WO2014043757A1 - Détection de foulée - Google Patents

Détection de foulée Download PDF

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Publication number
WO2014043757A1
WO2014043757A1 PCT/AU2013/001074 AU2013001074W WO2014043757A1 WO 2014043757 A1 WO2014043757 A1 WO 2014043757A1 AU 2013001074 W AU2013001074 W AU 2013001074W WO 2014043757 A1 WO2014043757 A1 WO 2014043757A1
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WIPO (PCT)
Prior art keywords
stride
time
phase
gyroscope
computer
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PCT/AU2013/001074
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English (en)
Inventor
Seh Phing GOH
Tharshan VAITHIANATHAN
Subhash Challa
Ricko Ardero LASE
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National Ict Australia Limited
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Priority claimed from AU2012904124A external-priority patent/AU2012904124A0/en
Application filed by National Ict Australia Limited filed Critical National Ict Australia Limited
Publication of WO2014043757A1 publication Critical patent/WO2014043757A1/fr

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    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • 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
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/002—Monitoring the patient using a local or closed circuit, e.g. in a room or building
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/112—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—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
    • 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
    • A61B5/40—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

Definitions

  • the invention concerns gait analysis, an in particular the detection of a stride in sensor data.
  • aspects of the invention include a computer-implemented method for stride detection from sensor data, software and a stride detection system.
  • gait analysis A systematic study of human walking, called gait analysis is of paramount importance in many applications ranging from monitoring daily activities to measuring the recovery of patients with neuromuscular disorders.
  • GITRite In the context of neuromuscular disorders, commercial gait measurement systems such as the GAITRite and VICON allow physiotherapists to monitor the performance of a patient undergoing a particular medical treatment.
  • the VICON system uses multiple infrared cameras to track reflective markers which are placed on certain anatomical location on the body.
  • the GAITRite is an automated system that measures the temporal and spatial gait parameters accurately via an electronic walkway which contains 13824 pressure sensors to capture the geometry and relative arrangement of each footfall as a function of time.
  • these systems are expensive and can only be used within clinical and controlled laboratory environments. Well-trained personnel are also required to operate the system.
  • MEMS micro-electro-mechanical systems
  • a computer-implemented method for stride detection from time series gyroscope sensor data representing angular velocity of a user's leg while taking the stride comprising:
  • the method uses the same time domain representation of high frequency components to identify the toe-off phase and the heel-strike phase. As a result only a single determining step is performed, as compared to determining different data
  • the extreme value of the time domain representation of high frequency components may be represented as a maximum value and/or spike having a magnitude above an adaptive or normalised threshold.
  • the extreme value that occurs before the first time is at a second time
  • the method further comprising:
  • the extreme value that occurs after the first time is at a third time, and the method further comprising:
  • the step of determining from the gyroscope data high frequency components may be performed using two-level wavelet decomposition.
  • the wavelet decomposition may be performed on the transverse plane.
  • the step of determining from the gyroscope data high frequency components may further comprise squaring the high frequency components.
  • Stride detection may also be based on time series accelerometer sensor data
  • conditional tests to the accelerometer data and the gyroscope data that if met identify a flat-foot phase at a fourth time.
  • the method may further comprise validating the stride detection if the fourth time:
  • the gyroscope data is tri-axial and stride detection is also based on time series
  • accelerometer sensor data representing a user's leg linear acceleration while taking the stride
  • the method may further comprise determining a three dimensional reconstruction of the stride based on the gyroscope data and the identified swing-phase, toe-off-phase and heel-strike phase. This three-dimensional reconstruction facilitates better understanding of the motion of the stride.
  • the initial orientation may be measured at the flat-foot phase. Determining the orientation may comprise determining the initial pitch and roll using accelerometer data, and setting the initial yaw to zero. Subsequent orientations will be updated using the gyroscope data.
  • the orientation may be used to align the accelerometer data with a global reference frame.
  • Determining the orientation may be repeated for each detected stride.
  • the determined times of the toe-off and heel strike events may be estimates but it is an advantage that these estimates are of high quality.
  • Stride detection may also be based on time series accelerometer sensor data representing the user's leg linear acceleration while taking the stride, and the method is repeated to detect a second stride in the time series gyroscope sensor data, and the method further comprising:
  • the accelerometer data may be tri-axial.
  • the method may further comprise determining the length of the detected stride based on the determined velocity.
  • the method may be repeated to detect a third stride in the time series gyroscope data, the method further comprising:
  • determining the velocity of the stride between the flat-foot phase of the second stride and the flat-foot phase of the third stride by initially re-estimating the initial velocity to zero and re-estimating the velocity of the sensor having the gyroscope and the accelerometer.
  • the method may comprise receiving gyroscope data from two or more gyroscopes.
  • the method may comprise receiving accelerometer data from two or more sensors
  • Identifying the swing phase of the stride may be based on an adaptive threshold.
  • software that is computer readable instructions stored on computer readable medium, that when executed by a computer causes the computer to perform the method described above.
  • a stride detection system to detect a stride from time series gyroscope sensor data representing angular velocity of a user's leg while taking the stride, the system comprising a processor programmed to:
  • the system may comprise a sensor having a gyroscope that is attached to the leg of the user taking the stride.
  • the sensor may also comprise an accelerometer.
  • the gyroscope and the accelerometer may be tri-axial.
  • the processor may be remotely located from the sensor.
  • stride detection and stride length can be performed in a low cost and suitably accurate way.
  • Fig. 1 shows an example system for stride detection.
  • Fig. 2 shows an example hardware design of a sensor node.
  • Fig. 3 shows an example hardware design of a central storage (CSN) node.
  • Fig. 4 shows an example hardware design of a central interface (CIN) node.
  • Fig. 5 shows an example software architecture of a transceiver module.
  • Fig. 6 shows an example network topology for the stride detection system.
  • Fig. 7 shows an example of a three dimensional inertial strapdown system where the initial body frame is aligned with the global reference frame.
  • Fig. 8 graphically shows an example global reference frame and body frame in a two dimensional stride detection system.
  • Fig. 9 shows an example representation of yaw, pitch and roll used in orientation.
  • Fig. 10 shows a flowchart showing an example of wavelet decomposition.
  • Fig. 1 1(a) shows example gait phases identified in a typical gyroscope data.
  • Fig. 1 1(b) is a table that shows spatial and temporal parameter definitions.
  • Fig. 2 shows a human leg with a sensor node attached to the shank.
  • Fig. 13 shows an example method for a single sensor system.
  • Fig. 14 shows an example overview method of gait events and stride detection.
  • Fig. 15 shows an example of time domain representation of high frequency components determined from gyroscope data.
  • Fig. 16 is an example flowchart for heel-strike and toe-off detection from gyroscope data and high frequency components of the gyroscope data.
  • Fig. 17 is an example flowchart for stance detection algorithm.
  • Fig. 18 is an example flowchart of gait phase validation.
  • Fig. 1 shows sensor orientation during a flat-foot phase and global reference frame.
  • Fig. 20 graphically shows an example z-axis acceleration measurement of a stationary sensor and the calculated velocity shows the drift resultant from the integration.
  • Fig. 21 shows an example of time domain representation of high frequency components determined from gyroscope data.
  • Fig. 22 shows examples of stance detection from typical y-axis gyroscope data.
  • Fig. 23 shows an example overall output of the stance detection algorithm.
  • Fig. 24 shows an example overall output of gait events and stride detection.
  • Fig. 25 is a table showing example thresholds for gait events detection.
  • Fig. 26 shows an example gyroscope signal and wavelet reconstructed signal for leg swing.
  • Fig. 27 shows an example integration interval of the z-axis acceleration and an example of integration drift of the z-axis velocity.
  • Fig. 28 shows an example reset velocity signal.
  • Fig. 29 shows a flowchart showing an example method for stride detection.
  • Fig. 30 is a table showing example values for thresholds and alternative methods for their calculation.
  • Fig. 31 is a table showing the actual number of stride and those detected using
  • NICTA's IMU algorithm developed on 3 normal subjects and one Parkinsons diseased patient.
  • Fig. 32 shows the stride length comparison between laboratory based gold standard
  • Fig. 33 shows the stride velocity comparison between laboratory based gold standard
  • Fig. 34 shows the stride length comparison between GAITRite and NICTA's IMU stride length algorithm on a Parkinson's disease patient (before and after medication)
  • Fig. 35 shows an example user interface.
  • the application of this example relates to calculating stride length of a medical patient.
  • step is used interchangeably with the term stride throughout this document, where step is taken in reference to movement of the same foot.
  • the medical patient 100 wears two sensors 102 and 104 which are shown in more detail at 102' and 104' respectively.
  • the sensor nodes 102 and 104 acquire
  • magnetic field strength data [00087] at the attached positions of the body In this example only two sensor nodes 102 and 104 are shown but it will be appreciated that more sensors could be used, and on different limbs of the medical patient 100.
  • the sensor provides samples at a user- settable frequency, such as 819.2Hz.
  • This data is then sent to the Central Storage Node (CSN) 108 which is shown in more detail at 108'.
  • CSN Central Storage Node
  • the CSN 108 is worn by the patient 100.
  • the data is aggregated by the CSN 108 and stored on a USB flash drive 108b.
  • the CSN 108 also acts as the sensor network master and coordinates the sensor nodes 102 and 104 on the network 120.
  • the wireless USB dongle 140 or Central Interface Node (CIN) 140 is installed on PC 150 and acts as a bridge allowing the network 120 to interface directly with a PC 150, analogous to a WiFi network card which allows a PC to interact with a WiFi network.
  • the CIN 140 performs the same storage and network coordination functions as the CSN 108. Unlike the CSN 108 however, data received by the CIN 140 from the sensor nodes 102 and 104 are sent directly to the PC 150 rather than CSN 108. This mode of operation allows sensor data to be processed in real time. Due to its overlapping functionality a deployed network 120 uses either a CSN 108 or CIN 140 but not both.
  • the network 100 has two modes of operation:
  • sensor data is sent to the CSN 108 for storage 108b and can be conveniently post-processed at a later time.
  • the offline mode of operation is suitable for long-term monitoring applications where a PC 150 may not be near the patient 100 all the time.
  • data is streamed from the sensors 102 and 104 via a CIN 1 0 to a PC 150 allowing for real-time data acquisition and processing.
  • An inertial measurement unit 202 is an inertial MEMS sensor including a 3 axis accelerometer 212, 3 axis gyroscope 214 and a 3 axis magnetometer 216 that provide measurements of angular rate, linear acceleration and magnetic field strength respectively.
  • the inertial measurement unit includes software configurable sampling rates for example of up to 819.2Hz. Acquired data transferred via SPI interface to the wireless microcontroller 218 and is then wirelessly transmitted to either the CSN 108 or CIN 140 for storage and processing.
  • the central components of the CSN 108 is shown in Fig. 3.
  • the VDIPL module 300 functions as an embedded USB host controller for the CSN 108. It connects to the transmission module, such as a JN5139 microcontroller 302 via the interface 304, such as a UART, allowing the transmission module 302 to store data acquired wirelessly from the sensor nodes 102, 104 on to a flash drive 180b.
  • Converter module 400 allows the transmission module 402 to interface directly with a PC via USB 404.
  • the converter module 400 performs the functions of a protocol translator and level shifter between the output of the transmission module 402 and the USB input 404 of the PC.
  • This communication channel to the transmission module 402 is exposed on the host PC as a configurable virtual serial port allowing programs written on the host PC to communicate with the CIN 140.
  • the CIN 140 is not battery powered, but draws power from the USB port 404 of the host PC.
  • the software architecture running in this example for the JN5139 is layered with multiple protocol stacks running simultaneously. An overview of this layered architecture is shown in Fig. 5.
  • the IEEE 802.15.4 standard is specifically designed for Wireless Personal Area Networks (WPAN) with short transmission distances of under 10m.
  • the Network Layer 502 on the JN5139 runs the JenNet protocol stack. User applications are built upon and interact with the JenNet stack via its Jenie Application Programming Interface.
  • the JenNet stack is a thin protocol stack built upon the IEEE 802.15.4 protocol and extends its capabilities.
  • the user application 504 is developed on top of the JenNet Network Layer 502. In fact, the user applications 504 sometimes bypass the JenNet layer 502 and access the lower level IEEE 802.15.4 stack directly to assist in packet loss issues.
  • FIG. 35 A sample user interface shown on the computer 150 is shown in Fig. 35.
  • This GUI visualises the determined stride length data in a stem plot and shows some output statistics.
  • a user can zoom in or out of the plot, or select some data plot to see the exact value.
  • More graphs and more statistics can be implemented in the plotting and statistics area, such as pie graph which can show the distribution of the stride length (SL) more easily; and percentage of the shorter strides and longer strides.
  • the user e.g. doctors
  • the user can see the status of the patient's walking in the data logging period. If the patient is walking more casually and freely, the medication towards this patient is decent. If significant short strides (more than a normal person walks) are observed from the patient's data, this would indicate that the patient's medication can be improved.
  • Gait analysis systems based on inertial sensors are affected by many different error sources including accelerometer/gyroscope bias, thermo-mechanical white noise, flicker noise, temperature effects and calibration errors. Kinematics relationship that requires the integration of biased and noise affected measurements will result in errors that accumulate over time. Therefore, several algorithms need to be employed to reduce the uncertainty in the measurement of gait parameters.
  • Accelerometers and gyroscopes are the cores of any inertial strapdown system. To obtain positional information from inertial strapdown systems four basic processing stages are required namely
  • the accelerometer will measure the acceleration of the motion as well as the acceleration due to gravity while the gyroscope will measure the angular velocity of the shank.
  • the body frame is aligned with the global reference frame.
  • This global frame defined as the orthogonal axis set that is aligned with the gravity vector (superscript g denotes global frame).
  • g denotes global frame.
  • ⁇ can be calculated by integrating the gyroscope measurements, ⁇ .
  • ⁇ can be used to construct a rotation matrix, R that is used to project the accelerometer measurements onto the global frame.
  • FIG. 9 shows a representation of yaw, pitch and roll orientation.
  • the Euler Theorem states that three rotations are needed to make a coordinate frame coincide with another frame. Each of the rotations will occur about a new coordinate frame after the previous rotation.
  • a particular reference frame is rotated to a new coordinate frame through a sequence of ⁇ rotations. This means that a rotation ⁇ (roll) about x-axis, followed by a rotation ⁇ (pitch) about the new y- axis and followed by a rotation ⁇ (yaw) about the newer z-axis are applied onto the original reference frame.
  • rotations can be expressed mathematically by the equation 5.6.
  • the Euler angles can be obtained by using accelerometer measurement under stationary
  • Quaternions are a four-parameter representation of rotation based on the idea that any
  • coordinate frame can be transformed to another by a single rotation about an Euler axis (axis of rotation). It consists of a set of four parameters in which there are three components of a vector directed along the Euler axis and one scalar quantity, as shown below.
  • ⁇ q ⁇ sin fl
  • q 0 cos 0
  • Quaternions can be used to rotate a vector by using a rotation matrix in equation 5.8.
  • Quaternions can be calculated by using the gyroscope measurements and hence the
  • Wavelet transformation allows the detection of a specified frequency at a specified time.
  • the wavelet theory the Coiflet wavelet, is chosen in the analysis of gait events due to its similarity with the gait events signal.
  • Wavelet decomposition involves splitting a signal into low-frequency components called approximation and high-frequency components called detail for each level of decomposition. The decomposed signal will be down-sampled
  • Fig. 10 summarises the wavelet decomposition algorithm where cA, and cD i are the approximation and detail respectively, and represents the level of decomposition.
  • FF foot-flat
  • HO heel-off
  • TO toe-off
  • SW swing phase
  • heel-strike being the point when initial contact is made with the heel.
  • Fig. 1 1(a) shows the region of these phases by using Y-axis gyroscope signal.
  • the sensor node is attached to the shank of the subject, with the sensor axes shown in Fig. 12. Note that the body initial frame does not align with the global frame.
  • Fig. 13 provides an overview of the single sensor gait analysis algorithm.
  • Fig. 14 The overview of the step "gait events and step detection” is shown in Fig. 14.
  • the gyroscope is used to detect the dynamic gait events while the fusion of accelerometer and gyroscope is used to detect the stance phase.
  • the y-axis gyroscope will be the most sensitive to shank rotation.
  • a typical gyroscope signal of a walking subject is shown in Fig. 15(a) and the method of detecting a stride is shown in Fig. 29.
  • the shank When the subject starts walking from a foot-flat phase, the shank will rotate anti-clockwise about y-axis during which the gyroscope outputs a negative angular velocity. The anti-clockwise rotation will reach a minimum just before the toe-off event, and the gyroscope signal appears to be a sharp minimum peak.
  • the subject's shank will then rotate in the clockwise direction and reach maximum velocity before slowing down to prepare for heel-strike.
  • the heel-strike signal will then appear to be a rather sharp negative minimum peak in the signal.
  • wavelet analysis using Coiflet wavelet is applied onto the gyroscope signal to locate these events in time and frequency domain.
  • Two-level wavelet decomposition is applied onto gyroscope signal shown in Fig. 15(a) to decompose the signal into its approximation and scale components.
  • a signal is then reconstructed in time domain using only the detail to extract the high frequency components, that is the high frequency components are determined to be in both in time and frequency domain 320.
  • a typical reconstructed signal is shown in Fig. 15(b) in which high amplitude spikes can be observed at both the toe-off and heel-strike events.
  • the squaring operation is used to amplify the amplitude for more robust peak detection as shown in Fig, 15(c).
  • a threshold limit is set to detect these spikes and in practice, multiple nearby spikes may have magnitudes above this threshold.
  • a swing-phase in the gyroscope data is identified by exceeding a predetermined or adaptive threshold 322.
  • the adaptive threshold for determining the swing-phase in the gyroscope data can be implemented by iteratively averaging over the peaks within a certain time window of data and reducing the window size. This approach is advantageous over normal averaging in that high values can be retained leading to a more accurate threshold value.
  • a time corresponding to the middle of the identified swing is used as a reference to remove replicate peaks that appear in the reconstructed signal for the same gait event.
  • the maximum peaks detected and identified at 144 in the reconstructed signal are candidate toe-off or heel-strike events in the reconstructed signal.
  • the mid-swing 146 is identified from the on gyroscope data 142.
  • the candidate 144b being the closest preceding spike is taken to represent the heel-strike, and candidate 144c is taken to be the toe-strike.
  • the local minimums 148a and 148b in the gyroscope signal 142 in a time window centred on the times of the spikes 144b and 144c are identified and the timing of these minimums 148a and 148b are taken as the timing of the heel-strike and toe-strike respectively.
  • stance detection is implemented to detect the time when the foot is stationary on the ground. Stance will be detected if the signal from both the accelerometer and gyroscope satisfy the three conditions stated below.
  • Condition 1 Ma nitude of acceleration must be between two thresholds.
  • Ci ⁇ 1 a min ⁇ M ⁇ th a (5.12) CO otherwise
  • Condition 2 Local acceleration must be below a given threshold.
  • Condition 3 Magnitude of the gyroscope must be below a given threshold.
  • a stride is only detected if the multiple phases of the gait have been identified 328.
  • a more detailed algorithm validates the detected gait events by checking if the gait events occur in proper sequence, namely foot-flat, heel- off, toe-off, swing and heel-strike. It also considers whether a stance is detected in the right sequence.
  • the block diagram of Fig. 18 summarises the validation algorithm. Box 180 can be seen as detecting the stride. Prior to this any detected stride is simply a candidate until validated.
  • Stride length is one of the most important gait parameters for measuring the performance of a Parkinson disease patient. It can be defined as the distance between two consecutive foot-flat, heel-strike or toe-off events.
  • the stride length calculation involves a vector integration that determines both the scalar magnitude and direction. Such information can therefore be used in performing 3D reconstructions of the motion when necessary.
  • there is an unknown factor which is the initial velocity condition. This quantity has to be kept minimal so that stride length can be calculated accurately. By choosing the integration interval from foot-flat to the next foot-flat event, this unknown quantity can be assumed to be zero since the foot is almost stationary during foot-flat events.
  • the velocity is reset to zero.
  • orientation computation can be divided into two parts. Firstly, the initial orientation is calculated using the accelerometer measurements and secondly, the relative orientation is calculated using the gyroscope measurements.
  • the initialized quaternion is updated in each sampling interval using gyroscope data allowing the orientation of the sensor to be computed for each sample. This information can then be used to project accelerometer measurements to the global reference frame.
  • the measured acceleration will contain both movement acceleration and acceleration due to gravity.
  • acceleration due to gravity needs to be removed from the measurement.
  • the measurement from the accelerometer must first be projected on to the global axes by using the rotation matrix set out above.
  • the gravity component can then be removed by simply removing the '-lg'component from the z-axis measurement.
  • the procedure is summarised in equation 5.19.
  • sensor measurement will contain offset and bias due to temperature dependent noise or random walk.
  • the projected z-axis acceleration measurement contains a small mean offset of approximately 0.005g while the accelerometer was put stationary. If this acceleration is integrated to calculate the velocity, the velocity will grow almost linearly over time as shown in Fig. 20(b). This effect is called integration drift, which will cause large error in the stride length calculation. However, this integration drift can be removed because the initial and end velocity condition are equal to zero (recall that the initial and end gait events are foot-flat). This is achieved by applying the equation 5.20.
  • the equation 5.20 can be thought as removing a linearly increasing offset from the velocity obtained from the integration of acceleration and reset the velocity to zero at the end of gait cycle.
  • the data filtering method uses detection based on normalized threshold values.
  • equations 5.25-5.27 apply to angle computation during stance phase with
  • thresholds for stance detection and two thresholds for toe-off and heel-strike detection.
  • the spike threshold is chosen such that its value is the middle point between the lowest amplitude of the spikes and zero.
  • the mid-swing point threshold is chosen such that its value is the middle point between the lowest mid-swing value and zero.
  • Fig. 21 shows sample data in use where 220 shows the x-axis gyroscope signal and 222 shows the squared wavelet reconstruction signal that is time aligned with the x-axis gyroscope signal 222.
  • Fig. 22(a) is a y-axis gyroscope signal.
  • Fig. 22(b) is a y-axis gyroscope signal.
  • Fig. 22(b) has shown the magnitude of acceleration with condition CI , When the sensor
  • Example thresholds are 9ms "2 and 1 1ms '2 for error margin.
  • the output of CI will be high if the magnitude of acceleration falls between the intervals.
  • Fig. 25(c) shows the local acceleration variance with condition C2.
  • the window size for calculating the local variance is in this specific example is set to be 21 , determined empirically. When the foot has less dynamics, the local variance of acceleration is very low. An example of a threshold of 1.2 can be set to detect this event. Output of C2 will be high if the local acceleration is less than this threshold.
  • Fig. 25(d) has shown the magnitude of angular velocity with condition C3. An example threshold is 80 degree per second. This means that the output for C3 will be high for the magnitude of angular velocity that is less than this threshold.
  • Fig. 23 has shown the overall output of stance detection algorithm. The combination of three conditions is able to detect the stance phase accurately.
  • Fig. 26 the x-axis gyroscope signal 270 of a person swinging their leg instead of walking is shown.
  • This signal 270 includes alternating maximum and minimum peaks like in a typical gait cycle.
  • the wavelet reconstructed signal 272 does not contain high amplitude spikes; Although swinging of leg exhibits consecutive positive and negative amplitude, using the method described here they are not detected as the heel-strike and toe-off events because the spikes events are not present in the signal. This shows that the wavelet analysis is able to differentiate a swing from a stride.
  • Fig. 27(a) shows z-axis acceleration measurement and Fig. 27(b) shows velocity signal.
  • Fig. 28 shows that the reset velocity signal has the integration drift removed, in which the initial and end velocities are equal to zero.
  • Fig. 31 shows that the gait events detection algorithm has detected 701 strides out of 709 strides taken by the subjects including the Parkinson Disease Patient and there is no false detection. The eight miss-detections are due to the gait events validation algorithm, which removes any suspected false detection. In conclusion, this algorithm has achieved an accuracy of 98.9%.
  • the mean squared error is 3.02cm 2 .
  • the mean squared error is 4.41cm 2 .
  • Fig. 34 shows that the Parkinson's diseased patients stride length increases after medication in comparison to before medication.
  • the reliability and portability offered by this system could serve as a self-monitoring and improvement system for people in different situations such as sports, gaming and rehabilitation.

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur d'un détecteur (102) qui est attaché sur un patient (100) pour récolter des données de capteur de gyroscope de série temporelle. Le procédé comprend la détermination (320) d'une représentation temporelle de composants de haute fréquence des données du gyroscope. À partir de la représentation temporelle des composants de haute fréquence, les phases de décollement des orteils (144c) et de frappe du talon (144b) sont identifiées pour détecter ensuite (328) la foulée. D'autres aspects incluent un système d'ordinateur et un logiciel.
PCT/AU2013/001074 2012-09-20 2013-09-20 Détection de foulée WO2014043757A1 (fr)

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AU2012904124 2012-09-20

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US10625074B2 (en) 2013-01-21 2020-04-21 Cala Health, Inc. Devices and methods for controlling tremor
US9452287B2 (en) 2013-01-21 2016-09-27 Cala Health, Inc. Devices and methods for controlling tremor
US9802041B2 (en) 2014-06-02 2017-10-31 Cala Health, Inc. Systems for peripheral nerve stimulation to treat tremor
US10173060B2 (en) 2014-06-02 2019-01-08 Cala Health, Inc. Methods for peripheral nerve stimulation
US10179238B2 (en) 2014-06-02 2019-01-15 Cala Health, Inc. Systems for peripheral nerve stimulation
US10549093B2 (en) 2014-06-02 2020-02-04 Cala Health, Inc. Method for peripheral nerve stimulation
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KR101702462B1 (ko) 2015-06-16 2017-02-03 전진홍 신체 상태 측정장치 및 측정방법
KR20160148356A (ko) * 2015-06-16 2016-12-26 전진홍 신체 상태 측정장치 및 측정방법
US11596785B2 (en) 2015-09-23 2023-03-07 Cala Health, Inc. Systems and methods for peripheral nerve stimulation in the finger or hand to treat hand tremors
US11918806B2 (en) 2016-01-21 2024-03-05 Cala Health, Inc. Systems, methods and devices for peripheral neuromodulation of the leg
US11344722B2 (en) 2016-01-21 2022-05-31 Cala Health, Inc. Systems, methods and devices for peripheral neuromodulation for treating diseases related to overactive bladder
US10814130B2 (en) 2016-07-08 2020-10-27 Cala Health, Inc. Dry electrodes for transcutaneous nerve stimulation
US11331480B2 (en) 2017-04-03 2022-05-17 Cala Health, Inc. Systems, methods and devices for peripheral neuromodulation for treating diseases related to overactive bladder
US11857778B2 (en) 2018-01-17 2024-01-02 Cala Health, Inc. Systems and methods for treating inflammatory bowel disease through peripheral nerve stimulation
US11213224B2 (en) * 2018-03-19 2022-01-04 Electronic Caregiver, Inc. Consumer application for mobile assessment of functional capacity and falls risk
KR20220123326A (ko) 2018-03-19 2022-09-06 일렉트로닉 케어기버, 아이앤씨. 기능적 능력 및 낙상 위험의 모바일 평가용 소비자 애플리케이션
JP2022153362A (ja) * 2018-03-19 2022-10-12 エレクトロニック ケアギヴァー，インコーポレイテッド 機能的能力及び転倒リスクのモバイル評価のためのコンシューマー向けアプリケーション
JP7375120B2 (ja) 2018-03-19 2023-11-07 エレクトロニック ケアギヴァー，インコーポレイテッド 機能的能力及び転倒リスクのモバイル評価のためのコンシューマー向けアプリケーション
US11923058B2 (en) 2018-04-10 2024-03-05 Electronic Caregiver, Inc. Mobile system for the assessment of consumer medication compliance and provision of mobile caregiving
US11488724B2 (en) 2018-06-18 2022-11-01 Electronic Caregiver, Inc. Systems and methods for a virtual, intelligent and customizable personal medical assistant
US11791050B2 (en) 2019-02-05 2023-10-17 Electronic Caregiver, Inc. 3D environment risks identification utilizing reinforced learning
US11113943B2 (en) 2019-05-07 2021-09-07 Electronic Caregiver, Inc. Systems and methods for predictive environmental fall risk identification
US11890468B1 (en) 2019-10-03 2024-02-06 Cala Health, Inc. Neurostimulation systems with event pattern detection and classification
CN110680335A (zh) * 2019-10-08 2020-01-14 深圳市臻络科技有限公司 步长测量方法及其设备、系统、非易失性计算机存储介质
CN110916984A (zh) * 2019-12-03 2020-03-27 上海交通大学医学院附属第九人民医院 一种预防冻结步态的穿戴设备及其实现方法
WO2022066095A1 (fr) * 2020-09-25 2022-03-31 Walkbeat Ab Système et procédé d'analyse de démarche chez les humains
WO2023095032A1 (fr) * 2021-11-24 2023-06-01 Kinetikos Driven Solutions, S.A. Système et procédé de surveillance non supervisée dans des troubles liés à la mobilité

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