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WO2009083032A1 - Procédé de détection d'épisodes de blocage physique lors d'une activité d'un individu - Google Patents

Procédé de détection d'épisodes de blocage physique lors d'une activité d'un individu Download PDF

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Publication number
WO2009083032A1
WO2009083032A1 PCT/EP2007/064613 EP2007064613W WO2009083032A1 WO 2009083032 A1 WO2009083032 A1 WO 2009083032A1 EP 2007064613 W EP2007064613 W EP 2007064613W WO 2009083032 A1 WO2009083032 A1 WO 2009083032A1
Authority
WO
WIPO (PCT)
Prior art keywords
physical blocking
movement signal
episodes
detect physical
movement
Prior art date
Application number
PCT/EP2007/064613
Other languages
English (en)
Inventor
Haritz Zabaleta Recondo
Thierry Keller
Eric Fimbel
Original Assignee
Fundacion Fatronik
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 Fundacion Fatronik filed Critical Fundacion Fatronik
Priority to PCT/EP2007/064613 priority Critical patent/WO2009083032A1/fr
Publication of WO2009083032A1 publication Critical patent/WO2009083032A1/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—Measuring 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 or mobility of a limb
    • A61B5/112—Gait analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring 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 or mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring 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 or mobility of a limb
    • A61B5/1124—Determining motor skills
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4082—Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6828—Leg
    • 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
    • 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
    • A61B2562/028—Microscale sensors, e.g. electromechanical sensors [MEMS]

Definitions

  • the present invention relates to a method designed to detect physical blocking episodes on an individual activity.
  • the method is based on the data acquired by a number of sensors that monitor continuously the individual while he is moving. When the individual remains in a stable position, that is, when the measured jerk is less than to a pre-established threshold for a period of time, the system parameters are readjusted.
  • 01/087411 and WO 2003/039662 focus on cue generation devices, acoustic, visual and electrical, according to the walking rhythm of the user, but again, their cue is generated continuously while the device is turned on only.
  • the cue For an effective unblock of the freezing, the cue has to be generated only while the blocking episode appears, and leave the control of the body movement to the motor control system during the time that the movement is working properly.
  • a body movement data acquisition system with possibility of off-line post processing of data is, for example, described in the doctoral thesis entitled "Ambulatory monitoring of motor functions in patients with
  • the system needs to have user-condition parameters detected and updated frequently. These parameters can be determined during periods of quiet body, that is, the subject remains quiet in a certain position no matter in witch position lying, sitting or upright standing
  • Residual body limb movements e. g. body sway produces movement data that can serve for characterizing the user condition automatically.
  • the used condition parameters can be used to automatically tune the blocking detection module at discrete times.
  • the invention relates to a method to detect physical blocking episodes, also known or referred as freezing, on an individual activity as, for example, walking.
  • the present invention discloses the steps needed to carry out how to detect a physical blocking episodes and whether the body is quiet or in motion.
  • the user-condition parameters are determined and stored in a memory. These parameters are used to recalibrate the blocking detection unit. Else, if the body is in motion the system distinguishes between voluntary movement and movement blocking or freezing.
  • Detection of physical blocking is performed continuously and requires to carry out, firstly, an acquisition of movement signals of at least one body part of an individual.
  • Said body parts can be selected from, for example, the trunk, the thigh, the shank or the foot.
  • the movement signals comprise of acceleration and rotational speeds. Sensors may be attached to more than one body part.
  • jerk values are determined for the accelerations measured. As it has been previously disclosed, jerk is the rate of change of the acceleration.
  • the movement signals are used to determine if the body is quiet.
  • the derivative of acceleration, jerk can be used as measure (Fimbel, E. et a/, 2003, "Event identification in movement recordings by means of qualitative patterns").
  • the jerk of the movement signal remains below a predetermined threshold value, whereas for walking or other movements the jerk presents episodes with values higher than the predetermined threshold. These higher values can be named as jerk peaks.
  • the predetermined threshold value is dependent on the filter characteristics of the filtering module.
  • a jerk threshold value of 0.2 g/s is a sufficient value for distinction of quiet body or movement. If there is no jerk peaks for longer than a determined period of time, e.g. 4 s, the signals are considered stable and therefore the body is thought to be quiet. Moreover, if the system is equipped with more than one accelerometer, one in each lower limb segment for example, the system is able to distinguish between different types of quiet body, e.g. between quiet lying, quiet sitting and quiet standing by comparing the accelerations measured with the values of the following tables.
  • the user- condition identification unit analyses the stable movennent signals and stores the user-condition parameters in memory.
  • the knowledge about the actual user condition is necessary to adjust the jerk thresholds to the activity states of the user caused for example by medications.
  • the user-condition changes steadily during medication cycle, typically a cycle of 6 hours, and therefore, the user-condition identification is only required from time to time, and therefore the analysis only needs to be performed, for example, 30 seconds after the previous parameter identification. In case this time has not been elapsed the signal is considered not stable.
  • the physical blocking detection can be performed considering the step duration.
  • An increase in the number of steps for a certain time period, in other words, a continuing decrease of the step duration, can be considered as a blocking episode.
  • a timestamp can be stored and an output signal can be generated.
  • the physical blocking detection can be performed considering a dominant frequency component method.
  • the movement signals can be transformed to the frequency domain, using, for example the short time fourier transform, STFT, obtaining a dominant frequency per time frame.
  • the movement signals are first divided into overlaping or non-operlapping time frames of frame length of a duration of less than 10 seconds. Then each frame is windowed and transformed into the frequency domain.
  • the dominant frequency is the frequency with the highest spectral power of the transformed movement signal. If the dominant frequency on each time frame considered has a continuing change towards higher frequencies, e.g. in more than 2 Hz in a given number of time frames, motor blocking or freezing is detected.
  • threshold The increase to higher frequencies, or threshold, that must occur to detect the motor blocking is as well dependent on the user-condition, which is determined during the user-condition identification. This implies that the same frequency increase may imply freezing in one situation and may not imply freezing in another situation. This different outcome of the method reflects the different behaviour of the body of an individual depending on the effect along a time period of the drugs dosed to a patient. If the threshold, defined according to the user-condition parameters, is reached or passed a timestamp can be stored and an output signal can be generated.
  • the physical blocking detection can be performed considering a spectral centroid of a signal.
  • the spectral centroid of a signal is the midpoint of its spectral density function, i.e. the frequency that divides the distribution into two equal parts.
  • the signal is also divided into overlapping or non-overlapping time frames and the detection method works similar as the dominant frequency variation. If the spectral centroid has a continuing change towards a higher frequencies, e.g. in more than 2 Hz, in a given number of time frames, motor blocking is detected.
  • a frequency threshold can be defined in order not to produce unnecessary output signals. The threshold values are dependant on the user-condition, and are determined during the user-condition identification.
  • the threshold is reached or passed a timestamp can be stored and an output signal can be generated.
  • the threshold depends on the user-condition parameters.
  • the same change in the spectral centroid may or may not imply freezing depending on the status of the patient, that is, his user-condition parameters.
  • the physical blocking detection can be performed considering a spectral power analysis method.
  • the power spectrum approximately up to the dominant frequency corresponds to the power of the oscillations that happen during voluntary movements like cyclic movement of walking and the power above the dominant frequency corresponds to oscillations that happen in non voluntary movements.
  • This method again divides the movement signal into overlapping or not- overlapping time frames and compares both spectrum regions above and below the dominant frequency of each time frame. If the non voluntary movements become dominant, motor blocking is detected.
  • the threshold values are dependant on the user-condition, and are determined during the user-condition identification
  • the system Whenever motor blocking is detected the system will record all the relevant data into the memory to study the physical blocking and the output signal may include a cue generation in order to overcome the physical blocking.
  • the disclosed method provides the means to improve the known physical blocking detectors.
  • the method of the invention works continuously on the individual, providing a cue only when it is necessary, without the need of any action from the individual or user.
  • the method recalculates the user-condition parameters. This adjustment is strictly necessary to be able to detect the physical blocking episodes.
  • Figure 1 shows a representation of the method claimed.
  • Figure 2 shows a general block diagram of a device that performs the claimed method.
  • Figure 3 shows a body of an individual with three possible locations for the sensors. PREFERRED EMBODIMENT OF THE INVENTION
  • the following invention to overcome movement blocking is based on measuring static and dynamic accelerations and rotational speeds of specific body parts.
  • the data from accelerometers and gyroscopes provide direct information about the body motion which are used by the processor to detect the blocking of movement.
  • the sensor module comprising a plurality of sensors (1 ), is in charge of sensing the body movements.
  • MEMS accelerometers such as the ADXL 330 of Analog Devices can measure the static acceleration of gravity in tilt-sensing applications, as well as dynamic accelerations resulting from motion, shock, or vibration. Its size, 4 mm * 4 mm x 1.45 mm, and power consumption, around 1 mW, makes it easily wearable and mountable on the body.
  • Gyroscopes like ADXRS of Analog Devices are angular rate sensors that generate an angular rate proportional voltage signal. The size, 7.5 mm x 2.5 mm x 2 mm, and power consumption, 3OmW, also make them easily portable.
  • Each kinematic sensor module comprises a combination of these two types of sensors (1 ). The acquired data are transmitted to the central unit (2).
  • the central unit (2) is a wearable computer that acquires and processes and generates outputs, which are sent to a display, memory (6) and/or the stimulation module (9).
  • the signal processing module (3) has two blocks or modules: the signal digitalization and filtering unit (4) and the signal conversion unit (5).
  • the signal digitalization and filtering unit (4) acquires and filters the sensor data. The signal is first sampled and converted into a digital signal and then is filtered to remove random noise of the signal. The resulting data represents noise filtered digital data.
  • the signal digitalization and filtering unit (4) provides the filtered digital data to the signal conversion unit (5).
  • the block diagram of the signal conversion module is designed to convert the sensor data into acceleration and to provide to the blocking detection unit (8) and the user-condition identification unit (7) the needed converted and processed values. Therefore, the signal is offset corrected and gain compensated according to the data that the manufacturers provide in technical documentation. As result signal is converted into acceleration values in case of an accererometer as input signal generator and rotational speed in case of a gyroscop as input signal generator. Finally the data is converted into position, velocity, acceleration and jerk data.
  • the signal processing module (3) provides this information to the user- condition identification unit (7) and the blocking detection unit (8).
  • the user-condition identification unit (7) performs the movement signal stability test to see if the body is quiet. Therefore, the derivative of the jerk calculated by the signal conversion module (5) is used. If the jerk of the movement signal remains below a threshold value, there are no jerk peaks, for longer than a determined period of time, i.e. 4 s, the signals are considered stable and the body is quiet. If it is so, the user-condition identification unit (7) analyses the stable movement signals obtained during the period without jerk events and stores the user-condition parameters in memory (7).
  • the blocking detection (8) is continuously running whenever the user- condition parameters are not being updated.
  • This unit uses the user- condition parameters in memory (6) and digitalized, noise filtered and converted movement data, to analyse them in the temporal and frequency domain, to determine the speed and regularity of the movements of the body. During physical blockings or freezing episodes the movements of the body tend to be faster and rougher than during voluntary motor activity. This behaviour could be explained in terms of frequencies.
  • any change of the speed of the movement towards higher frequencies can be interpreted as possible physical blockings. If a physical blocking is detected, the signals that have been interpreted as physical blocking and the time that it happened is stored in memory (6) in order to have historical data of the physical blocking episodes of the user. An output signal is generated as well for the stimulation module (9).
  • the approaches of blocking detection can be divided into two broad categories: approaches that use frequency domain, like Dominant Frequency Component Analysis, Spectral Power Analysis, Wavelets, and approaches that use time frequency domain, for example, detection of periods.
  • approaches that use frequency domain like Dominant Frequency Component Analysis, Spectral Power Analysis, Wavelets, and approaches that use time frequency domain, for example, detection of periods.
  • the linear time frequency representations such as using Short Time Fourier Transform, STFT, can be used to determine the regularity of the movement.
  • the STFT consists in pre-windowing the acquired signal around a particular time, calculating its Fourier Transform, FT, and repeating that for each time instant. This way the frequency components of the signal are known during time and the spectral density or power spectrum can be easily calculated.
  • the spectral density changes significantly during blocking episodes compared to voluntary movement, and therefore the blocking episode can be accurately detected.
  • Two principal methods the dominant frequency component and signal power spectrum analysis will be explained.
  • the signal is first divided into overlapping or non-overlaping time frames and each time frame transferred into frequency domain.
  • the dominant frequency is the frequency with the highest power and represents the frequency in which the movements are being done.
  • the dominant frequency component increases, e.g. for more than 2 Hz, freezing is detected.
  • This threshold value is obtained from memory (6) and determined by the user-condition identification unit (7) each time that the user-condition parameters are updated.
  • the signal is first divided into overlapping or non-overlaping time frames and each time frame transferred into frequency domain.
  • the spectral centroid of a signal is the midpoint of its spectral density function.
  • the spectral centroid increases, e.g. for more than 2 Hz, freezing is detected.
  • This threshold value is obtained from memory (6) and determined by the user-condition identification unit (7) each time that a the user-condition parameters are updated.
  • the power spectrum approximately up to the dominant frequency corresponds to the power of the oscillations which happen during voluntary movements and the power above the dominant frequency corresponds to oscillations which happen in non voluntary movements.
  • the voluntary movements are smoother than the movements during freezing episodes which means that during freezing episodes, the spectrum power of higher frequencies increases during freezing episodes.
  • This unit examines the changes of the power in each of the regions to detect physical blocking and freezing.
  • the threshold values of the increase of the spectrum power of high frequencies are obtained from memory (6) and determined by the user- condition identification unit (7) each time that a the user-condition parameters are updated.
  • Concerning the time domain approach another possible way to detect blocking of movement can be measuring the step duration.
  • Festination is a tendency to speed up the walking by taking very short steps in parallel with a loss of normal amplitude of the movements. This increases the number of steps in a certain time period and therefore measuring the step duration and step-to-step time can be used to detect freezing of gait.
  • the signals that have been interpreted as physical blocking and the time that it happened is stored in memory (6) in order to have historical data of the physical blocking episodes of the user and an output signal is generated for the stimulation module (9).
  • External cues that provide stimuli benefit motor activity in Parkinson's disease patients and can be used to overcome physical blocking episodes (Suteerawattananon M et a/., "Effects of visual and auditory cues on gait in individuals with Parkinson's disease”. J Neurol Sci. 2004; 219(1-2):63-69).
  • External cues can be applied in the form of visual, auditory, tactile or any other nature, to trigger movements or to provide rhythmic or spatial support to improve the quality of movements.
  • the output generated by the blocking detection unit is used by the stimulation module (9) to generate a cue of stimulus for this purpose.

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Abstract

La présente invention se rapporte à un procédé de détection d'épisodes de blocage physique lors d'une activité d'un individu. Ledit procédé comprend l'étape consistant à acquérir un signal de mouvement d'au moins une partie du corps d'un individu. Les signaux de mouvement peuvent être une accélération statique et dynamique et des vitesses de rotation de ladite ou desdites parties du corps d'un individu. Avec lesdites valeurs, il est possible de calculer une valeur de saccade de l'accélération statique et dynamique mesurée. En considérant qu'un signal de mouvement est stable lorsque la saccade déterminée demeure en dessous d'une valeur de seuil établie pendant une période de temps prédéterminée, on détermine si le signal de mouvement est stable ou pas. Si le signal de mouvement est stable, on détermine que le corps est calme, et les signaux de mouvement stable sont analysés, de manière à obtenir au moins un paramètre de l'état de l'utilisateur. Si le signal de mouvement n'est pas stable, une détection de blocage physique est exécutée.
PCT/EP2007/064613 2007-12-28 2007-12-28 Procédé de détection d'épisodes de blocage physique lors d'une activité d'un individu WO2009083032A1 (fr)

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Cited By (8)

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WO2010150260A1 (fr) * 2009-06-24 2010-12-29 The Medical Research, Infrastructure, And Health Services Fund Of The Tel Aviv Medical Center Détecteur de chute imminente automatisé
CN103021128A (zh) * 2011-09-20 2013-04-03 英业达股份有限公司 老人摔跤警报系统及其方法
WO2013082436A1 (fr) * 2011-12-02 2013-06-06 Fitlinxx, Inc. Surveillance intelligente d'activités
FR3033243A1 (fr) * 2015-03-03 2016-09-09 Sebastien Teissier Dispositif d'assistance destine a une personne atteinte de troubles moteurs d'origine neurologique
WO2016180728A1 (fr) * 2015-05-11 2016-11-17 Koninklijke Philips N.V. Appareil et procédé de détermination d'un état sédentaire d'un sujet
US9700222B2 (en) 2011-12-02 2017-07-11 Lumiradx Uk Ltd Health-monitor patch
US9734304B2 (en) 2011-12-02 2017-08-15 Lumiradx Uk Ltd Versatile sensors with data fusion functionality
CN112057080A (zh) * 2020-08-10 2020-12-11 华中科技大学 一种基于分阶段特征提取的冻结步态检测方法和系统

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WO2008091227A1 (fr) * 2007-01-22 2008-07-31 National University Of Singapore Procédé et système pour une détection de début de chute

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Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010150260A1 (fr) * 2009-06-24 2010-12-29 The Medical Research, Infrastructure, And Health Services Fund Of The Tel Aviv Medical Center Détecteur de chute imminente automatisé
US10548512B2 (en) 2009-06-24 2020-02-04 The Medical Research, Infrastructure and Health Services Fund of the Tel Aviv Medical Center Automated near-fall detector
CN103021128A (zh) * 2011-09-20 2013-04-03 英业达股份有限公司 老人摔跤警报系统及其方法
US9734304B2 (en) 2011-12-02 2017-08-15 Lumiradx Uk Ltd Versatile sensors with data fusion functionality
US10695004B2 (en) 2011-12-02 2020-06-30 LumiraDX UK, Ltd. Activity-dependent multi-mode physiological sensor
US11350880B2 (en) 2011-12-02 2022-06-07 Lumiradx Uk Ltd. Health-monitor patch
US9700222B2 (en) 2011-12-02 2017-07-11 Lumiradx Uk Ltd Health-monitor patch
US9700223B2 (en) 2011-12-02 2017-07-11 Lumiradx Uk Ltd Method for forming a component of a wearable monitor
WO2013082436A1 (fr) * 2011-12-02 2013-06-06 Fitlinxx, Inc. Surveillance intelligente d'activités
US9854986B2 (en) 2011-12-02 2018-01-02 Lumiradx Uk Ltd Health-monitor patch
US10022061B2 (en) 2011-12-02 2018-07-17 Lumiradx Uk Ltd. Health-monitor patch
FR3033243A1 (fr) * 2015-03-03 2016-09-09 Sebastien Teissier Dispositif d'assistance destine a une personne atteinte de troubles moteurs d'origine neurologique
WO2016139428A1 (fr) * 2015-03-03 2016-09-09 Teissier Sébastien Dispositif d'assistance destiné à une personne atteinte de troubles moteurs d'origine neurologique
US10758163B2 (en) 2015-03-03 2020-09-01 Resilient Innovation Assistance device intended for a person suffering motor disorders of neurological origin
US20180125394A1 (en) * 2015-05-11 2018-05-10 Koninklijke Philips N.V. Apparatus and method for determining a sedentary state of a subject
US10980446B2 (en) 2015-05-11 2021-04-20 Koninklijke Philips N.V. Apparatus and method for determining a sedentary state of a subject
WO2016180728A1 (fr) * 2015-05-11 2016-11-17 Koninklijke Philips N.V. Appareil et procédé de détermination d'un état sédentaire d'un sujet
CN112057080A (zh) * 2020-08-10 2020-12-11 华中科技大学 一种基于分阶段特征提取的冻结步态检测方法和系统

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