Systems and methods for patient fall detection

• US 10,448,844 B2
• Filed: 08/31/2016
• Issued: 10/22/2019
• Est. Priority Date: 08/31/2015
• Status: Active Grant

• Current Assignee: Masimo Corporation
• Original Assignee: Masimo Corporation
• Inventors: Al-Ali, Ammar, Raths, Cornelius, Usman, Mohammad
• Primary Examiner(s): Cerioni, Daniel L
• Assistant Examiner(s): Warsi, Yasmeen S
• Application Number:
  US15/253,355
• Publication Number:
  US 20170055882A1
• Time in Patent Office: 1,147 Days
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0223 Magnetic field sensors
• CPC Class Codes: A61B 2562/0271 Thermal or temperature sensors
• CPC Class Codes: A61B 2562/029 Humidity sensors
• CPC Class Codes: A61B 2562/166 the sensor is mounted on a specially adapted printed circuit board
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0008 Temperature signals
• CPC Class Codes: A61B 5/0015 characterised by features of the telemetry system
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/004 adapted for image acquisition of a particular organ or body part A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1113 : Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1116 : Determining posture transitions
• CPC Class Codes: A61B 5/1117 : Fall detection
• CPC Class Codes: A61B 5/1118 : Determining activity level
• CPC Class Codes: A61B 5/1121 : Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1126 : using a particular sensing technique
• CPC Class Codes: A61B 5/113 : occurring during breathing
• CPC Class Codes: A61B 5/14551 : for measuring blood gases
• CPC Class Codes: A61B 5/14552 : Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/25 : Bioelectric electrodes therefor
• CPC Class Codes: A61B 5/318 : Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/447 : specially adapted for aiding the prevention of ulcer or pressure sore development, i.e. before the ulcer or sore has developed
• CPC Class Codes: A61B 5/6801 : specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6823 : Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6833 : Adhesive patches
• CPC Class Codes: A61B 5/68335 : including release sheets or liners
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61G 2203/32 : for force
• CPC Class Codes: A61G 2203/36 : for motion
• CPC Class Codes: A61G 2203/42 : for inclination
• CPC Class Codes: A61G 2203/46 : for temperature
• CPC Class Codes: A61G 7/057 : Arrangements for preventing bed-sores or for supporting patients with burns, e.g. mattresses specially adapted therefor ventilation openings in mattresses A47C21/042; pneumatic or hydraulic mattresses A47C27/08
• CPC Class Codes: G01K 1/16 : Special arrangements for conducting heat from the object to the sensitive element
• CPC Class Codes: G01K 13/20 : Clinical contact thermometers for use with humans or animals
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

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.

Claims

1. A method of detecting whether a monitored patient has fallen by processing signals indicative of movement by the patient, the method comprising:

receiving, with a processing device from a wireless sensor, signals responsive to a linear acceleration of said patient with respect to a roll axis, a pitch axis, and a yaw axis, said roll axis corresponding to a longitudinal axis extending along a length of said patient'"'"'s body, said pitch axis corresponding to said patient'"'"'s hips, and said yaw axis corresponding to a horizontal plane in which said patient is located, wherein said wireless sensor is configured to be worn by said patient and comprises an accelerometer, a gyroscope, and a first wireless transceiver, and wherein said processing device comprises a processor, a memory device, a storage device, a display, and a second wireless transceiver configured to communicate with the first wireless transceiver;

receiving, with said processing device from said wireless sensor, signals responsive to an angular velocity of said patient with respect to said roll axis, said pitch axis, and said yaw axis; and

processing said received signals responsive to said linear acceleration and said angular velocity of said patient with said processor of said processing device, including electronically;

forming a sensor data vector comprising a plurality of data elements, each of the plurality of data elements responsive to one of said linear acceleration or said angular velocity with respect to one of said roll axis, said pitch axis, and said yaw axis;

normalizing said plurality of data elements to form a normalized sensor data vector;

generating a feature vector from said normalized sensor data vector, the feature vector comprising a plurality of features indicative of a patient fall, wherein each of the plurality of features are generated based on one or more of the data elements from said normalized sensor data vector;

deriving an activation value based on an application of weight vector to said feature vector, wherein said weight vector comprises a plurality of weights, each of the plurality of weights corresponding to one of the plurality of features of said feature vector;

determining whether a patient fall has occurred based on the derived activation value; and

alerting a caregiver based on said determination, said alert indicating that said patient fall has occurred.

2. The method of claim 1, wherein said normalizing said plurality of data elements comprises normalizing each of said plurality of data elements to have zero-mean and unit-variance.

3. The method of claim 1, wherein said plurality of features comprise:

an acceleration magnitude;

an angular velocity magnitude;

a jerk magnitude;

a fall duration;

a pitch change; and

vertical velocities.

4. The method of claim 1, wherein said application of said weight vector to said feature vector comprises computing an inner product of said feature vector with said weight vector.

5. The method of claim 1, wherein said application of said weight vector to said feature vector comprises:

presenting, to a supervised learning algorithm, training data that include example inputs and known outputs; and

mapping, by said supervised learning algorithm, said example inputs to said known outputs to derive said weight vector.

6. The method of claim 5, wherein said mapping said example inputs to said known outputs to derive said weight vector is performed by Fishers'"'"' linear discriminant.

7. The method of claim 1, wherein said determining whether a patient fall has occurred comprises identifying a sign attribute of said derived activation value, wherein a positive sign attribute of said derived activation value indicates that said patient fall has occurred.

8. The method of claim 1, wherein one of said plurality of features comprises a fall duration, said fall duration determined by evaluating an acceleration profile of said patient over a first time period.

9. The method of claim 8, further comprising determining a starting point of the patient fall by comparing a first magnitude of acceleration during the acceleration profile to a first threshold.

10. The method of claim 9, wherein the first threshold is 0.6 g.

11. The method of claim 9, further comprising determining an impact point of the patient fall by comparing a second magnitude of acceleration during the acceleration profile to a second threshold.

12. The method of claim 11, wherein the second threshold is 0.8 g.

13. The method of claim 1, wherein said plurality of features comprises a pitch change, said pitch change determined by comparing a first pitch orientation of the patient with a second pitch orientation of the patient, said first pitch orientation occurring prior to said second pitch orientation.

14. A method of detecting a patient fall, the method comprising:

receiving, with a processing device, a first set of signals responsive to an acceleration of a patient from a wireless sensor, wherein said wireless sensor is configured to be worn by said patient and comprises a accelerometer, a gyroscope, and a first wireless transceiver, and wherein said processing device comprises a processor, a memory device, a storage device, and a second wireless transceiver configured to communicate with the first wireless transceiver of the wireless sensor;

receiving, with said processing device, a second set of signals responsive to an angular velocity of said patient from said wireless sensor; and

generating a sensor data vector comprising a plurality of data elements, each of the plurality of data elements responsive to one of the first set of signals or one of the second set of signals;

generating a feature vector from said sensor data vector, the feature vector comprising a plurality of features indicative of a patient fall, wherein each of the plurality of features are generated based on one or more of the data elements from said sensor data vector;

deriving an activation value based on an application of a weight vector to said feature vector, wherein said weight vector comprises a plurality of weights, each of the plurality of weights corresponding to one of the plurality of features of said feature vector;

determining whether the patient fall has occurred based on the derived activation value; and

alerting a caregiver based on said determination.

15. The method of claim 14, wherein the first set of signals is responsive to the acceleration of said patient with respect to a roll axis, a pitch axis, and a yaw axis, said roll axis corresponding to a longitudinal axis extending along a length of said patient'"'"'s body, said pitch axis corresponding to said patient'"'"'s hips, and said yaw axis corresponding to a horizontal plane in which said patient is located.

16. The method of claim 14, wherein said plurality of features comprises a fall duration, said fall duration determined by evaluating an acceleration profile of said patient over a first time period.

17. The method of claim 16, further comprising determining a starting point of the patient fall by comparing a first magnitude of acceleration during the acceleration profile to a first threshold.

18. The method of claim 17, further comprising determining an impact point of the patient fall by comparing a second magnitude of acceleration during the acceleration profile to a second threshold.

19. The method of claim 14, wherein said plurality of features comprises a pitch change feature, said pitch change feature determined by comparing a first pitch orientation of the patient with a second pitch orientation of the patient, said first pitch orientation occurring prior to said second pitch orientation.

20. The method of claim 19, wherein said first pitch orientation is determined one second prior to said second pitch orientation.