Researchers Submit Patent Application, “Device, System And Method For Patient Monitoring To Predict And Prevent Bed Falls”, for Approval (USPTO 20190214146)

2019 JUL 25 (NewsRx) -- By a News Reporter-Staff News Editor at Hospital & Nursing Home Daily -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors DUNIAS, Paraskevas (Eindhoven, NL); WEFFERS-ALBU, Mirela Alina (Boukoul, NL), filed on August 23, 2017, was made available online on July 11, 2019.

The patent’s assignee is Koninklijke Philips N.V. (Eindhoven, Netherlands).

News editors obtained the following quote from the background information supplied by the inventors: “Falls are the most common adverse event reported in hospitals and are a leading cause of hospital-acquired injury, and frequently prolong or complicate hospital stays. Reviews of observational studies in acute care hospitals show that fall rates range from 1.3 to 8.9 falls/1,000 patient days and that higher rates occur in units that focus on eldercare, neurology and rehabilitation. In spite of extensive research on falls risk factors and the development of a number of falls risk instruments, protocols are applied inconsistently, and risk factor directed interventions are far from standardized.

“Although falls can occur at all ages, they are known to lead to significant injury in older people or those from high falls risk populations when compounded by an acute health problem requiring hospitalization, or for those requiring admission to residential care settings.

“In addition, other observational studies show that 60-70% of all falls in hospital occur from the bed or bedside chair, that more than 80% of falls are unwitnessed and that about 50% occur in patients who fall repeatedly.

“Given the high impact of fall incidents on patients’ health and quality of life as well as costs of care, finding scalable and cost efficient solutions for patient monitoring and fall prevention to reduce the number of bed falls incidents becomes of utmost importance.

“The current state of art includes solutions such as sitter services as means for bed fall incidents prevention, bed rails as means for bed fall incidents prevention and certain automatic solutions for patient monitoring.

“Sitter service is difficult to implement, not scalable and not cost efficient. Towards its implementation hospitals typically must choose between employing sitters from outside the hospital staff which is financially taxing due to the service high costs or assigning sitter duties to their own hospital staff, which increases significantly the responsibilities and workload of the staff, typically already overburdened due to staff shortages. In addition assigning sitter tasks (that do not require medical training) to qualified staff prevents appropriate use of personnel qualifications and skills.

“In that sense sitter services do not present a favorable outlook when it comes to implementing a reliable, scalable and cost efficient prevention strategy for inpatient bed fall incidents.

“Bed rails as a single prevention strategy do not seem to guarantee the prevention of bed fall incidents. 50-90% of falls from bed in hospital occur despite bedrails being applied, showing limited success in preventing falls in general. In addition bedrail use may also be associated with worsening of agitation, fear and delirium. ‘Chemical’ restraint e.g. in the form of neuroleptic use despite the misguided intention to prevent falls by its use is associated with increased fall rates. Moreover, restraint or bed rail use can lead to muscle wasting, infection or pressure sores from immobility, and deconditioning. Finally there is an ethical component to be considered when it comes to restricting patient moving ability.

“Technology based products have been used in an attempt replace sitter service but may also have innate drawbacks. Some systems do not provide any intelligence to support the so-called ‘eSitter’ in monitoring patients, which limits the number of patients monitored in parallel. As the scalability of such solutions is limited, up-scaling requires additional devices and remote monitoring stations. No intelligence implemented to determine automatically the fall risk in real time raises concerns and puts in question the feasibility of fall incidents prevention. Fall risk assessed is based on input obtained upon initial and ongoing patient contact by hospital staff but is not updated based on continuous automatic observation of patient in real time. As updates of the fall risk are based on interviews with the patient at admission and during hospitalization, these updates are liable to inaccuracy and significant delays, raising further concerns.

“Other systems implement a technical approach where whenever a partial bed edge crossing happens (by the blanket or by patient arm) without an actual patient bed-exit, the system triggers an alarm. This leads to a high rate of false alarms.

“In addition regarding effective preventive interventions, these technologies focus very much on monitoring and alarms but no attention is given to understanding the most optimal intervention that should be provided given a particular patient profile at a certain time. Furthermore, if medical staff should intervene, these systems do not provide support in determining the actual persons who should be notified, in order to optimize staff resources. Finally all alerts are issued only when the risk of falling is very high and the incident occurrence is imminent, at which point the chance of providing an effective intervention that actually prevents the fall is decreased.

“US 2008/0122926 A1 refers to a system and a method for process segmentation using motion detection. Video recording technology is utilized to enable business process investigation in an unobtrusive manner. Several cameras are situated, each having a defined field of view. For each camera, a region of interest (ROI) within the field of view is defined, and a background image is determined for each ROI. Motion within the ROI is detected by comparing each frame to the background image. The video recording can then be segmented and indexed according to the motion detection.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “It is an object of the present invention to provide a device, a system and a method for monitoring of patients which not only monitors the movement patterns and the vital signs of the patient but also assesses the data and evaluates a risk score which on the one hand allows reliable prediction and on the other hand minimizes false alarms, thus allowing efficient but attentive care for the patients.

“In a first aspect of the present invention a device for detection of a video data related risk score for a bed fall risk of an individual is presented that comprises a first port for obtaining video data related to movement of the individual and a video data processing unit for obtaining and processing the video data to generate a video data related risk score indicating the bed fall risk of the individual by detecting at least one risk factor from the video data and computing the video data risk score from the at least one risk factor.

“In a further aspect of the present invention a system for determination of a bed fall risk of an individual is presented that comprises at least one video sensor, in particular a camera, for acquiring video data related to movement of the individual and a device for determination of a bed fall risk of an individual based on the acquired video data.

“In another aspect of the invention a method for determination of a bed fall risk of an individual is presented, the method comprising the steps of obtaining video data related to movement of an individual and processing the video data to generate a movement related risk score indicating the bed fall risk of the individual by detecting at least one risk factor from the video data and computing the video risk score from the at least one risk factor.

“In yet a further aspect of the invention a computer program comprising program code means for causing a computer to carry out the steps of the method when said computer program is carried out on a computer is presented.

“The inventive device, system, method and computer program for determination of a bed fall risk differ to the state of art described above in that the data obtained by the video data processing unit are used to determine a bed fall risk on base of an analysis of the movement data if the individual with respect to the environment. The risk score determined from the data is used to predict the risk of a bed fall on a precise and reliable scale and thus helps to save staff costs by streamlining the process and simultaneously assuring high level care to the individual.

“Preferred embodiments of the invention are defined in the dependent claims. It shall be understood that the claimed method has similar and/or identical preferred embodiments as the claimed device and as defined in the dependent claims.

“Preferably the risk factor is at least one of a movement from a supine to an erect position of the individual, a movement assigned to restlessness of the individual, a movement in direction to and/or in vicinity of an edge of the bed of the individual. Especially movements which are detected to take place in vicinity of or in direction to a bed edge are crucial for determination of an imminent bed fall. Thus, close monitoring of the confinements of the bed are highest priority for prevention of a bed fall. This can be achieved by observing the position of the individual with respect to the edges and changes of the position.

“According to an advantageous embodiment the video data processing unit is further configured to detect discrete conditions from the video data, in particular the undisturbed presence of the individual in the bed, an intentional exit of the individual from the bed and an unintentional fall of the individual from the bed. This also helps streamlining the monitoring process since e.g. an individual fit enough to leave the bed on purpose does not have to be attended. Likewise, a sedated individual lying still does not have to be monitored continuously. Thus, resources of any kind can be used in a very effective manner.

“The device advantageously further comprises an evaluation unit configured to assign a reliability value to said video data and/or said video data related risk score and to evaluate a variable risk score of the individual from the reliability value and the video data risk score. By way of this, an assessment of the reliability of the signal is possible. If the signal is not reliable for a bed fall prediction, the system can give out an alert and use other sources of information.

“Preferably, the video data comprise data related to the position of a bed the individual is located in, of bed edges confining the bed, of first and second regions of interest defined with respect to the bed, and data related to the position of the individual in relation to the position of the bed, the bed edges and the first and second regions of interest. This helps defining the space to be monitored to keep the rate of false alarms low. a false alarm may be e.g. raised if another person is present in the room and moving, who could be mistaken for the individual under care being helpless out of bed. A clear definition of the position of the bed and of the adjacent regions of interest helps to locate the individual and take the position and changes of the position into account when calculating the related risk score.

“The first and second regions of interest advantageously have a rectangular shape with a low base stretching horizontally across the bed at approximately half of the length of the bed and have a length approximately equal to the length of the bed, wherein the low base of the first region of interest has a length approximately equal to the width of the bed and wherein the low base of the second region of interest has a length approximately equal to the double width of the bed, and wherein the second region of interest consists of two portions being adjacent to the first region of interest. The restriction of the regions of interest to the upper part of the bed and the upper part of the individual’s body respectively is easily understood by the fact that the individual’s head is the part which needs most attention. It is far more likely that a bed fall with injuries will occur when the upper part of the body and the head are shifting their position towards the bed edge.

“According to an alternative embodiment, the data related to the position of the individual comprise a highest center of motion gravity, a right most center of motion gravity, a left most center of motion gravity and a global center of motion gravity. The restriction of the body of the individual to e few points marking the centers of motion gravity makes monitoring far more easy than observing movements of body parts.

“Preferably, the video data processing unit is configured to detect the movement of the centers of motion gravity by determination of valid motion trajectories defined by length and maximum variance and clustering of the trajectories to identify moving entities based on direction, slope, position and length over a predetermined time, and to assign a risk factor and/or a discrete condition to the movements. By way of this, the individual as a whole can be monitored with high reliability without error sources defined by single limbs and their respective movements.

“According to a preferred embodiment of the invention, the video data processing unit is configured to assign visual output indicators to the risk factors and the discrete conditions and to output the indicators for display. Visual indicators are far easier and more intuitive to monitor than values represented by numbers.

“Advantageously, the visual output indicators are configured to change continually or in discrete steps from green to red depending on the detection of risk factors and/or discrete conditions. Thus, not only extremal values related to complete motionlessness or a sudden bed fall can be illustrated, but also intermediate values which put the system into a status of higher attention. Medical staff checking the indicators have a quick overlook about the status without consulting charts or tables.

“Preferably, the video data processing unit is configured to determine the video data risk score and/or the reliability value at discrete intervals or continually. The mode can be chosen in dependency of the behavior of the individual. If e.g. sedation has been administered and the person under care is quite still in position, a longer interval for the measurement of the vital signs can be chosen. This produces less data. If the individual is moving a lot, the determination of the vital signs and the related risk score and reliability values can be shifted to a continuous mode to make sure to keep track of the values.

“According to an advantageous embodiment, a vital signs sensor and a vital signs processing unit are provided, wherein the vital signs processing unit is configured to generate a vital signs related risk score from vital signs data obtained from the vital signs sensor, and wherein the evaluation unit is configured to evaluate the variable risk score from the video data risk score and the vital signs risk score. By way of combining video and vital signs data, the system works on redundancy and thus provides a high level of reliability. A sensor not being reliable can be detected, the other sensor can take over and the system part not being reliable can either reboot or alert technical staff for maintenance. Thus, monitoring without interruption is possible.”

The claims supplied by the inventors are:

“1. Device for detection of a video data related risk score for a bed fall risk of an individual, the device comprising: a first port for obtaining video data related to movement of the individual, and a video data processing unit for obtaining and processing the video data to generate a video data related risk score indicating the bed fall risk of the individual by detecting at least one risk factor from the video data and computing the video data risk score from the at least one risk factor.

“2. Device according to claim 1, wherein the risk factor is at least one of a movement from a supine to an erect position of the individual, a movement assigned to restlessness of the individual, a movement in direction to and/or in vicinity of an edge of the bed_of the individual.

“3. Device according to claim 1, wherein the video data processing unit is further configured to detect discrete conditions from the video data, in particular the undisturbed presence of the individual in the bed, an intentional exit of the individual from the bed and an unintentional fall of the individual from the bed.

“4. Device according to claim 1, further comprising an evaluation unit configured to assign a reliability value to said video data and/or said video data related risk score_and to evaluate a variable risk score of the individual from the reliability value and the video data risk score.

“5. Device according to claim 1, wherein the video data comprise data related to the position of a bed the individual is located in, of bed edges confining the bed, of first and second regions of interest defined with respect to the bed, and data related to the position of the individual in relation to the position of the bed, the bed edges and the first and second regions of interest.

“6. Device according to claim 5, wherein the first and second regions of interest have a rectangular shape with a low base stretching horizontally across the bed at approximately half of the length of the bed and have a length approximately equal to the length of the bed, wherein the low base of the first region of interest has a length approximately equal to the width of the bed_and wherein the low base of the second region of interest has a length approximately equal to the double width of the bed, and wherein the second region of interest consists of two portions being adjacent to the first region of interest.

“7. Device according to claim 5, wherein the data related to the position of the individual comprise a highest center of motion gravity, a right most center of motion gravity, a left most center of motion gravity_and a global center of motion gravity.

“8. Device according to claim 7, wherein the video data processing unit is configured to detect the movement of the centers of motion gravity_by determination of valid motion trajectories defined by length and maximum variance and clustering of the trajectories to identify moving entities based on direction, slope, position and length over a predetermined time, and to assign a risk factor and/or a discrete condition to the movements.

“9. Device according to claim 2, wherein the video data processing unit_is configured to assign visual output indicators to the risk factors and the discrete conditions and to output the indicators for display.

“10. Device_according to claim 9, wherein the visual output indicators are configured to change continually or in discrete steps from green to red depending on the detection of risk factors and/or discrete conditions.

“11. Device according to claim 4, wherein the video data processing unit is configured to determine the video data risk score_and/or the reliability value at discrete intervals or continually.

“12. System for determination of a bed fall risk of an individual, the system comprising: at least one video sensor, in particular a camera, for acquiring video data related to movement of the individual, and a device according to claim 1 for determination of a bed fall risk of an individual based on the acquired video data.

“13. System according to claim 12, further comprising a vital signs sensor and a vital signs processing unit, wherein the vital signs processing unit is configured to generate a vital signs related risk score from vital signs data obtained from the vital signs sensor, and wherein the evaluation unit is configured to evaluate the variable risk score from the video data risk score and the vital signs risk score.

“14. Method for determination of a bed fall risk of an individual, the method comprising the steps of: obtaining video data related to movement of an individual, and processing the video data to generate a movement related risk score indicating the bed fall risk of the individual by detecting at least one risk factor from the video data and computing the video risk score from the at least one risk factor.

“15. Computer program comprising program code means for causing a computer to carry out the steps of the method as claimed in claim 14 when said computer program is carried out on a computer.”

For additional information on this patent application, see: DUNIAS, Paraskevas; WEFFERS-ALBU, Mirela Alina. Device, System And Method For Patient Monitoring To Predict And Prevent Bed Falls. Filed August 23, 2017 and posted July 11, 2019. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220190214146%22.PGNR.&OS=DN/20190214146&RS=DN/20190214146

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