a patient-worn wireless sensor that senses the patient's motion and wirelessly transmits information indicative of the sensed motion to a patient monitor. The patient monitor receives, stores, and processes the transmitted information to determine whether the patient has fallen or is about to fall. Upon such detection, the system can notify the patient's caretakers that the patient has fallen or is about to fall and therefore, is in need of immediate attention.

The invention relates to a system for detecting biosignals, comprising a sensor unit and a patch that can be attached to the body and has electrodes and conductor tracks. The sensor unit and the patch can be connected to one another via a connector arranged on the patch in such a way that an electrical connection is produced and the sensor unit is held on the body via the patch. The sensor unit has a plurality of contact elements for electrically contacting the connector and/or the patch. According to the invention, at least two contact elements of the sensor element are conductively connected to one another via a conductor track of the patch by connecting the patch to the sensor unit.

A system for monitoring medical conditions including pressure ulcers, pressure-induced ischemia and related medical conditions comprises at least one sensor adapted to detect one or more patient characteristic including at least position, orientation, temperature, acceleration, moisture, resistance, stress, heart rate, respiration rate, and blood oxygenation, a host for processing the data received from the sensors together with historical patient data to develop an assessment of patient condition and suggested course of treatment, including either suspending or adjusting turn schedule based on various types of patient movement. The sensor can include bi-axial or tri-axial accelerometers, as well as resistive, inductive, capacitive, magnetic and other sensing devices, depending on whether the sensor is located on the patient or the support surface, and for what purpose.