This is a 35 U.S.C. 371 National Stage Patent Application of International Application No. PCT/EP2018/050450, filed Jan. 9, 2018, which claims priority to French application 1770031, filed Jan. 10, 2017, each of which is hereby incorporated by reference in its entirety.
The present invention concerns a device for analysing the regularity and symmetry of a sequence of an individual's gait or running cycles.
Recently, advances in electronics and informatics have led to the development of new sensors which make it possible to measure the gait within a routine clinical consultation (for example using inertial sensors). These sensors give access to signals which need to be summarised for conclusions to be drawn about an individual's gait.
Gait parameters such as speed or cadence are the most summarised form of gait signals, as they reduce the signals to a number. Thus, the manufacturers of tools for measuring steps provide exhaustive reports in the form of lists of parameters expressing the various aspects of an individual's gait, such as symmetry, regularity or swiftness of start.
There may be more than a hundred parameters. They are presented in a suitable way in the form of a table, bar chart or curve.
Nevertheless, the subject of viewing the intermediate data between the uninterpretable raw time signals and the parameters has not been investigated to any great extent.
In order to be calculated, some parameters require thresholds to be defined. Thus, for example, it is defined that an individual has reached an established rhythm when the amplitude of their cycles has exceeded 67% of the amplitude of cycles 5 to 10 (it is no longer considered accurate to refer to the ‘beginning’ at more than five cycles). Some cycles may have limit values (60% or 70% of the amplitude of the remote cycles).
In this way, errors occur in connection with the chosen threshold, which could be avoided by directly considering the raw data.
The parameters average out the gait characteristics over the whole exercise, and because of this they mask the progress of the exercise. For example, to evaluate the regularity of an individual's gait, the standard deviation of the duration of the gait cycles is used. If the cycles are regular overall and one cycle is found to be longer than the others, the standard deviation will be increased. It will not be known that this increase is due to an isolated cycle, or at what point in the walk this occurred. The number of erratic cycles and the time of their occurrence are important analytical information.
The parameters are good indicators of an individual's walking performance. However, they do not provide analytical information on the origin of the change in the gait. For example, the speed of walking is a good overall evaluation, but a speed may be reduced by shorter steps or a reduced cadence. The size of the steps and the cadence can in turn be affected by various factors.
Measurement of walking shows major inter-individual fluctuations due mainly to anatomical and functional variations in each individual, which influence the positioning of the sensors, and to variability of locomotion style in each individual. The parameters are affected by this variability and no reference method exists for eliminating it.
A need exists in the prior art for a method for investigating the regularity and symmetry of a sequence of an individual's gait or running cycles over time.
One object of the invention is to make it possible to take into account the shape of the signal to report on the regularity of the gait or running, while eliminating the variability of the duration or amplitude of the gait or running cycles.
In the invention, a visualisation matrix or table of a gait or running exercise is designed. This visualisation matrix M(i,j) is called a ‘locogram’ and is a Gramian matrix in which xij=xij, with xij being the value in row i and column j in the matrix M(i,j).
The locogram is robust in respect of the type of sensors used (accelerometers, gyroscope, infra-red tracker), as well as in respect of their anatomical sites for the recording of the walk (foot, leg, waist, wrist, head).
Owing to the visualisation matrix according to the invention, the professional who analyses the gait (doctor, physiotherapist, nurse, podiatrist, coach, sports trainer, shoe designer, etc.) can have a global view of the gait exercise, on the basis of which they can do the semiology to get an idea of the quality of the patient's gait and easily spot an exercise with atypical steps. The parameters alone are not enough to get a satisfactory snapshot of the patient. The visualisation matrix is a midway point between the parameters and the raw time signals, and gives the doctor access to the patient's gait data.
The visualisation matrix is based on the division of the gait or run into cycles. The cycles are compared with each other using mathematical ‘distances’ or metrics. Depending on the choice of distance, a particular aspect of the signal will be compared.
To be more precise, the invention provides a device for analysing the regularity and symmetry of a sequence of N cycles of an individual's gait or run over time by means of a visual representation, the cycles being compared with each other, and for determining the presence and number of erratic cycles as well as the number of cycles necessary to establish walking or running rhythms, and at what point these erratic walking cycles and rhythms are reached,
comprising:
where i and j are natural integers ranging from 1 to N, the N gait cycles investigated being in chronological order, according to their order in the gait or running sequence;
The locomotion approach using the visualisation matrix suggests describing a cycle (walking, running, marching on the spot) according to three aspects (cycle duration, amplitude and shape) about which similarity distance calculations will be performed. The amplitude and the shape of the signal are independent aspects.
Advantageously, to describe an individual's steps (walking, running, marching on the spot), it may be useful to design three visualisation matrices F(i,j), A(i,j), D(i,j) using these three distances.
The present invention also concerns a method using the device defined above, comprising, as steps:
Other goals, features and advantages will become clear from the following detailed description with reference to the drawings, which are provided as an illustrative, non-limiting example and in which:
The figures presented in this document are only for the purpose of illustration.
The present invention concerns a device 1 for analysing the regularity and symmetry of a sequence of N gait or running cycles of an individual over time, without calculation of parameters based on the overall consideration of the N gait or running cycles, by comparing each cycle with each of the other cycles.
The term ‘gait or running cycle’ corresponds to the defined phases between two heel strikes of the same foot on the ground during the individual's walk or run which took place at certain moments between the two heel strikes. It is therefore representative of the behaviour of the person's foot during a step.
As is known from the prior art, different phases can be defined as percentages, 0% corresponding to the first heel strike and 100% to the second.
The cycle can be separated into two phases: the stance phase (0 to 60%) in which the foot is in contact with the ground, and the swing phase (60% to 100%) in which the heel is in the air.
This term defines all the gait styles and running styles; for example, marching on the spot must be regarded as part of the category defined by the term ‘gait cycle’.
The device 1 illustrated in
The measurement sensors 2 may be any sensor, such as, for example: an accelerometer, a gyroscope, electromyography, insole pressure sensors or infra-red trackers/video or IR acquisition devices.
They can be placed, for example, on the foot, ankle, waist, wrist or head.
The physical variable measured may be, for example, the magnitude of the acceleration, the magnitude of the non-gravitational acceleration, the speed, the angular velocity, movement, position, etc.
For example, the values emitted by inertial sensors may be linear acceleration or angular velocity along one of the three spatial axes. By means of other sensors, other values can be the position of the foot in space using stereophotogrammetry, or the pressure on the ground using force-sensitive insoles having pressure sensors.
As shown in
In these calculations and when ordering the determined values in the matrix M(i,j), the natural integers for i and j range from 1 to N, and the N gait cycles investigated are in chronological order, for the given foot, according to their order in the gait sequence.
Thus for example,
By design, the matrix also shows the case in which cycle 1 (m[1,1]), cycle 2, etc. are compared with themselves.
The processing (‘compend resampling’) and the separation of the raw data are illustrated in
The similarity coefficient may be: a similarity coefficient of the shape fij of the two signals Ci and Cj (as represented in
A synonym of ‘coefficient’ is ‘algebraic distance’ in the mathematical sense of the term, for example, and a synonym of ‘similarity’ is ‘resemblance’.
The term ‘similarity’ implies the symmetry of the coefficients and therefore that fij=fji, aij=aji and dij=dji.
The similarity coefficient can be regarded as a coefficient of correlation in a certain number of cases in the figures.
Other similarity coefficients can be used depending on the aspect of the shape to be compared between two signals: dynamic time warping (DTW), Spearman's coefficient, Euclidean distances (L1, L2 and L∞).
Thus, with a similarity coefficient which varies within a range [a; b],
the similarity between the signals increasing linearly between a and b.
Advantageously, the processing and calculation unit 3 is set up to:
The device 1 also has display means 4 linked to the processing unit and displaying the matrix M(i,j) for i and j ranging from 1 to N, each similarity coefficient value being represented in the matrix M(i,j) by a graduated visual representation to make it possible for the similarity between the gait or running cycles i and j to be seen with the naked eye.
Advantageously, the similarity coefficients (fij, dij, aij) are chosen:
Advantageously, this visual representation is graduated to represent the value of the similarity coefficient. In other words, there is a correspondence between the scale of similarity coefficient values and that of the colours.
This correspondence may or may not be proportional.
Preferably, each value is represented by a colour on a graduated colour scale to make it possible for the similarity between the two gait or running cycles i and j to be seen with the naked eye (which amounts to investigating the similarity between the two signals Ci and Cj).
Advantageously, the scale used by the display means 4 is chosen without thresholds or is continuous as illustrated in
Here, the scale is [0, 1] for the shape similarity coefficient, the amplitude similarity coefficient and the duration similarity coefficient, permitting quick and easy comparison of the different similarity coefficients with each other.
Each matrix box can have a colour corresponding to the degree of similarity/resemblance of two steps to each other.
A warm colour (red, for example) is close to 1 and expresses a high degree of resemblance (similarity).
A cool colour (blue, for example) is close to 0 and expresses a low degree of resemblance (similarity).
It should be noted that this scale is inverted between
The display means 4 display a visual representation of the matrix of similarity such that:
In a first embodiment illustrated in
In a second embodiment illustrated in
In this case, all the investigated cycles of the right foot, then all the investigated cycles of the left foot (or vice versa) are in chronological order, on the x and y axis of the matrix, as illustrated in
Thus, the similarity matrix:
When the similarity coefficient is a coefficient of shape fij, the processing and calculation unit 3 is set up to:
In other words, to investigate the resemblance of the shapes of the cycles to each other, their difference in amplitude and their difference in duration need to be eliminated.
For example, the normalisation in terms of duration is performed by linear resampling or by DTW, as illustrated in
For example, the normalisation in terms of amplitude is performed by dividing the signal Ci by the standard deviation or by the root mean square.
In a first embodiment, the shape similarity coefficient fij may be Pearson's coefficient, as illustrated in
Other mathematical distances are possible, such as Spearman's correlation distance or the dynamic time warping technique.
If only the shape is of interest, it is important to eliminate the amplitude and the duration. To do so, the signal is re-normalised by resampling the signal in 100 samples (normalisation in terms of duration) and dividing the signal by the standard deviation of the cycle (normalisation in terms of amplitude). The Pearson's correlation distance preserves the time line and strictly compares the shape of the steps. Spearman's correlation distance and dynamic time warping deform the time line and indicate whether a deformation is possible to make the shape of two cycles resemble each other or if the shapes are truly different.
In a second embodiment, the similarity coefficient may be a duration similarity coefficient dij.
In this case, as illustrated in
The duration of the signal Ci is equal to the number of samples, each sample being taken regularly at a given frequency.
In a third embodiment, the similarity coefficient may be an amplitude similarity coefficient.
In this case, as illustrated in
The amplitude Ai and Aj is, for example, the standard deviation or the root mean square.
Once the calculations have been performed to find the values of the similarity coefficients,
the processing and calculation unit 3 is set up to calculate:
In particular:
The present invention also concerns a method illustrated in
The method, illustrated in
where i and j are natural integers ranging from 1 to N, the N gait or running cycles being in chronological order;
The viewing step performed by the operator makes it possible to determine:
The processing and calculation unit 3 can be designed to count the number of cycles defined above, or to display only certain similarity coefficient values among the cycles in the light of predefined threshold values.
Step 1: Data Acquisition
A subject wearing a triaxial accelerometer on the dorsal surface of each foot performs a walking exercise of 10 metres ‘there and back’, start-stop. Each of the sensors records a time signal in three dimensions (along the three axes x, y and z). The signals for the right foot and the left foot are called, respectively, accright and accleft, as represented on the curves of
Step 2: Finding the Beginnings of the Gait Cycles
The times corresponding to the beginnings of the gait cycles are found (manually or automatically) in the signals. These moments, according to the definition by Mariani et al. (2013), correspond to the heel strikes, i.e. the moments when the heel touches the ground. Two τright and τleft sets are defined, which correspond, respectively, to the set of heel strikes for the right foot and the set for the left foot (
Step 3: Removal of Gravity
At the start of the procedure, or in a preliminary step, the subject was asked to remain upright and stationary. The value (constant) of the accelerations during this phase was
As shown on the curves of
The same procedure is repeated for the left foot.
Step 4: Calculation of the Magnitude
As shown on the curve of
and in the same way for the left foot.
This fusion of the three axes of acceleration allows or independence in the position of the sensor, which is a major source of measurement imprecision in measurements by accelerometer.
Step 5: Creation of the Gait Cycles
As shown on the curve of
For
{c1, c2, . . . , cN
Step 6: Calculation of the Metrics
From all these steps, a time signal Ci is determined, comprising a series of points of the physical variable measured as a function of time.
Each time signal Ci has a given shape, amplitude and duration, the series Ci being associated with a given cycle i, as represented in
6.1 Some Preliminary Notes
Given a vector x made up of N samples{x1, x2, . . . , xN}, the following quantities are defined:
Given two vectors x and y, the following is defined:
6.2 Duration Metric
The duration metric is defined according to:
where |cj| is the number of elements of the cycle j.
This is the ratio of the cycle durations, always using the longest cycle as the denominator.
6.3 Amplitude Metric
The amplitude metric is defined according to:
This is the ratio of the standard deviations, always using the largest amplitude as the denominator.
6.4 Shape Metric
At this stage, the cycles do not all have the same length. The length of each cycle is normalised to 100 samples using the ‘resample’ function of Matlab® (‘MATLAB 2014a, The MathWorks, Natick, 2014.’), as shown in
Finally, the shape metric is defined according to:
Step 7: Design of the Visualisation Matrices
In this way, three matrices are obtained for one gait exercise
We have the following form:
The figures presented in this document are for the purpose of illustration. According to the design of the ‘locogram’, the size does not differ between shape, amplitude and duration. The differences in number of squares observed here between
The healthy subject did a total of 38 cycles versus 54 for the Parkinson's patient for the same distance, which shows a pathological gait with small steps (
The patches of uniformity along the diagonal of the ‘locogram’ are signs of quality walking. In fact, this means that all the cycles in this patch of uniformity resemble each other and are regular.
In
In
Moreover, erratic cycles are observed, indicating irregularities that are quite localised in time (freezing, stumbling): cycle 19, right foot, cycles 8 and 9, left foot, for example. Finally, irregularities more spread out in time are also observed, taking the form of heterogeneous patches: cycles 15 to 20, right foot and cycles 15 to 20, left foot, for example. It should be noted that these two heterogeneous patches immediately follow the about-turn. This may indicate difficulty in performing the about-turn and resuming quality walking after it. There are no patches of heterogeneity or erratic cycles to be observed in
The about-turns are also indicators of the quality of locomotion. In
In the amplitude matrix for the healthy subject, the increase followed by decrease in the similarity of the amplitude of acceleration between the cycles is evidence of the establishment of a cruising speed (
In the duration matrix for the control subject, the lighter overall colour than that of the Parkinson's patient is evidence of greater resemblance between the gait cycles in terms of duration (
We calculate the standard deviation (SD) of the duration of the gait cycles (set of gait cycles without the first three cycles, the three cycles before the about-turn, the cycles of the about-turn, the three cycles following the about-turn and the last three cycles of the exercise):
P0=SD|ci|i=established gait
where |ci| is the number of samples of ci.
We calculate the mean of the ‘locogram’ for the established gait:
We calculate the standard deviation of the ‘locogram’ for the established gait:
We calculate the number of hierarchical clusters on the ‘locogram’ obtained using the same stop rule:
Table 1 shows the results comparing the parameters {P1, P2, P3} for two groups of healthy subjects (young and elderly) and patients suffering from Parkinson's disease.
Table 2 shows the results comparing the parameters {P1, P2, P3} with the clinical severity of the disease in Parkinson's patients (evaluated using the UPDRS III score) and the gait quality (evaluated using the speed of walking and P0).
The results show that the ‘locogram’ for patients suffering from Parkinson's disease is significantly more varied in colour than that of the elderly and young healthy subjects for the 3 parameters {P1, P2, P3} obtained from the ‘locogram’ (Table 1).
Furthermore, there is a correlation between the clinical findings (UPDRS III score) and the gait quality assessed by the ‘locogram’ according to the three parameters {P1, P2, P3} obtained from the ‘locogram’ (Table 2).
Finally, there is a correlation between the gait quality evaluated using the parameters of the prior art (speed of walking and P0) and the gait quality assessed by the ‘locogram’ according to the three parameters {P1, P2, P3} (Table 2).
Therefore, for a group of 40 subjects, the ‘locogram’ makes it possible to numerically (and visually) evaluate the gait quality of a patient suffering from Parkinson's disease.
The visualisation matrix displayed in
The visualisation matrix presents these parameters more intuitively than a list would. Furthermore, for the last two parameters, the definition in the literature is based on thresholds that were fixed experimentally. The benefit of a global representation rather than a representation of one parameter is that the errors resulting from an arbitrary threshold can be avoided.
The shape of the visualisation matrix makes it possible answer the following questions:
The visualisation matrix makes it possible to get an idea of the progress of the gait exercise, unlike the parameters, which do not have a time value. The visualisation matrix provides access to the concept of walking rhythm, which is a concept for which we do not have investigation tools apart from the chronological tracking of parameters gait cycle after gait cycle.
Every individual has a particular walking style, which gives rise to a wide inter-individual variability of the parameters. This style is indicated in the signals by a very reproducible signature of one gait cycle after the other in healthy subjects. The parameters are not a suitable tool for describing this signature. The visualisation matrix, with an appropriate choice of distance (see ‘Description of the invention’) compares the shape of the steps and evaluates the resemblance of the signature of one cycles to the other gait cycles. Thus, it evaluates the quality and reproducibility of the gait and eliminates the personal style, as the individual variabilities are cancelled out in the space between two cycles of the same person. The visualisation matrix provides a table of cells with a number between 0 and 1 for each subject. This makes it possible to compare the visualisation matrices with each other in a reliable way.
The visualisation matrix provides access to new gait parameters not seen before, such as:
This makes the visualisation matrix a useful tool for measuring the long walk that will show whether a subject uses two different gait rhythms, which is a useful clinical indicator (onset of pain in osteoarthritis or freezing in Parkinson's disease, for example). Walking is a pseudoperiodic activity which is naturally subdivided into cycles modulated by physiological changes in walking rhythm (i.e. starting, about-turn and stopping) or pathological irregularities in the established gait. In a walking exercise, there are different gait cycles, such as the cycle for starting to walk (the first four cycles), the established gait cycles, the cycles of preparing for the about-turn, the about-turn cycle and the cycle for stopping walking. Based on this property, the visualisation matrix makes it possible to represent all the walking time using the same method. This makes the visualisation matrix a suitable tool for representing walking in an outpatient setting, covering all phases of the walk.
The main application of the visualisation matrix is the visualisation of a walking exercise. The visualisation matrix can be used for any step exercise, including the 10-metre ‘there and back’ exercise described above, the Timed Up and Go test and also the walking treadmill and the ambulatory walk.
The visualisation matrix is suitable for the visualisation of a 10-metre ‘there and back’, stop-start walking exercise measured by means of the magnitude of acceleration of an accelerometer placed on the dorsal surface of the foot, by comparing the gait cycles using Pearson's correlation distance. This procedure is suitable for a routine clinical consultation and permits a summarised representation for direct viewing in the clinical setting.
Number | Date | Country | Kind |
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1770031 | Jan 2017 | FR | national |
Filing Document | Filing Date | Country | Kind |
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PCT/EP2018/050450 | 1/9/2018 | WO | 00 |
Publishing Document | Publishing Date | Country | Kind |
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WO2018/130521 | 7/19/2018 | WO | A |
Number | Name | Date | Kind |
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20190150793 | Barth | May 2019 | A1 |
Entry |
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Number | Date | Country | |
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20190380623 A1 | Dec 2019 | US |