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WO2010112007A2 - Détection de mouvement avec feed-back - Google Patents

Détection de mouvement avec feed-back Download PDF

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Publication number
WO2010112007A2
WO2010112007A2 PCT/DE2010/000368 DE2010000368W WO2010112007A2 WO 2010112007 A2 WO2010112007 A2 WO 2010112007A2 DE 2010000368 W DE2010000368 W DE 2010000368W WO 2010112007 A2 WO2010112007 A2 WO 2010112007A2
Authority
WO
WIPO (PCT)
Prior art keywords
data
living
movement
detection device
sensor
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/DE2010/000368
Other languages
German (de)
English (en)
Other versions
WO2010112007A3 (fr
Inventor
Johannes Rosenmöller
Richard Feichtinger
Jürgen Löschinger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Humotion GmbH
Original Assignee
Humotion GmbH
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 Humotion GmbH filed Critical Humotion GmbH
Publication of WO2010112007A2 publication Critical patent/WO2010112007A2/fr
Publication of WO2010112007A3 publication Critical patent/WO2010112007A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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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
    • A61B5/1116—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
    • A61B5/1118—Determining activity level
    • 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
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • 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
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/083—Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/486—Biofeedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period

Definitions

  • the invention relates to a method for detecting movements in living beings.
  • the detection of movements is at
  • the known methods have in common that they cause very high costs and are labor-intensive, and that they can only be carried out by experts.
  • Related to the Medical application is also particularly disadvantageous in that the analyzes only possible movements that makes the patient under special conditions, quasi laboratory conditions, namely while he knows one hand observed white and on the other hand on a non-ordinary underground such as the treadmill mentioned as an example moves.
  • a so-called admissibility corridor is specified, which determines, for example, for the various joints of the human being certain angular movements in one or more directions which should preferably not be exceeded or fallen short of. It can not be ruled out that the patient in everyday life due to fatigue in the course of the day, or depending on its daily shape, or depending on the length of a distance already traveled, the movements that should be in the admissibility corridor, performs with very different accuracy and the admissibility
  • the invention has for its object to provide a method which allows a low-cost human movement analysis in humans. Furthermore, the invention has for its object to provide a device which enables the implementation of the method.
  • the proposed method makes it possible to judge the movements of people without the presence of specialized personnel. This can be done by analyzing the movement data the person or, if appropriate, an object moved by him. In the case of disadvantageous movements that lie outside of a previously defined admissibility corridor, this is signaled to the human being, so that the same training effect as in the aforementioned physiognomic exercises can be achieved.
  • bio-feedback refers to the fact that a physiological parameter that is not or not sufficiently perceived by the living being is converted into a sensible signal, so that the living being can react consciously or unconsciously to it ,
  • the desired improvement in the holding and the way in which it is moved is preferably a so-called "biofeedback".
  • biofeedback Method of use.
  • the person is mainly informed of an improvement or deterioration of the desired posture / movement.
  • the body has the opportunity to unconsciously perform the improvement.
  • the exact knowledge of how to intervene in the complex sequence of movements is not absolutely necessary.
  • Motion-sensitive sensors are used as transducers, so that even without external reference points, the mobile detection unit can be used immediately anywhere (indoor and outdoor) without setup or calibration.
  • the detection devices are self-sufficient in terms of power supply, so that no power connection during the detection of the movements is needed.
  • the person - for example, the mentioned patient after a hip operation - can thus move freely according to his normal everyday life, so that movements can be detected as putting on a seat, climbing stairs, or running on different types of ground.
  • the proposed method is economically advantageous: firstly, for the detection, evaluation and possible correction of the movements no specialist staff must be present, secondly, the detection, evaluation and possible correction of movements is not limited to short periods in comparatively large distances, for example one hour per Day or week. Rather, training, as otherwise achievable by the physiotherapeutic exercises, can take place throughout the day by means of the mobile detection unit, ie, constantly when the person is moving, so that training progress can be achieved much more quickly and it can be avoided the human adopts unfavorable movements unconsciously. On the one hand, regular visits to specialist staff can serve to check the permissibility corridor and, if necessary, to redefine it, and on the other hand to document the progress of the training and to end a therapy after a corresponding success.
  • the handling of the devices is simple and without expertise to perform, since only the detection device to be turned on and must be carried by the living being.
  • An evaluation of the data takes place fully automatically in the mobile registration unit.
  • the computation time required for the analysis can be advantageously kept as short as possible, that only certain of the acquired data are automatically analyzed, for example, only the movement data, while actually also other data are recorded such.
  • human physiological data such as heart rate, skin moisture and the like, or environmental data such as humidity, air pressure or air temperature. All recorded data can be stored in a buffer and later transferred from the cache to a stationary computer at the mentioned specialist personnel, so that subsequently more complex evaluations of the recorded data can take place. This can be done either on the stationary computer itself, or from this connection can - for example, via the Internet - are built to a central server running on the expert program, and from which analysis results and, where appropriate, therapy proposals can be transmitted to the stationary computer.
  • the data can be transferred to a worldwide server via the Internet, with subsequent analysis. Subsequently, the results are transferred back to the above-mentioned transmission device, forwarded to another user, for example to another server.
  • This other server may, for example, be operated by a medical center or the like. Long-term storage of the raw data and / or the analysis data on the server can advantageously be provided in order to be able to re-evaluate the stored raw data later, if appropriate from other analysis aspects, or to be able to make the stored results available again to the same or to another user ,
  • a general statistical analysis such as For example, number of steps, (non-) regularity of steps, (A) symmetry of steps, and their time profile can be automatically generated based on the data stored on the server.
  • the maximum as well as the minimum expression of different movements can be automatically created as an analysis result.
  • the results of the analysis can be used to create therapeutic measures.
  • the analysis results can be transmitted to a local user and / or to an external service provider, to a hospital operator or therapy organizer.
  • a "training concept" similar to that used in sport can also be used, for example in the context of rehabilitation measures, in which patients with limited physical flexibility perform exercises to improve their physical mobility be judged whether, for example, a range of movement of a first Due to the rehabilitation measures, the affected joint has become larger, but erroneous movement sequences can also be detected, so that, for example, protective postures and gentle movements that a patient has accustomed to, for example due to pain over an extended period of time, can be detected and corrected.
  • Another advantage of the proposed acquisition of biometric data is that not limited to the short period, the movements of patients can be analyzed, namely, if this patient with a doctor, physiotherapist or the like together, but that the data collection rather over a longer period can be done, for example, throughout the day, or over several days. Thus, for example, during a walk o. The like.
  • the movement of the patient can be detected, so that at a later date, the data of
  • Qualified personnel can be analyzed and used for the appropriate treatment can be drawn for the treatment of the patient.
  • the analysis of the data may possibly also by an expert, so-called “expert system” in
  • Evaluation has previously been done by a human or by the mentioned automated expert system, and this caregiver, so the doctor or physiotherapist, can give this patient suggestions in conversation with the patient or submit appropriate further therapy suggestions or terminate the therapy after a correspondingly advantageous course of treatment.
  • a particularly advantageous embodiment of the present invention it is provided to transmit signals in the form of acknowledgment signals or warning signals during certain movements which the analyzed person performs, for example a patient.
  • Confirmation signals can be emitted, for example, if, during gymnastic exercises, for example, limbs have been moved over a wider range, for example beyond a further angle, than hitherto. In this way, the training or rehabilitating person is immediately signaled a training success.
  • warning signals can be given off if incorrect movements are carried out, for example if a patient is still performing a movement sequence he has appropriated as a restraint or as a gentle movement.
  • a certain "movement corridor” can be defined and programmed into the movement detection device, so that the corresponding warning signal is emitted when leaving this movement corridor
  • Such programming represents the mentioned contentual data transmission in the direction of the motion detection device.
  • a three-dimensional (3-D) representation of the recorded and cached movements is possible, so that the person can vividly show differences of permissible and impermissible movements and be explained by the qualified personnel.
  • the recording of the movement profiles by means of a small, light detection device that is worn by the living being or integrated into the clothing and / or in a man-held object.
  • Gyroscopes are Gyroscopes.
  • Other movement-relevant data can be detected by means of air pressure sensors, temperature sensors and / or physiological sensors, such as, for example, As pulse, respiratory rate, skin moisture, breathing air or oxygen content in the blood.
  • the acquisition device can compress the data. This speeds up the later data transmission to the stationary computer and also makes it possible to dimension the buffer in the mobile detection unit as small as possible.
  • a transfer of all data can advantageously be done automatically as soon as a connection to the stationary computer is established.
  • This connection can be made by connecting the detection device to a transmission device by means of a plug connection, for example by means of an intermediate cable or by virtue of the detection device having a USB plug.
  • the transmission device may be a PC with a USB port, or a special device that can be connected to multiple detection devices simultaneously, so that several detection devices of a human, namely, which are intended for multiple limbs of man, are connectable.
  • the person does not carry the detection device directly on the body.
  • the detection device In order to enable the most accurate detection of the movement of humans, can be advantageously provided to the movement of the
  • a mobile Do not interfere with people that motion-related data via a mobile, integrated into a piece of clothing or game device detection device with high temporal and spatial resolution and stored therein.
  • only a single measuring device is used, so that, for example, speed, acceleration and trajectory of the movement can be detected.
  • a person carries several measuring rates, so that the posture of the person and the movements of several of his limbs can be detected.
  • the acquisition device advantageously has sensors, a processor with its own firmware, a NandFlash as a buffer for the data, a real-time module with date recording, a power management system, a charger for a built-in battery and an interface for data transmission.
  • the charger and the interface can be provided together, for example by means of USB
  • the transmission device already mentioned above can advantageously be configured in such a way that it enables the simultaneous connection of a plurality of detection devices.
  • the multiple detection devices can also be synchronized before the start of the automatic motion detection, the multiple detection devices, so that the internal real-time modules from a common start time based provide motion data , And so that due to this synchronized data acquisition, the individual movements later allow a precise analysis of complex movement patterns, such. B. movements of the trunk, head and limbs of a human can be accurately detected by the synchronization of the detection devices and analyzed together and possibly readjusted.
  • the device can be charged both by the specialist personnel, eg by connection to the transmission device or to the stationary computer, as well as at home.
  • the firmware automatically detects different hardware configurations and different versions. Up-to-date updates and upgrades can be loaded either automatically or manually with a USB connection through an integrated bootloader.
  • the type of recording, individual channel frequencies, recording densities, etc. are adjustable before use, for example, controlled by the stationary computer or individually for each individual recording device.
  • Three-dimensional (3-D) acceleration sensors, 3-D magneto-sensitive sensors or 3-D gyroscopes are advantageously used as sensors, as well as FSR pressure sensors, air pressure sensors, temperature sensors and physiological sensors (for recording pulse, breath, skin moisture, respiratory air, Oxygen content in the blood, and / or myography).
  • FSR pressure sensors for recording pulse, breath, skin moisture, respiratory air, Oxygen content in the blood, and / or myography.
  • 3-D acceleration sensors and 3-D magneto-sensitive sensors are used. With these sensors it is possible to record a motion profile.
  • the advantage is that the system can be used immediately for both indoor and outdoor applications because it requires no further infrastructure in the human environment.
  • the mobile detection unit is a self-sufficient device that manages without information from the outside, such. B. of sensors that can also be configured as a camera. All position and movement data required for the analysis can be recorded by the device itself.
  • the mobile detection unit can consist of several spatially separated, for example, distributed to humans attached components that interact functionally.
  • An electrical power supply can from a central energy storage z. Wired to the individual NEN components be provided.
  • a data transfer between the individual components can, for. B. using short-range radio such as the known Bluetooth radio standard.
  • the movement detection device can advantageously be very compact, so that it easily in clothes, shoes, protective equipment such. B. helmets or gloves, etc. can be integrated. Restrictions on installation, eg. B. because of an antenna to be considered, does not exist. In contrast to known devices that work with bending sensors, the sensors of a proposed detection device need not be mounted both above and below a joint.
  • the system can easily be adapted by the manufacturer to the required resolution by producing appropriately equipped detection devices.
  • the user side in an expert mode the activation or deactivation of individual sensors may be provided so that the amount of detected data can be adapted to the respective requirement and kept optimally low.
  • Attachment of the sensors on the body can not be ruled out in practice. To compensate for this, it may be advantageous to convert the sensor data system into a body coordinate system. For this purpose, statistical methods can be used. These are based for example on the
  • State estimator such as Kalman filters or particle filters, offset against each other to a position and position information.
  • boundary conditions can be considered such. As the location of attachment to the body / object and known body characteristics of the people / objects. These boundary conditions can also be extracted by statistical calculations from existing data records.
  • the detection device is additionally equipped with a GPS receiver for adjusting the sensor data.
  • the combination of 3-D sensor data and GPS position data is preferably also via a stochastic state estimator, e.g. Particulate filter. So z. For example, an average human step length can be calculated
  • All measurement data are stored together with time and date information in the internal buffer of the acquisition device.
  • This time and date information comes from a realtime module that is integrated in the acquisition device.
  • the synchronization and synchronization with other recording devices can advantageously be carried out automatically each time a connection to a PC with Internet access is made, for example by means of atomic-time-specific time data provided in a manner known per se by time servers and by means of which the stationary one Computer is automatically synchronized automatically.
  • Each device additionally has a unique identification (ID number) assigned to a user. This allows the measurements of any number of devices to be synchronized with high precision in order to enable analyzes across them.
  • the detection device has no visible controls such. B. switch or button.
  • the recording of the movement data starts as soon as a certain, adjustable activity is exceeded.
  • the activity can be monitored by the acceleration sensor, which wakes the processor from a so-called sleep mode, or the
  • Recording can be started by certain movements, the z. B. by finger snapping or knocking against the housing as short-term and sudden accelerations are detected.
  • the detection device is activated by means of a capacitive switch.
  • the housing itself can serve as a capacitive switch.
  • the detection device has a magneto-sensitive sensor, by means of which information for synchronization and / or to start the measurement are transmitted.
  • This z. B. a battery-powered electromagnetic field emitting device used to start the detection devices.
  • Several acquisition devices can be started at the same time.
  • By means of coding of the magnetic signal certain start parameters can also be transmitted.
  • Status messages such. B. state of charge, still available data and / or electrical storage capacity, or triggering number of measurements by certain movements and visually and / or acoustically display with only a few display elements. So z. B. depending on the position in which Housing is on the table, different information is output.
  • a shutdown does not take place by certain movements, impacts or the like but rather automatically, namely only when, for example, the above-mentioned monitored activity for a predetermined period has remained below a certain threshold.
  • the sensor unit has a detection of whether and where it is associated with the garments.
  • certain markings are attached to the garments as a signal generator, the one of
  • Apparent sensor element of the detection unit capacitive, optical, magnetic, via a Radio Frequency Identification Tag (RFID system) or mechanically detected by direct or indirect contact indirectly.
  • RFID system Radio Frequency Identification Tag
  • Clothing of humans is housed, thus a so-called position signal is generated.
  • the recorded data can be automatically assigned to specific parts of the human body, while at the same time ensuring easy human handling.
  • similar and similar-looking detection devices can be produced economically in large numbers, and the human being does not have to pay attention to which detection device is to be attached at which position of the clothing for detecting certain movements.
  • BAN Body Area Network
  • the detection device is designed with a nonspecific shape narrow and elongated so that it can be accommodated anywhere as a separate element close to the body, for example, can be inserted into a stocking.
  • this nonspecific shaping without being specially adapted to a specific receptacle or mount, it can be arranged almost anywhere in textile pockets of garments, or in cavities of garment protective elements, or in cavities of objects carried by man, so that cost-effective detection devices can be produced in large numbers and can also be used inexpensively by the user for various applications.
  • the detection device is arranged as an integrable element in a special protective part of the clothing.
  • a special protective part of the clothing is advantageously provided.
  • the device advantageously has a plastic or metal housing, which in a special
  • the detection device is inseparably connected to such a protective element.
  • the protective element itself can form the housing of the detection device and only have at least one connection element which makes possible the data transmission and charging of the energy storage device of the detection device.
  • This can be z. B. be effected by the electronics of the detection device with the material of the z. B. plastic protection element is encapsulated.
  • the battery, z. As a Li-polymer battery, additionally protected by a special reinforcement of plastic or metal.
  • the detection device meets the requirements of protection class IP65, that is protected accordingly against dust and moisture.
  • the elongate detection device is additionally protected by a soft, damping material.
  • At least one light-emitting diode can be provided as a display, which is also visible in the ready state from the outside. It can be provided that the light-emitting diode is still visible from the outside even if the detection device is installed in a textile garment. It can, for example, signal the state of charge of the battery or give information about the recorded data.
  • the data transmission and the charging of the battery are each advantageously via a USB interface, wherein particularly advantageously the data transmission can also take place during the charging process.
  • the USB connection can either be in the so-called built-in, ready-to-operate state or in the removed state, when the detection device is removed from the clothing or from the sports equipment.
  • the USB port is only for charging the battery, and the data transfer to the cache is wireless, for example via a cellular network such as GPRS, UMTS, 3G, 3.5G, or 4G, or by other wireless standards such as wireless, Bluetooth, Zigbee or the like, wherein advantageously as widely used radio standard is used, so that a detection device can be used practically anywhere in the world.
  • the raw data is compressed to reduce the amount of data prior to transmission and storage.
  • a lossless compression is preferably used which achieves an average compression rate of at least the factor 5, in a more preferred embodiment at least the factor 7, in a most preferred embodiment at least the factor 9. If necessary, it can be provided that the data is further compressed by lossy compression.
  • the compression can be done either before transfer from the acquisition device to the buffer, or before transfer from the buffer to the stationary computer. If the compression takes place before the transmission from the detection device to an external intermediate memory, or if the detection device itself has the intermediate memory, the compression can take place in particular directly within the detection device, so that the storage capacity required there for storing the measurement data must be as small as possible can.
  • the battery-powered detection device is charged when it is connected to the transmission device or the stationary computer.
  • an analysis of the acquired movement data is performed in order to inform the human on the basis of the emitted signals to adverse movements.
  • An analysis that takes into account more or other measurement data in comparison, takes place in the shortest possible time automatically on the stationary computer or on the associated central server. In doing so, earlier measurement data and analysis results can also be taken into account.
  • the mean value and the standard deviation are analyzed over a specific time window and compared for conformity with given patterns or subjected to cluster analysis.
  • factors derived from the raw data such as variance, energy or entropy, correlation between different axes or FFT coefficients, peaks in the raw data, or wavelet coefficients may also be used.
  • the length of the time window is usually in the seconds range or in the near subsecond range and is preferably variable.
  • the raw data is previously decomposed into DC (DC) and AC (AC) components via filters.
  • DC DC
  • AC AC
  • filters For the recognition z. B. a hierarchical classification scheme can be used.
  • the raw data Storage allows a coherent overall view of the history of a person and thus also long-term analysis of his fitness state.
  • the data are also suitable for anonymized analyzes of the fitness or health status of specific population groups.
  • the present proposal covers general medical and rehabilitation applications. It allows you to map the following four basic applications:
  • the procedure comprises 3 different subtasks: the determination of the general fall risk (diagnosis and
  • the general fall risk is usually used to take general and longer-term measures, e.g. Structural measures, change of dwelling, support by a walking aid or the like, or it can be responded to by means of a behavioral change, eg. For example, "it may be better not to leave the house or apartment.”
  • the procedure can also be used in neurological conditions such as Parkinson's, Huntington's, and Cerebellar dysfunctions
  • the calculation of the acute fall risk has 2 tasks: 1. Increase the pre-warning time. This gives the wearer the opportunity to sit down, lie down or hold on time.
  • biomechanical z As sudden persistence, solidification, fluctuations in standing z. B. by dizziness and the like.
  • the body movement is detected by the sensors and calculated together with the physiological data.
  • the output for the risk profile is made on the PC in the form of diagrams, texts, etc.
  • the risk is displayed on the device itself, e.g. by number / color / flashing frequency of 1 or more LEDs.
  • the information of at least one sensor which is attached to at least one body position evaluated.
  • the sensors are at least one acceleration sensor, one or more magnetosensors, one or more gyroscopes, one or more air pressure sensors, or a combination thereof.
  • physiological data are recorded.
  • sensors are attached to more than one location on the body.
  • the sensors can be integrated in orthoses, or be designed outside of it as a separate unit.
  • the battery can advantageously be charged by movement, body temperature, light, etc., regardless of one
  • the sensor can be an inertial system for measuring 3D orientation / rotation.
  • the buffer can be designed as a measuring memory for the data accumulated over days / weeks.
  • a sensor unit may consist of individual sensors for detection of 3D acceleration, 3D gyroscope, 3D magneto and extended by air pressure, temperature, FSR, strain gauges, or sensors for physiological factors. GPS and GPRS and MP3 can be integrated into the sensor unit.
  • z. B. may be provided for differential measurement, z. B. to detect height movements when the person z. B. sets or lies down, or if he falls.
  • the sensor unit For acquiring physiological data, the sensor unit can be designed in the form of a keyword as follows:
  • the isolated processes themselves may already be known. These are measurements in the laboratory under knew conditions and conditions, eg. B. 5 walking steps in the straight corridor or measurements on the treadmill o. ⁇ .
  • the measured data are recorded under normal everyday situations. That can
  • Pattern analysis recognition of specific patterns in the sensor coordinates.
  • Movement direction of the COM by gyro or by magnetosensor is a direction of the COM by gyro or by magnetosensor.
  • FIG. 2 the time interval of the heel attachment points of 26 consecutive slow steps, at a measurement frequency of 100 Hz, so that 70 data points correspond to 0.7 seconds
  • FIG. 3 a shows an autocorrelation of the vertical acceleration signal of all 26 steps from FIG. 2, FIG.
  • FIG. 3 a in the form of the first six peaks, wherein the even-numbered peaks are comparisons of the side with the 1st, 2nd and 3rd following double steps of the same side of the human (ipsilateral), and FIG the odd-numbered peaks are comparisons of one page with the immediately following single step of the other side and the next and following pages of the other side of the human (contralateral),
  • Fig. 4 the first six peaks of the autocorrelation of the vertical acceleration signal after time normalization of all 26 steps 200 values from step start to step start
  • FIG. 5 the first six peaks of the autocorrelation of the antero-posterior acceleration signal
  • FIG. 6 the first six peaks of the autocorrelation of the antero-posterior acceleration signal after time normalization of all 26 steps to 200 values from step start to step start
  • FIG. 7 the first six peaks of the autocorrelation of FIG
  • Fig. 8 the first six peaks of the autocorrelation of
  • Fig. 9 the first six peaks of the autocorrelation of
  • Fig. 10 the first six peaks of the autocorrelation of
  • Fig. 1 which stylized shows three different peak shapes, denoted by a, b and c, of the vertical acceleration signal, which are measured with the footrest on the lower spine and typically occur within a series of measurements of several steps. Obviously, such a peak often contains several local maxima.
  • Peaks are used whose properties relate to each other within the search string, also called relational peak analysis.
  • a peak is a coherent, suprathreshold range of measured values, which is defined by various parameters.
  • the position in the search string is determined by the time sequence of the peaks in the measurement channel. For z. As the timing of one or more maxima / minimum values, the start or end of a peak or the position of the center of gravity. For relative references to each other, qualitative measures such.
  • B the area, width,
  • the temporal succession of events is predetermined by the order within the search string (MaB2,
  • channel_122 is the measurement channel to analyze minSteps is the minimum number of samples that the event must be valid. A 0 indicates that the event does not have to occur at all. maxSteps denotes the maximum number of samples that the event may be valid, a ++ indicates that the length is arbitrary.
  • MaB2 refers to the temporal event maximum of the analysis function Peakfinder B.
  • the number 2 serves as the address for the relative reference.
  • IPeak means that in the defined time period no (!) Extreme value found by the peak finder may occur, ie according to MaB2 the next extreme is MiA4, but any number of samples may be later.
  • MiA4 refers to the temporal event Minimum of the analysis function Peakfinder A. The number 4 serves as the address for the relative reference.
  • [qState4] returns a relative condition within the search string: the area of the peak with the relative address 4, ie MiA4, must be larger (>) than 60% (0.6) of the area of the peak with the relative address 2, ie MaB2 ,
  • Threshold -5 Only data points smaller than -5 are examined thr_slope_left_proz 0.1; so. thr_slope_right_proz 0.1; so. thr_slope_left_abs 20.0; s.o thr_slope_right_abs 20.0; so. thr_slope_ratio 0.4; so.
  • PeakFinder function When the PeakFinder function is called, it is still decided whether it should return all or only the first, the most extreme or the last of the local extrema.
  • the conditions in the peak finder function must be set such that the first local maximum in FIG. 1 b does not reach a validity step, because the thr_slope_right is not reached, and that only the first valid local maximum is returned as a result of the peak analysis.
  • relational peak analysis uses additional peaks in the first and / or second derivative of the acceleration signal within this temporal reference frame.
  • Another application of relational peak analysis is, for example, the detection of foot impact which, together with the footrest, divides the step into stance and swing phase.
  • Another application of relational peak analysis is, for example, the detection of kick-off and jump-in-take, which together determine the flight time and from which the flight altitude can be calculated, an important parameter for the measurement of the speed force.
  • the signals of the motion sensors eg acceleration sensors, gyroscopes and magnetic field sensors
  • a central measuring point eg at the sternum or at the back.
  • the signals are transmitted through the footrest and repulsion of all 4 legs, which occur in different step patterns (walking, trotting, pass, booby, gallop) is a relational peak analysis of great advantage in the precise analysis, such as for detecting movement anomalies (limp, stress, etc.), since - If necessary with a lot of effort motion sensors are required at several extremities to allow a correspondingly accurate analysis.
  • a measurement of a slow walking human became a sequence of 26 contiguous ones
  • Steps detected and extracted as a block The measurement frequency was 100 Hz, so that measurement times were made in 10 ms distance.
  • Fig. 2 shows the sequence of pitches, which can recognize no particularity and is quite normal. Nevertheless, the autocorrelation of the vertical acceleration, shown in Fig. 3a, shows that the height of the peaks drops very rapidly to a value of 0.4. This is because individual steps deviate from the normal time step length and thus the
  • Peaks in the autocorrelation calculation can no longer be brought into line, so the corresponding Kurvenabschnite so can not be superimposed exactly, if they are only moved by a fixed predetermined amount of time.
  • the height of the peaks of the autocorrelation calculation becomes very high sensitive already influenced by small temporal variation in the peak patterns of the signal, in this case the steps.
  • the even-numbered peaks compare with the same side of a double step (ipsilateral), the odd-numbered peaks compare with the other side (contralateral).
  • a measure of the gait asymmetry is the ratio of the height of the first peak to the second peak, that is, how much smaller the contralateral regularity with respect to the ipsilateral regularity of the step pattern is pronounced (Moe).
  • the characteristic need not be timed exactly a known event, eg. B. the beginning of the footrest, correspond, but only at every possible event, such. B. footrest, be precisely timed recognizable. This is for example given by the above-described PeakFinder function by means of moving triplet analysis, which is a
  • Characteristic (first valid local maximum in Fig. 1) detects about 50 ms after the beginning of the footrest.
  • the even-numbered (ipsilateral) peaks 2, 4 and 6 are at approximately the same level of 0.91, so that, with the temporal variability excluded, the curve from double step to double step is 91% similar.
  • the odd-numbered (contralateral) peaks are only slightly lower, so that no significant asymmetry can be seen in this signal.
  • FIG. 5 normal, unnormalized
  • FIG. 6 after time normalization again for the first six peaks.
  • a symmetry can clearly be seen in FIG. 6 in that the odd-numbered peaks, situated between 0.85 and 0.9, are significantly lower than the even-numbered peaks at just 0.95.
  • this fact is obscured by the progressive decrease in the peak height and can only be recognized by the fact that this decrease is not uniform but stepped.
  • FIGS. 7 and 8 show the first six peaks of the autocorrelation of the measurement series of the rotation rates about the vertical axis (in the transverse plane) of the same sequence of steps, again without (FIG.
  • FIG. 9 and 10 show the first six peaks of the autocorrelation of the measurement series of the rate of rotation about the anterior-posterior axis (in the frontal plane) of the same sequence of steps again without (FIG. 9) and with (FIG. 10) temporal normalization.
  • the absolute values of the autocorrelation were used here for better optical visibility, since the odd-numbered peaks are negative, because the rotation rates around the anterior-posterior axis for the left and right steps are in opposite directions.
  • a robust measure of asymmetry of step parameters in signals of acceleration and yaw rate sensors is obtained by temporally segmenting the steps based on a particular step feature. This is preferably done with the ones described in the previous section
  • the method according to the invention has the advantage that it is much less computationally and, above all, memory-intensive, since it is not necessary to calculate the entire autocorrelation curve, but only the value of the peaks themselves, the position of which is known in advance due to the temporal normalization ,
  • This saving in the example shown about a factor of 70, is very important in calculations in small, portable devices, especially if the calculation is to be performed while walking for the purpose of immediate biofeedback.
  • the proposed method takes into account at least one of the following values derived from the sensors: intensity of the sensor signals
  • Low-pass filtered and averaged sensor signal used. Dynamics of the body parts operatively connected to the sensors.
  • the intensity of the sensor signals is used to calculate the intensity of the sensor signals.
  • the position to gravity is used to calculate the position to gravity
  • the air pressure sensor detects a stay near the ground.
  • Fall detection is either only the intensity (impact, unbraked case, immobility), only the position, only the
  • a timely combination of head posture intensity values is used.
  • a fall is given when either a shock, an unbraked fall or a large height difference of a body part with a not upright head posture coincides in a timely manner.
  • an event is considered a fall when either a shock, an unbraked fall or a large height difference of a body part coincides with the non-parallel position of the shoulder strap to the ground in a timely manner.
  • a timely combination of intensity values with the position values "do not sit 1" , “do not lie 1" and “do not stand 1” are used.
  • a fall is given when either a shock, an unbraked fall or a large height difference of a body part does not coincide with any of the described postures or if an unusual posture is detected.
  • a fall event is detected by detecting the 'crawl' behavior or at least one pending attempt 1 event.
  • an alarm signal can be issued wirelessly.
  • a tiered alarm may be issued with varying probability.
  • Biofeedback method for fall prevention is fully integrated in orthosis is attached to the body
  • ⁇ is at another site of action, eg. B. insole, toe, glove, shoulder, socks
  • the biofeedback signal can be given as vibration, e.g. B. with adjustable amplitude
  • the biofeedback signal may be tactile, e.g. B. with pneumatic or hydraulic control.
  • the energy for activation may be gained from body movement itself (eg, a rubber ball may be provided in the shoe, the joint movement actuating a pneumatic or hydraulic pump, or an electric generator may be provided).
  • the tactile stimulus can be generated by using a parenthesis more or less pushes or a mandrel extends more or less, or a protective layer moves more or less back and releases a tactile element (eg a matrix with cover).
  • the biofeedback signal can be given optically, e.g. B. 5 by a arranged in a pair of glasses lighting element such.
  • An LCD display can be mirrored into the glasses through which the person can see through.
  • One or more laser pointers project more or less
  • the biofeedback signal can be given auditory, e.g. B. tones can be transmitted via Bluetooth to a receiver in the ear, z. B. to a conventional hearing aid.
  • the tones can be changed in intensity, frequency or spectrum to transmit different signals.
  • Voice instructions can be given in plain text.
  • the biofeedback signal can be given electrically, e.g.
  • a stimulus current may be delivered via one or more electrodes. Electrode attachment and material may be as required. EMG.

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Abstract

L'invention concerne un procédé de détection de mouvement d'êtres vivants au moyen d'une unité de détection mobile, selon lequel a) l'être vivant porte au moins un appareil de détection de mouvement, le profil de mouvement de l'appareil de détection de mouvement est détecté pendant un laps de temps déterminé, un appareil de détection de mouvement qui comporte un enregistreur de valeurs de mesure conçu comme un capteur sensible au mouvement est utilisé, le capteur détecte des mouvements dans l'espace comme un capteur d'accélération en 3D, un capteur magnétosensible en 3D ou un gyroscope 3D, b) l'être vivant porte une électronique d'évaluation, les données détectées par le capteur sont transmises à l'électronique d'évaluation dans laquelle elles sont automatiquement analysées selon des paramètres prédéfinis, une comparaison est automatiquement effectuée pour déterminer si les résultats d'analyse se situent dans ou en dehors d'un corridor dit d'admissibilité et c) l'être vivant porte un transmetteur de signal, et une impulsion déclenchant un signal est automatiquement transmise de l'électronique d'évaluation au transmetteur de signal quand un résultat d'analyse se situe en dehors du corridor d'admissibilité. L'invention propose qu'un paramètre détecté du déroulement du mouvement se présente comme une courbe dans le temps et qu'un modèle de mouvement soit détecté sur la base du déroulement du mouvement et que des événements survenant dans le cadre de ce modèle de mouvement soient précisément reconnus dans le temps, en ce sens que deux valeurs d'analyse sont chaque fois mises en rapport l'une avec l'autre.
PCT/DE2010/000368 2009-04-01 2010-03-30 Détection de mouvement avec feed-back Ceased WO2010112007A2 (fr)

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