WO2011064697A1 - Procédé pour estimer des vitesses et/ou des déplacements à partir d'échantillons de mesure d'accéléromètre - Google Patents

Procédé pour estimer des vitesses et/ou des déplacements à partir d'échantillons de mesure d'accéléromètre Download PDF

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Publication number
WO2011064697A1
WO2011064697A1 PCT/IB2010/055255 IB2010055255W WO2011064697A1 WO 2011064697 A1 WO2011064697 A1 WO 2011064697A1 IB 2010055255 W IB2010055255 W IB 2010055255W WO 2011064697 A1 WO2011064697 A1 WO 2011064697A1
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WIPO (PCT)
Prior art keywords
vertical
acceleration
fall detector
estimates
time series
Prior art date
Application number
PCT/IB2010/055255
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English (en)
Inventor
Constant Paul Marie Jozef Baggen
Ningjiang Chen
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Koninklijke Philips Electronics N.V.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics N.V. filed Critical Koninklijke Philips Electronics N.V.
Publication of WO2011064697A1 publication Critical patent/WO2011064697A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0446—Sensor means for detecting worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches

Definitions

  • the invention relates to a method for estimating velocities and displacements from measurement samples from an accelerometer, and in particular to a method for estimating vertical velocities and vertical displacements that can be used in detecting falls by a user.
  • Falls affect millions of people each year and result in significant injuries, particularly among the elderly. In fact, it has been estimated that falls are one of the top three causes of death in elderly people. A fall can be defined as a sudden, uncontrolled and unintentional downward displacement of the body to the ground followed by an impact.
  • PLBs Personal Help Buttons
  • Fall detectors are also available that process the output of one or more movement sensors to determine if the user has suffered a fall. However, it has been found that these fall detectors have an unfavorable trade-off between fall detection probability and false alarm rate.
  • an economically viable fall detector should provide a false alarm rate of, say, less than one false alarm in each two-month period, while maintaining a (positive) fall detection probability above 95 percent.
  • Most existing body-worn fall detectors make use of an accelerometer (usually a 3D accelerometer that measures acceleration in three dimensions) and they try to infer the occurrence of a fall by processing the time series generated by the accelerometer.
  • a fall detector can estimate a velocity and/or displacement for the fall detector from the accelerometer measurement samples and use these features (along with other features derived from the accelerometer measurement samples) to determine whether the user of the fall detector has suffered a fall.
  • fall detectors in the form of pendants that can be worn around a user's neck and that is otherwise free to move relative to the user; as such fall detectors are lightweight and unobtrusive in use.
  • existing methods for estimating the vertical velocity and vertical displacement do not provide sufficiently accurate estimates when applied to measurement samples obtained from an accelerometer in this type of fall detector.
  • a fall detector for use in detecting falls by a user, the fall detector comprising an accelerometer for producing a time series of measurement samples representing the acceleration acting on the fall detector; a processor for estimating a vertical velocity and/or vertical displacement of the fall detector from the measurement samples and using the estimated vertical velocity and/or vertical displacement to determine whether the user has suffered a fall; wherein the processor is configured to estimate a vertical velocity and/or vertical displacement of the fall detector from the measurement samples by estimating a corresponding time series of unit vectors representing acceleration due to gravity in the reference frame of the accelerometer from the time series of measurement samples; projecting each measurement sample onto the corresponding unit vector and subtracting acceleration due to gravity to give a series of estimates for the vertical acceleration of the fall detector; and integrating the series of estimates for the vertical acceleration over a time period to give a time series of values for the vertical velocity and/or vertical displacement of the fall detector.
  • a method of estimating a vertical velocity and/or vertical displacement of an object comprising an accelerometer, the method comprising obtaining a time series of measurement samples from the accelerometer representing the acceleration acting on the object; estimating a
  • a method for use in detecting falls by a user of a fall detector comprising an accelerometer, the method comprising estimating a vertical velocity and/or vertical displacement from measurement samples from the accelerometer as described above and using the estimated vertical velocity and/or vertical displacement to determine whether the user has suffered a fall.
  • a computer program product comprising computer program code that, when executed on a suitable computer or processor, is configured to cause the computer or processor to perform either of the methods described above.
  • Fig. 1 is a block diagram of a fall detector suitable for implementing the method in accordance with the invention
  • FIG. 2 is a flow chart illustrating a method in accordance with the invention
  • Fig. 3 illustrates step 103 of the flow chart in Figure 2 in more detail
  • Fig. 4 illustrates step 105 of the flow chart in Figure 2 in more detail
  • Fig. 5 is a graph illustrating an integration window that can be used to integrate vertical acceleration into vertical velocity in accordance with an embodiment of the invention.
  • a fall detector 2 capable of implementing the method according to the invention is shown in Figure 1.
  • the fall detector 2 is designed in the form of a pendant to be worn around the neck of a user that does not adversely affect the movement or balance of the user.
  • the fall detector 2 comprises an accelerometer 4 (in particular a 3D accelerometer that provides measurements of the acceleration along the three orthogonal measurement axes of the accelerometer 4) which is connected to a processor 6.
  • the processor 6 receives measurement samples from the accelerometer 4 and processes the measurement samples to estimate the vertical velocity of the fall detector 2 and/or the vertical displacement of the fall detector 2 over various time periods.
  • the processor 6 uses the vertical velocity and/or vertical displacement (usually in connection with other features derived from the accelerometer measurement samples) to determine whether the user has fallen.
  • the fall detector 2 also comprises a transmitter unit 8 that allows the fall detector 2 to transmit an alarm signal to a base station associated with the fall detector 2 (which can then issue an alarm or summon help from a healthcare provider or the emergency services) or directly to a remote station (for example located in call centre of a healthcare provider) if a fall is detected, so that assistance can be summoned for the user.
  • a base station associated with the fall detector 2 which can then issue an alarm or summon help from a healthcare provider or the emergency services
  • a remote station for example located in call centre of a healthcare provider
  • the fall detector 2 can further comprise other sensors in addition to the accelerometer, such as a pressure sensor, magnetometer and/or gyroscope.
  • the fall detector 2 may also comprise an audible alarm unit that can be activated by the processor 6 in the event that the processor 6 determines that the user has suffered a fall.
  • the fall detector 2 may also be provided with a button that allows the user to manually activate the audible alarm unit if they require assistance (or deactivate the alarm if assistance is not required).
  • step 101 a time series of measurement samples are obtained from the accelerometer 4.
  • measurement sample is a three-dimensional vector representing the measured acceleration in the reference frame of the accelerometer 4. Subsequent steps in the method operate on these measurement samples to obtain an estimate of the vertical velocity and/or vertical
  • step 103 'relatively quiet' periods in the measurement samples are identified in which the vertical acceleration and vertical velocity are approximately zero.
  • the end point of a 'relatively quiet' period and the start point of the next 'relatively quiet' period provide boundary values for the estimation of the vertical velocity and/or vertical
  • a 'non-quiet' period is identified (i.e. a period in which the vertical acceleration and/or vertical velocity are substantially nonzero).
  • step 103 A preferred implementation of step 103 is described in more detail below with reference to Figure 3.
  • step 1031 of Figure 3 the absolute acceleration is computed for each measurement sample by taking the square root of the sum of the squares of the three components of the sample: where a x , a y and a z are the components of the acceleration along each of the measurement axes of the accelerometer 4.
  • step 1033 acceleration due to gravity (9.81ms 2 ) is subtracted from the absolute acceleration to obtain a normalized absolute acceleration.
  • the normalized absolute acceleration is squared to obtain the power of the normalized absolute acceleration (step 1035) and a moving average (MA) of the power of the normalized absolute acceleration is taken to obtain an MA-filtered power of the normalized absolute acceleration (step 1037).
  • the duration of the MA filter is 0.5 seconds.
  • the MA-filtered power of the normalized absolute acceleration is compared with a non-zero threshold value to identify the sample times that are relatively quiet. For example, if in a two-second interval the value of the MA-filtered power is less than 10, the middle of the interval can be defined as 'relatively quiet'.
  • a non-zero threshold value For example, if in a two-second interval the value of the MA-filtered power is less than 10, the middle of the interval can be defined as 'relatively quiet'.
  • step 105 a unit vector
  • a median filter is applied to each of the three acceleration components in each measurement sample to obtain three time series of median- filtered acceleration components.
  • the duration of the median filter is 0.5 seconds.
  • a unit gravity time series is computed by normalizing the length of each of the median- filtered acceleration components at each sampling time using an absolute value derived from each of the three median-filtered acceleration components at that sample time.
  • the gravity unit vector g in the reference frame of the accelerometer 4 at a sampling time t is given by:
  • each acceleration measurement sample is projected onto its respective unit gravity vector (step 107). That is, a measurement sample obtained for a time t is projected onto the gravity unit vector determined for the sample at time t.
  • the projection of the acceleration measurement sample a onto the gravity unit vector g gives a scalar value a v representing the magnitude of the acceleration in a vertical direction.
  • Acceleration due to gravity i.e. 9.81ms "2
  • Acceleration due to gravity i.e. 9.81ms "2
  • step 109 the initial estimate of the vertical acceleration is refined by correcting for excess acceleration due to sampling errors and/or clipping of the measurements made by the accelerometer 4.
  • Sampling errors and/or clipping can occur, for example, when an impact occurs, so the measurement samples from the accelerometer 4 are less reliable or accurate at these times.
  • the excess vertical acceleration is divided across each of the measurement samples in the 'non-quiet' period to correct the initial estimate of the vertical acceleration. After correction, the integral of the corrected vertical accelerations over the 'non-quiet' period should be zero.
  • the excess vertical acceleration in the initial estimate is preferably corrected proportionally according to the locally computed "filtered impact".
  • This proportional correction is applied by (i) computing the absolute value of the vertical accelerations in the 'non-quiet' period, (ii) determining the sum of the absolute accelerations in the 'non-quiet' period and (iii) dividing the excess acceleration among the initial estimates of the vertical acceleration in the 'non-quiet' period, where each estimate obtains a fraction of its absolute value divided by said sum.
  • the fraction is given by impact _ filt(i)
  • impact filt(i) is the average filtered normalized absolute acceleration for the i-th estimate and n is an integer.
  • the non-quiet period is generally in the range of a second or a few seconds, and with a sampling frequency of 50 Hz, n will be of the order of 50 to 100. This correction results in the refined estimate of the vertical acceleration.
  • step 111 the refined estimate of the vertical acceleration is integrated over a 'non-quiet' period to give a time series of values for the vertical velocity and/or twice integrated over the 'non-quiet' period to give a time series of values for the vertical displacement.
  • a non-rectangular integration window is used, for example as shown in Figure 5.
  • the estimated vertical velocity is determined from a weighted sum of the M most recent estimates of the vertical acceleration, where the weighting decreases as estimates get older.
  • M is 50.
  • a similar type of window is also used for calculating the vertical displacement.
  • the determined vertical velocity and/or vertical displacement can be used by the fall detector 2, possibly in conjunction with values for other features that are
  • the fall detector 2 may also determine values for other features from the determined vertical velocity and/or vertical displacement themselves. For example, if the user has suffered a fall, there is likely to be a minimum vertical velocity of -1.3 ms -1 (i.e. 1.3 ms -1 downwards), and the fall detector 2 can use a local minimum in the vertical velocity to identify a point at which a fall has occurred.
  • the method according to the invention provides significant improvements in the estimation of vertical velocities and/or vertical displacements in pendant-type fall detectors (i.e. fall detectors that can move relatively freely when worn by the user), it will be appreciated that the method can also provide improvements in the estimation of vertical velocities and vertical displacements in other types of fall detector, such as those worn on a user's wrist, at their waist, on their chest or on their back.
  • pendant-type fall detectors i.e. fall detectors that can move relatively freely when worn by the user
  • the method can also provide improvements in the estimation of vertical velocities and vertical displacements in other types of fall detector, such as those worn on a user's wrist, at their waist, on their chest or on their back.
  • the processor 6 in the fall detector 2 determines the vertical velocity and/or vertical displacement from the measurement samples from the accelerometer 4
  • the transmitter unit 8 can be used to transmit the measurement samples from the accelerometer 4 to the base station or remote station, and the vertical velocity and/or vertical displacement can be determined in the base station or remote station.
  • the base station or remote station can also carry out the processing to determine whether the user has suffered a fall.

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Abstract

L'invention concerne un détecteur de chute destiné à être utilisé lors de la détection de chutes d'un utilisateur, le détecteur de chute comprenant un accéléromètre permettant de produire une série dans le temps d'échantillons de mesure représentant l'accélération agissant sur le détecteur de chute ; un processeur permettant d'estimer la vitesse verticale et/ou le déplacement vertical du détecteur de chute à partir des échantillons de mesure et utilisant la vitesse verticale et/ou le déplacement vertical estimés afin de déterminer si l'utilisateur a subi une chute ; le processeur étant configuré pour estimer une vitesse verticale et/ou un déplacement vertical du détecteur de chute à partir des échantillons de mesure en estimant une série dans le temps correspondante de vecteurs élémentaires représentant l'accélération due à la gravité dans un cadre de référence de l'accéléromètre à partir de la série dans le temps d'échantillons de mesure, en projetant chaque échantillon de mesure sur le vecteur élémentaire correspondant et en soustrayant l'accélération due à la gravité pour donner une série d'estimations de l'accélération verticale du détecteur de chute, et en intégrant la série d'estimations de l'accélération verticale sur un intervalle de temps pour donner une série dans le temps de valeurs de la vitesse verticale et/ou du déplacement vertical du détecteur de chute.
PCT/IB2010/055255 2009-11-25 2010-11-18 Procédé pour estimer des vitesses et/ou des déplacements à partir d'échantillons de mesure d'accéléromètre WO2011064697A1 (fr)

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CN200910226559.5 2009-11-25

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105726034B (zh) * 2016-01-29 2018-08-10 江阴中科今朝科技有限公司 基于摔倒报警与跟踪定位功能的腕表式智能养老看护平台
SE1800077A1 (en) * 2018-01-30 2019-07-31 Infonomy Ab Method and system for wearable balance meter II
CN118021294A (zh) * 2024-04-11 2024-05-14 四川省铁路建设有限公司 一种基于多传感器的跌倒检测方法及系统

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009138900A1 (fr) * 2008-05-12 2009-11-19 Koninklijke Philips Electronics N.V. Mesure de déplacement dans un système de détection de chute

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009138900A1 (fr) * 2008-05-12 2009-11-19 Koninklijke Philips Electronics N.V. Mesure de déplacement dans un système de détection de chute

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
BOURKE A K ET AL: "The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls", MEDICAL ENGINEERING & PHYSICS, BUTTERWORTH-HEINEMANN, GB, vol. 30, no. 7, 1 September 2008 (2008-09-01), pages 937 - 946, XP024523813, ISSN: 1350-4533, [retrieved on 20080220], DOI: DOI:10.1016/J.MEDENGPHY.2007.12.003 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105726034B (zh) * 2016-01-29 2018-08-10 江阴中科今朝科技有限公司 基于摔倒报警与跟踪定位功能的腕表式智能养老看护平台
SE1800077A1 (en) * 2018-01-30 2019-07-31 Infonomy Ab Method and system for wearable balance meter II
CN118021294A (zh) * 2024-04-11 2024-05-14 四川省铁路建设有限公司 一种基于多传感器的跌倒检测方法及系统

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