EP2207154A1 - Verfahren und Vorrichtung zur Detektion eines Sturzes - Google Patents

Verfahren und Vorrichtung zur Detektion eines Sturzes Download PDF

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Publication number
EP2207154A1
EP2207154A1 EP10305024A EP10305024A EP2207154A1 EP 2207154 A1 EP2207154 A1 EP 2207154A1 EP 10305024 A EP10305024 A EP 10305024A EP 10305024 A EP10305024 A EP 10305024A EP 2207154 A1 EP2207154 A1 EP 2207154A1
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EP
European Patent Office
Prior art keywords
user
vector
fall
detecting
acceleration
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Granted
Application number
EP10305024A
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English (en)
French (fr)
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EP2207154B8 (de
EP2207154B1 (de
Inventor
Laurent Magdelaine
Eric Martin
Philippe Mate
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Info Network Systems
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Info Network Systems
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    • 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
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
    • G08B29/18—Prevention or correction of operating errors
    • G08B29/20—Calibration, including self-calibrating arrangements
    • G08B29/24—Self-calibration, e.g. compensating for environmental drift or ageing of components
    • G08B29/26—Self-calibration, e.g. compensating for environmental drift or ageing of components by updating and storing reference thresholds

Definitions

  • the present invention relates to a method of detecting a fall of a person and the device associated with this method.
  • known devices of the prior art in the form of medallion, bracelets, or belts, allowing their user to send a call for help by means of a voluntary decision, such as pressing a button push, in case of distress or discomfort.
  • a major disadvantage of these devices of the prior art is to require a voluntary action by the user to trigger the sending of the alert signal.
  • a fall detection device must therefore be the least inconvenient in the habits or activities of the user, and also, said device must be discreet so that the user does not refuse to wear it vis-à-vis his entourage .
  • said device must be easily attached to a simple place.
  • the detection at the wrist is more difficult because we do not know the position of the trunk of the user.
  • the sensor adopted by most devices of the prior art for the fall detection is the accelerometer for measuring shocks and movements.
  • a fall detection device must absolutely avoid triggering alerts inadvertently, the accumulation of false alerts to remote assistance centers, or other people, discrediting the detection and may lead to unnecessary interventions.
  • This multiplication of the measuring means increases the size of the detection device, which goes against the criteria of discretion of the device in the life of the user, and especially the cost of the device.
  • the subject of the invention is a method for detecting a fall of a user, said user wearing a detection device comprising a housing enclosing supply means, means for transmitting an alert signal, an accelerometer, and means for processing the measurements provided by the accelerometer comprising calculation means and storage means, said method being characterized in that it consists in classifying the behavior of the user on the basis of indicators of its activity resulting from acceleration measurements acquired by the accelerometer and to postpone the triggering of an alert signal to avoid false alarms.
  • the method of detecting a fall of a user according to the invention is carried out by means of a detection device carried by a user.
  • the invention includes a person, such as an elderly person, a disabled person, a patient of a medical center, or an isolated worker, said person being likely to fall, to feel unwell, and to fall unconscious because of his difficulties, his disability or the conditions of his activity.
  • FIG. figure 1 Such a detection device 10 used for the implementation of the method according to the invention is illustrated in FIG. figure 1 .
  • Said detection device 10 comprises a closed housing 12, sealed, and connected to fixing means 14.
  • the housing 12 is composed of two half-parts of polymer material obtained by injection / molding, the sealing of said housing being achieved through an assembly by ultrasonic welding of the two half-parts.
  • the fastening means 14 support the housing and attach the assembly thus formed to a body portion of said user.
  • the housing 12 and its attachment means 14 will take the form of a bracelet, a pendant or a device attached to the belt.
  • Said housing 12 encloses power supply means 16, means for transmitting an alert signal 18, an accelerometer 20, and processing means 22 for measurements provided by the accelerometer.
  • the power supply means 16 take the form of at least one commercial battery, such as a so-called "button” battery of a few hundred milliampere hours to give an order of ideas.
  • the transmission means 18 are capable of sending an electromagnetic, radiofrequency or other waveform warning signal to a remote assistance center or to any other designated person, directly or via a base connected to a network of telephones.
  • telecommunication such as Internet, GSM or other.
  • the accelerometer 20 is a three-axis accelerometer
  • the processing means 22 comprise calculation means 24 and storage means 26 in the form of a random access memory and a mass memory.
  • the detection device 10 comprises means of voluntary call 28, such as a push button, allowing the user to trigger itself an alert signal, the automatic detection of fall being placed as a complement to the voluntary call of the user.
  • the method according to the invention aims at to perform a reliable treatment, and economic in terms of electrical energy consumption, measurements made by the accelerometer to deduce the supposed behavior of the wearer.
  • said method consists in classifying the behavior of the user on the basis of indicators of his activity resulting from the acceleration measurements acquired by the accelerometer 20 and deferring the triggering of an alert signal so as to avoid false alerts.
  • only acceleration measurements are used to deduce the activity of the carrier.
  • the invention also covers a drop detection method that would use measurements of another physical quantity in parallel with the steps of the method as will now be described.
  • the method according to the invention focuses solely on the perception of accelerations.
  • the acceleration measurements made by the accelerometer 20 of the detection device 10 comprise several components.
  • the user and therefore the accelerometer 20 attached to a part of his body are subjected to a constant acceleration to the ground due to gravity.
  • This user-specific acceleration component is due to accelerations experienced or initiated by the part of the body at which the accelerometer is fixed as well as the whole body.
  • the perceived accelerations are due to movements of the forearm, the arm and finally the whole body, thus revealing the activity, more or less important and disordered, of the user.
  • the previous acceleration measurements make it possible to establish variables relating to the gravity and the nature of the activity of the wearer, defining a state of perception.
  • Each new acceleration measure modifies said variables and changes the perception state that is used to trigger an alert or not.
  • the solution adopted by the invention to arrive at an estimate of the gravity vector with a good confidence rate is to use a sphere of unit radius discretized in several vertices to model all the possible directions of the unit vector g , and a stochastic calculation to determine the probability carried by each vertex that the vector g is exactly in the direction indicated by this vertex.
  • the figure 2 can be chosen a tetrahedron, an octahedron, a hexahedron (cube), an icosahedron or a dodecahedron, having respectively 4, 6, 8, 12 and 20 vertices, the dodecahedron being the solid giving the best results because of the density of its mesh.
  • the general principle of the invention for determining the gravity vector g is therefore to determine for each vertex the probability that said gravity vector is exactly in the direction indicated by said vertex.
  • the determination of the probabilities carried by a set of certain directions of the vector g makes it possible to deduce the probable direction of this vector as well as the degree of confidence in this direction.
  • the vector thus obtained being of the form: ⁇ . g , it must be normalized in order to obtain the probable vector g, the degree of confidence then being a function of ⁇ .
  • the solution adopted to determine the probability carried by each vertex of the selected solid is in the field of stochastic and Bayesian inference, in particular Markov chains and more precisely H.M.M. (Hidden Markov Model) or hidden Markov chains.
  • the set of vertices of the Platonic solid used represents the states of a Markov chain used within a hidden Markov model and whose coefficients of the transition matrix Tr are function of the distance (linear or angular) between two vertices, and the emitting function Em is a function of the own acceleration vector v , deduced by subtracting the vector g designated by each vertex to the observed accelerometric vector w.
  • v n w - g n .
  • the transition matrix Tr can be simplified by taking into account only the direct neighbors. of each vertex, thus defining the elemental lattice of the Markov chain where each vertex, as illustrated in figure 3 , in addition to being connected to itself, is connected to each of its neighbors.
  • the emission function Em can also simplify the emission function Em by defining the probability of emission as a function of a supposedly Gaussian distribution of the norm of the vector v .
  • the probabilities carried by a set of certain directions of the gravity vector allow us to deduce the most probable direction for this vector, while determining its degree of confidence, the gravity detector behaves substantially like a two-axis probabilistic gyroscope.
  • the raw acceleration measurements provided by the accelerometer 20 undergo a normalization step 40.
  • said normalization 40 consists of applying offsets and scaling coefficients to the raw measurements in order to obtain a representation of said measurements in the form of a standardized three-dimensional vector.
  • a simple criterion used to determine the parameters of this normalization is that, in the absence of proper acceleration or when it is very close to zero, and whatever the position of the detection device, the norm of the vector measured w , which is then substantially equal to g , must be as close as possible to one.
  • the Platonic solid preferably used is a hexahedron, a cube.
  • the application 42 of the transition matrix introduces a probabilistic blur on the knowledge of the direction of the vector g .
  • the transition matrix Tr has been implemented by taking into account only the direct neighbors of each vertex of the selected solid, it is therefore defined by a single rotation coefficient ⁇ .
  • the coefficient of the transition matrix Tr from a vertex to itself is then 1- ⁇ , and that of transition from one vertex to one of its neighbors is ⁇ / 3, the others being considered null (or very close to zero).
  • This rotation coefficient ⁇ then represents the probability that, from one instant to another, the direction of gravity has moved from a vertex to a neighboring vertex.
  • the application 44 of the emission function Em provides the probability that an observation w will occur assuming that the direction of the vector g is exactly on each of the vertices n of the solid.
  • the corresponding acceleration vector v n corresponding can then easily be computed by a simple subtraction. Since the natural accelerations are considered as marginal, it is assumed that the probability of occurrence of this vector follows a Gaussian distribution according to its norm.
  • This function is implemented as a table of discretized values and the probabilities are then obtained by linear interpolation.
  • the evaluation function 46 can determine the most probable direction for the gravity vector g , performing the weighted vector sum.
  • the vector thus obtained, of the form ⁇ . g denotes the most likely direction for g , and its norm ⁇ is proportional to the concentration of probabilities around that direction.
  • the unitarization function 48 in order to obtain the vector g , and the confidence function 50 provides the confidence rate in this direction, which is a function of the norm ⁇ .
  • the Markov chain making it possible to obtain an estimate of the vector g at the output of the unitarization function 48 introduces a form of inertia to the change, it is therefore necessary to delay the observed acceleration vector w with the help of the delay function 52 to obtain, by the subtraction function 54, the own acceleration vector v delayed.
  • the following information is available: an estimate of the terrestrial gravity vector g , an indicator of the confidence rate of this vector, a function of ⁇ , and therefore, by subtraction, an estimate of the vector d clean acceleration v .
  • the detection method according to the present invention then provides for the transformation and aggregation of said data into indicators, both qualitative and quantitative, of the user's activity.
  • the detection method according to the invention then provides a classification step of the behavior of the user made from these observation parameters.
  • the behavior classifier is implemented in the form of a hidden state machine Markov (H.M.M.), whose hidden part represents the state of the carrier and the observable part is provided by the previously calculated indicators.
  • H.M.M. hidden state machine Markov
  • a simplified version of the possible states, or equivalence classes, of the user's behavior is represented on the figure 5 a rest state 56, an active state 58, a falling state 60 and a state of unconsciousness 62.
  • Each of these states (56,58,60,62) carries the probability that the user is in this state, depending on the previous state and new observations.
  • the probabilities of being in one of the four states (56,58,60,62) of the automaton evolve over the observations, and it is enough to examine the probability of the state of unconsciousness 62 to decide the sending an alert using the transmission means 18 as soon as it passes a predetermined threshold corresponding to a supposed abnormal behavior of the user.
  • the fall detection method that has just been described can be implemented in processing means 22 taking for example the form of a microcontroller.
  • the storage means 26 require only a useful random access memory of about 200 bytes and a useful mass memory of about 6 kilobytes. Also, with a 16-bit microcontroller, the computing times achieved by the processing means 22 accelerometer data 4 megahertz range between 0.3 milliseconds and 1.4 milliseconds with an average of about 0.6 milliseconds.
  • the accelerometric acquisitions are performed at 25 Hertz in normal mode and can go down to 1 Hertz in the case of prolonged rest or during the user's sleep.
  • the method according to the invention makes it possible to limit the useful capacities of the processing means, hence a significant decrease in their consumption of electrical energy and a significant extension of the autonomy of the detection device vis-à-vis those obtained with the detection methods of the prior art.
  • the implementation of the detection method as just described in said processing means 22 makes it possible to obtain a detection device 10 whose autonomy is at least greater than one year for means power supply 16 providing at most five hundred milliamperes-hours.
  • a two-axis accelerometer may be sufficient to perform a reliable and economical fall detection using the method according to the invention.
  • the invention also covers the applications of the detection method which has just been described to movements other than a fall, as well as to persons or objects in situations different from those set out above.

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  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Gerontology & Geriatric Medicine (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Computer Security & Cryptography (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Engineering & Computer Science (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Alarm Systems (AREA)
  • Emergency Alarm Devices (AREA)
  • Spinning Or Twisting Of Yarns (AREA)
EP10305024.1A 2009-01-12 2010-01-11 Verfahren und Vorrichtung zur Detektion eines Sturzes Active EP2207154B8 (de)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
FR0950127A FR2941081A1 (fr) 2009-01-12 2009-01-12 Procede de detection de chute

Publications (3)

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EP2207154A1 true EP2207154A1 (de) 2010-07-14
EP2207154B1 EP2207154B1 (de) 2011-10-19
EP2207154B8 EP2207154B8 (de) 2019-10-02

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EP (1) EP2207154B8 (de)
AT (1) ATE529844T1 (de)
ES (1) ES2376044T3 (de)
FR (1) FR2941081A1 (de)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103027687A (zh) * 2012-12-05 2013-04-10 北京工业大学 基于3d加速度仪和陀螺仪的老年人跌倒检测报警系统
CN104182231A (zh) * 2014-08-22 2014-12-03 Tcl通讯（宁波）有限公司 一种加速度传感器应用功能的运行控制方法及系统
CN111383420A (zh) * 2018-12-29 2020-07-07 深圳市华赢飞沃科技有限公司 一种跌倒检测方法及设备

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105342623B (zh) * 2015-09-25 2018-05-29 广东乐源数字技术有限公司 智能跌倒监护装置及其处理方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2874727A1 (fr) 2004-09-02 2006-03-03 Thierry Burbaud Dispositif portable de detection, d'alerte et de transmission d'informations relatives a une personne physique
US20060139166A1 (en) * 2004-12-09 2006-06-29 Christian Choutier System and method for monitoring of activity and fall
WO2007057692A2 (en) * 2005-11-18 2007-05-24 Lusora Limited Detection of a person falling
US20070146145A1 (en) * 1999-09-15 2007-06-28 Lehrman Michael L System and method for analyzing activity of a body

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070146145A1 (en) * 1999-09-15 2007-06-28 Lehrman Michael L System and method for analyzing activity of a body
FR2874727A1 (fr) 2004-09-02 2006-03-03 Thierry Burbaud Dispositif portable de detection, d'alerte et de transmission d'informations relatives a une personne physique
US20060139166A1 (en) * 2004-12-09 2006-06-29 Christian Choutier System and method for monitoring of activity and fall
WO2007057692A2 (en) * 2005-11-18 2007-05-24 Lusora Limited Detection of a person falling

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103027687A (zh) * 2012-12-05 2013-04-10 北京工业大学 基于3d加速度仪和陀螺仪的老年人跌倒检测报警系统
CN103027687B (zh) * 2012-12-05 2015-03-04 北京工业大学 基于3d加速度仪和陀螺仪的老年人跌倒检测报警系统
CN104182231A (zh) * 2014-08-22 2014-12-03 Tcl通讯（宁波）有限公司 一种加速度传感器应用功能的运行控制方法及系统
CN104182231B (zh) * 2014-08-22 2018-07-17 Tcl通讯（宁波）有限公司 一种加速度传感器应用功能的运行控制方法及系统
CN111383420A (zh) * 2018-12-29 2020-07-07 深圳市华赢飞沃科技有限公司 一种跌倒检测方法及设备

Also Published As

Publication number Publication date
EP2207154B8 (de) 2019-10-02
EP2207154B1 (de) 2011-10-19
ES2376044T3 (es) 2012-03-08
FR2941081A1 (fr) 2010-07-16
ATE529844T1 (de) 2011-11-15

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