A patient request system includes a tablet computer that configured for entry of patient requests. The tablet computer is configured to display a first menu corresponding to basic request categories for a patient. The tablet computer is configured to display a second menu that corresponds to specific patient requests falling under the basic request category selected by the patient using the first menu. A server is configured to receive a specific patient request made by the patient. A notification device is configured to display the specific patient request to a caregiver. A patient request method is also disclosed.

Embodiments of this disclosure are directed to an in-bed device having one or more substrates, a wireless transceiver, an antenna coupled to the wireless transceiver, and a control system. The one or more substrates are shaped to be positioned on a bed. The wireless transceiver is disposed on at least one of the one or more substrates, and configured to transmit and receive wireless pulses via the antenna. The control system is configured to cause the wireless transceiver to generate the wireless pulses and analyze the received wireless pulses to determine whether the bed is occupied by a user.

Methods, devices, and systems for contactless cough detection and attribution are presented herein. Audio data may be received using a microphone. A cough may be identified as having occurred based on the received audio data. Radar data may be received indicative of reflected radio waves from a radar sensor. A state analysis process may be performed using the received radar data. The detected cough may be attributed to a particular user based at least in part on the state analysis process performed using the radar data.

A body motion determination system (100) configured to determine whether or not a subject (S) on a bed (BD) has a body motion includes: a plurality of load detectors (11, 12, 13, 14) each configured to detect the load of the subject on the bed; a respiratory waveform obtaining unit (32) configured to obtain a respiratory waveform of the subject based on a temporal variation of the load of the subject detected by each of the plurality of load detectors; and a body motion determining unit (33) configured to determine whether or not the subject has the body motion based on a comparison between a first threshold value and a standard deviation of the temporal variations in the load of the subject detected by at least one of the plurality of load detectors. The body motion determining unit is configured to compensate the standard deviation to be used in the comparison by an amplitude of the respiratory waveform.

This invention relates to a prediction support system and associated methods for supporting prediction of the severity of a disease. The systems and methods may perform operations that include continuously detecting whether a patient with the disease is in bed, and acquiring, based on the detection result of the detecting, an in-bed pattern indicating, as a time series, whether the patient is in bed. The systems may include a detection device and an acquisition unit.

A patient support apparatus includes a frame, support surface, first and second wireless transceivers, a room control, and a controller. The first transceiver communicates with a headwall module and the second transceiver communicates with a local area network. The room control controls a feature of a room device. The controller transmits a room control signal to the headwall module via the first transceiver in response to a user activating the room control on the patient support apparatus, and receives a room control command from an off-board source via the second wireless transceiver. The controller also transmits the room control signal to the headwall module via the first transceiver in response to receiving the room control command. The headwall module forwards the room control signal to the room device, thereby allowing the room device to be controlled both by the room control and by the off-board source.

The present invention provides systems, methods, and articles for stress reduction and sleep promotion. A stress reduction and sleep promotion system includes at least one remote device, at least one body sensor, and at least one remote server. In other embodiments, the stress reduction and sleep promotion system includes machine learning.

A sensor strap system according to an embodiment of the present disclosure includes: a sensor capable of detecting physical or physiological activities or conditions of a target subject; a top strap extending along a top surface of a bed, the top strap configured to secure the sensor to the top surface of the bed; and a circumferential strap extending about a circumference of the bed and coupled with the top strap.

A patient support apparatus system and patient support apparatus, such as a bed, cot, stretcher, operating table, recliner, or the like, include a litter frame, a support deck, a sensor configured to detect caregiver activity, a transceiver communicating with a server, and a controller. The controller is configured to send alerts or messages to the server when caregiver assignment errors or caregiver inattention issues are identified. An alert is sent when a caregiver has not attended to a patient within a certain period of time, or when a caregiver has not been assigned to a particular patient or a particular location within a healthcare facility to which a patient has been assigned. The patient support apparatus and system are in communication with other healthcare facility systems and devices that gather and share information and data to alert caregivers in an effort to avoid patient neglect.

An evaluating apparatus of this application includes a detector configured to detect a biological signal of a user, and a controller configured to calculate an amount of activity of the user from the biological signal, determine whether the user is sleeping during a first period by comparing the amount of activity with a low threshold, and determine whether the user is sleeping during a second period after the first period by comparing the amount of activity with a high threshold, the low threshold being lower than the high threshold.