Systems and methods for a fall prevention system
A fall prevention system provides intervention and distraction to attempt to prevent a “fall risk” patient from falling from bed while providing alerts to caregivers. The system provides verbal, visual and tactile interventions to create a distraction and time for a nurse or caregiver to intervene and prevent a fall.
1 . A system, comprising:
a processor in communication with a memory and the intervention element, the memory including instructions executable by the processor to:
access temporally adjacent sensor observations obtained from a sensor array in communication with the processor, the sensor array being positioned within the space designated for the patient;
determine, using time-dependent analysis of the temporally adjacent sensor observations, that a position-change pattern of a patient is indicative of the patient attempting to stand; and
apply, based on the position-change pattern, a control signal to the intervention element to initiate a patient-facing interactive engagement performed locally by the intervention element that: (i) establishes a two-way exchange with the patient via audio and/or visual modalities, (ii) elicits and receives assistance-need information from the patient via spoken or non-verbal inputs, and (iii) provides, via the intervention element, an audible, visual, or tactile acknowledgment that assistance has been notified.
2 . The system of claim 1 , the memory further including instructions executable by the processor to:
generate, based on the temporally adjacent sensor observations, an alert signal for communication to an alert device associated with a supervising individual.
3 . The system of claim 2 , the alert device including one or more haptic display elements, one or more audio display elements, and/or one or more visual display elements.
4 . The system of claim 1 , the memory further including instructions executable by the processor to:
capture the assistance-need information received from the patient; and
communicate the assistance-need information to a supervising individual through an alert device.
5 . The system of claim 1 , the sensor array including one or more pressure sensors.
6 . The system of claim 5 , the memory further including instructions executable by the processor to:
apply the temporally adjacent sensor observations as input to a machine learning model, the machine learning model being trained to detect the position-change pattern based on pressure distribution measurable across the one or more pressure sensors.
7 . The system of claim 1 , the sensor array including one or more video capture devices.
8 . The system of claim 7 , the memory further including instructions executable by the processor to:
apply the temporally adjacent sensor observations as input to a machine learning model, the machine learning model being trained to detect the position-change pattern based on a pose of the patient as captured by the one or more video capture devices.
9 . The system of claim 1 , the intervention element including a robot.
10 . The system of claim 9 , the control signal being applied to the robot to cause the robot to move from a first location to a second location, the second location being associated with the patient.
11 . The system of claim 1 , the intervention element including a holographic projector, and the control signal being applied to the holographic projector to display a holographic image.
12 . The system of claim 1 , the intervention element including an audio output device and the control signal being applied to the audio output device to initiate playback of an audio message.
13 . The system of claim 1 , the intervention element including a light emitting device that alters a lighting condition of the space responsive to the control signal.
14 . The system of claim 1 , the intervention element including a motorized element positionable within the space responsive to the control signal.
15 . The system of claim 1 , the intervention element including an interactive display device that establishes communication between the patient and a communication device associated with a supervising individual based on the control signal.
16 . The system of claim 1 , wherein the patient-facing interactive engagement is executed at the intervention element by presenting a prompt via the intervention element, receiving patient inputs via the intervention element, and interpreting the patient inputs using at least one of: (i) natural-language processing applied to speech and/or text inputs; (ii) processing of non-verbal inputs provided via a user interface of the intervention element.
17 . A method, comprising:
accessing temporally adjacent sensor observations obtained from a sensor array in communication with a processor, the sensor array being positioned within a space designated for a patient;
determining, using time-dependent analysis of the temporally adjacent sensor observations, that a position-change pattern of the patient is indicative of the patient attempting to stand; and
applying, based on the position-change pattern, a control signal to an intervention element to initiate a patient-facing interactive engagement performed locally by the intervention element that: (i) establishes a two-way exchange with the patient via audio and/or visual modalities, (ii) elicits and receives assistance-need information from the patient via spoken or non-verbal inputs, and (iii) provides, via the intervention element, an audible, visual, or tactile acknowledgment that assistance has been notified.
18 . The method of claim 17 , further comprising:
generating, based on the temporally adjacent sensor observations, an alert signal for communication to an alert device associated with a supervising individual.
19 . The method of claim 17 , further comprising:
applying the temporally adjacent sensor observations as input to a machine learning model, the machine learning model being trained to detect the position-change pattern based on pressure distribution measurable across one or more pressure sensors of the sensor array.
20 . The method of claim 17 , further comprising:
applying the temporally adjacent sensor observations as input to a machine learning model, the machine learning model being trained to detect the position-change pattern based on a pose of the patient as captured by one or more video capture devices of the sensor array.
21 . The method of claim 17 , further comprising:
recording the assistance-need information received from the patient; and
communicating the assistance-need information to a supervising individual through an alert device.
22 . The method of claim 17 , further comprising executing the patient-facing interactive engagement at the intervention element by:
presenting a prompt via the intervention element,
receiving patient inputs via the intervention element, and
interpreting the patient inputs using at least one of: (i) natural-language processing applied to speech and/or text inputs; (ii) processing of non-verbal inputs provided via a user interface of the intervention element.