SYSTEMS AND METHODS FOR PATIENT FALL DETECTION

• US 20170055882A1
• Filed: 08/31/2016
• Published: 03/02/2017
• Est. Priority Date: 08/31/2015
• Status: Active Grant

• Current Assignee: Masimo Corporation
• Original Assignee: Masimo Corporation
• Inventors: Raths, Cornelius, Al-Ali, Ammar
• Granted Patent: US 10,448,844 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 at least one of whether a monitored patient has fallen or is about to fall by processing signals indicative of movement by the patient, the signals output from a wireless sensor and communicated to a processing device, the sensor including an accelerometer, a gyroscope, a first processor, and a first wireless transceiver, the sensor being configured to be worn by the patient, the processing device having a signal processor, a memory device, a storage device, a display, and a second wireless transceiver, the processing device configured to process the signals to determine at least one of whether the patient has fallen or is about to fall, the method comprising:

receiving, by said processing device from said wireless sensor, signals responsive to a linear acceleration and an angular velocity of said patient; and

processing said signals with said signal processor of said processing device, including electronically;

forming a sensor data vector comprising data elements responsive to said linear acceleration and said angular velocity;

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

determining from said normalized sensor data vector, a plurality of features indicative of a patient fall, to form a feature vector;

applying an a priori weight vector to said feature vector to derive an activation value;

analyzing said derived activation value to determine at least one of whether a patient fall has occurred or is about to occur; and

when said determination is that the at least one of said patient fall has occurred or is about to occur, alerting a caregiver.

2. The method of claim 1, wherein said receiving said signals includes receiving said signals indicative of said linear acceleration responsive to an output of an accelerometer and receiving said signals indicative of said angular velocity responsive to an output of a gyroscope.

3. The method of claim 2,

wherein said receiving said signals indicative of said linear acceleration includes receiving said signals indicative of said linear acceleration in three dimensions,

wherein said receiving said signals indicative of said angular velocity includes receiving said signals indicative of said angular velocity in three dimensions, and

wherein said sensor vector comprises six data elements.

4. The method of claim 1, wherein said normalizing said data elements comprises:

normalizing each of said data elements to have zero-mean and unit-variance; and

forming said normalized sensor data vector comprising normalized data elements, wherein certain of said normalized data elements correspond to said linear acceleration and certain of said normalized data elements correspond to said angular velocity.

5. The method of claim 1, wherein determining said plurality of features to form a feature vector further comprises:

determining an acceleration magnitude;

determining an angular velocity magnitude;

determining a jerk magnitude;

determining a fall duration;

determining a pitch change; and

determining vertical velocities.

6. The method of claim 1, wherein said applying said weight vector to said feature vector to derive said activation value comprises computing an inner product of said feature vector with said weight vector.

7. The method of claim 1, wherein said applying said weight vector to said feature vector to derive said activation value 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.

8. The method of claim 7, wherein said mapping said example inputs to said known outputs to derive said weight vector is performed by Fishers'"'"' linear discriminant.

9. The method of claim 1, wherein said analyzing said derived activation value to determine at least one of whether a patient fall has occurred or is about to occur comprises identifying a sign attribute of said derived activation value, wherein a positive sign attribute of said derived activation value indicates that said patient has fallen or is about to fall.

10. A system configured to determine at least one of whether a patient has fallen or is about to fall, the system comprising:

a wireless physiological sensor including an accelerometer, a gyroscope, a processor, and a first wireless transceiver, said sensor configured to sense a linear acceleration and an angular velocity of said patient, said sensor also configured to transmit information indicative of said sensed linear acceleration and angular velocity of said patient;

a patient monitor comprising a signal processor, a memory device, a storage device, a communications interface, a display, and a second wireless transceiver, said patient monitor configured to receive said transmitted information, to analyze said received information, and to determine at least one of whether said patient has fallen or is about to fall, said patient monitor further configured to transmit, in response to determining said patient has fallen or is about to fall, a notification that said patient has fallen or is about to fall.

11. The system of claim 10, wherein said patient monitor is further configured to determine from said received information one or more of an acceleration magnitude, an angular velocity magnitude, a jerk magnitude, a fall duration, and a pitch change, and to form a feature vector comprising said determined acceleration magnitude, angular velocity magnitude, jerk magnitude, fall duration, and pitch change.

12. The system of claim 11, wherein said patient monitor is further configured to apply a weight vector to said feature vector.

13. The system of claim 12, wherein said patient monitor is further configured to determine an inner product of said weight vector and said feature vector.

14. The system of claim 10, wherein said accelerometer comprises a three-axis accelerometer and said gyroscope comprises a three-axis gyroscope.

15. The system of claim 12, wherein said weight vector is derived using a supervised learning algorithm comprising executable instructions stored on a computer-readable medium.

16. The system of claim 15, wherein said supervised learning algorithm is configured to execute on a processing device, to receive a set of training data having example inputs and known outputs, and to map said example inputs to said known outputs to derive said weight vector.

17. The system of claim 15, wherein said supervised learning algorithm is Fisher'"'"'s linear discriminant.