Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
  • A61F4/00—Methods or devices enabling patients or disabled persons to operate an apparatus or a device not forming part of the body 
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
  • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
  • A61F2/50—Prostheses not implantable in the body
  • A61F2/68—Operating or control means
  • A61F2/70—Operating or control means electrical
  • A61F2/72—Bioelectric control, e.g. myoelectric
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
  • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
  • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
  • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
  • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
  • A61F2/50—Prostheses not implantable in the body
  • A61F2/60—Artificial legs or feet or parts thereof
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
  • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
  • A61F2/50—Prostheses not implantable in the body
  • A61F2/68—Operating or control means
  • A61F2/70—Operating or control means electrical
  • A61F2002/704—Operating or control means electrical computer-controlled, e.g. robotic control

Definitions

• the present invention is related to a method for determining an artificial periodic patterned signal.
• the present invention is also related to a system implementing such a method.
• Another aspect of the invention is related to
• Lower limb prosthesis or orthosis comprising such a system.
• leg prostheses have been developed in order to replace the limb that amputees have lost.
• the main objective of these prostheses is to allow their user to walk as naturally as possible .
• Active prostheses solve these problems partially: powered by a battery-operated motor, they move on their own and therefore reduce the fatigue of the amputees while improving their posture.
• EP1848380 and EP1786370 describe sensors placed on the healthy leg of the amputee. By analyzing the motion of the leg with a sophisticated algorithm, the control system can identify the phase of the gait cycle and trigger an actuator to appropriately adjust one or more prosthetic or orthotic joints.
• EP1260201 describes other systems analyzing upper-body motions to trigger and maintain walking patterns.
• the second type of active prostheses is controlled by myoelectric signals recorded at the surface of the skin, just above the muscles. These signals are then used to guide the movement of the artificial limb.
• This type of prosthesis is described for example by Y. Sankai, in article "Leading edge of cybernics: Robot suit hal,” published in SICE-ICASE, 2006. International Joint Conference, 2006.
• the improvement brought by the active prosthetic technology with respect to conventional prostheses is indisputable.
• BCI Computer Interfaces
• EEG signals are known to be very noisy implying a very low Signal-to-Noise Ratio (SNR) and, consequently, a low information transfer rate.
• SNR Signal-to-Noise Ratio
• This low bit-rate leads to the difficulty to send complex commands and the users are rather limited to very high-level commands.
• the consequence of this low quality signal is the slowness and the lack of reliability of some BCI- based applications, as reported by B. Obermaier et Al . in "Information transfer rate in a five-classes brain-computer interface," Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 9, no. 3, pp. 283-288, 2001.
• CPGs Central Pattern Generators
• a CPG is composed of motoneurons linked together that can generate periodic patterns whose frequency is controlled by the brain.
• MLR Mesencephalic Locomotor Region
• PCPG Programmable Central Pattern Generator
• the present invention is related to a method for determining an artificial periodic patterned signal comprising the steps of:
• BCI brain computer interface
• said central pattern generator producing an artificial periodic patterned signal based on said command.
• a BCI should be understood as any interface able to determine the intent of a user without noticeable movements, including for example BCI based on EEG signal (non-invasive BCI), electro cortical signals (invasive BCI) or eye movement detection signals .
• eye movement should be understood in its broadest sense, including eye-related movements such as eye blinking.
• the method of the present invention comprises one or a suitable combination of at least two of the following features:
• the central pattern generator is a programmable central pattern generator
• the method further comprises the step of training the programmable central pattern generator by using a predetermined standard walking pattern, preferably obtained by direct measurement of a real walking pattern;
• the method further comprises the step of determining the frequency and amplitude of the trained PCPG corresponding to a predetermined set of walking speeds ;
• the BCI is based on eye movement measurement signal, the commands being preferably based on eye movement sequence detection;
• the set of signals is corresponding to a set of eye movement sequence, preferably measured by electrooculography
• the method further comprises the step of calibrating the eye movement measurement for determining the angular resolution of the eye movement measurement;
• the determination of the eye movement sequence from eye movement measurement signal comprises the step of band-pass frequency filtering the eye movement measurement signal, the band-pass filtering presenting preferably a pass frequency comprised between 0,05 Hz and 20 Hz;
• the determination of eye movement sequence from eye movement measurement signal comprises the step of determining the derivative of the eye movement measurement signal (optionally filtered) ;
• the determination of eye movement sequence from eye movement measurement signal comprises the step of thresholding the derivative of the eye movement measurement signal
• the artificial periodic patterned signal is related to a limb movement or an electromyographic (EMG) signal corresponding to said limb movement;
• EMG electromyographic
• said set of command comprises the command of accelerating, decelerating and stopping, the resulting speed being preferably continuously adapted by interpolation of frequency and/or amplitude between walking speeds pertaining to the predetermined set of walking speeds ;
• the commands are sent simultaneously to more than one programmable central pattern generators for determining movement of a limb comprising more than one degree of freedom, said programmable central pattern generators being preferably coupled and synchronised by said coupling;
• the method further comprises the step of measuring a feature of the resulting movement for adapting the CPG to external perturbation, said adaptation being performed by a feedback loop;
• the determination of the signal pertaining to said set of signals comprises the determination of an incertitude level, said incertitude level being used to refine the command sent to the programmable central pattern generator ( s ) .
• a second aspect of the invention is related to a computer readable medium having computer readable program code embodied therein for determining a periodic patterned signal, the computer readable code comprising instructions which when executed by a processor execute the method of the invention.
• a third aspect of the invention is related to a system for determining an artificial periodic patterned signal comprising:
• a central pattern generator connected to said BCI, said central pattern generator producing, in use, artificial periodic patterned signal based on said BCI signal .
• the CPG used in the system is either hardware implemented or in the form of a program code on a computer readable medium.
• the CPG is a programmable CPG, in order to easily adapt to experimental gait parameters.
• the BCI is based upon eye movement measurement signal.
• the means for measuring eye movements comprise an electrooculograph, for measuring the resting potential of the retina, the eye movement signal being determined from said resting potential.
• the system of the invention further comprises a high-pass frequency filter able to reduce eye movement signal drifting.
• said high- pass frequency filter is adapted to suppress signal below 0, 05Hz .
• the system of the invention further comprises a low-pass frequency filter able to reduce high frequency eye movement signal noise.
• said low-pass filter has a cutoff frequency of 20 Hz.
• the present invention is also related to lower limb prosthesis or orthosis comprising a system according to the invention and further comprising actuators, said actuators being able to produce prosthesis or orthosis movements based on the artificial periodic patterned signal produced by the programmable central pattern generator.
• said prosthesis or orthosis movement is related to bipedal locomotion.
• the prosthesis or orthosis comprises at least one position and/or pressure sensor used in a feedback loop of the programmable central pattern generator (PCPG) , more particularly for synchronising said PCPG with the other limb movements.
• PCPG programmable central pattern generator
• Fig. 1 represents an example of EOG-based wearable eye tracker used in the invention.
• Fig. 2 represents an example of positioning of the EOG electrodes on user's face.
• Fig. 3 represents the EOG pipeline alowing the determination of the eye movement magnitude on both vertical and horizontal channels.
• Fig.4 represents the accuracy of the obtained eye angle measurement.
• Fig.5 represents the general principle of the
• Fig. 6 represents an example of PCPG output compared to a standard pattern of walk, using 7 oscillators .
• Fig. 7 represents the output of the PCPG of the example with variable speed, with variable amplitude and frequency.
• Fig. 8 represents a foot elevator orthosis used in the example.
• Fig. 9 represents the evolution of the foot pattern frequency (a) and amplitude (b) as a function of waling speed.
• the present invention is related to a system combining the ease of control of human/computer interface (BCI) with the use of artificial central pattern generator (CPG) .
• BCI Several types can be used, as far as they provide the possibility of generating high level commands at high speed and sufficient level of confidence.
• EEG signal non-invasive BCI
• electro cortical signals may be used.
• Such method for determining high-level command by BCI is for example described by J. del R. Millan in "Asynchronous Non-Invasive Brain-Actuated Control of an Intelligent Wheelchair", EMBC 2009, which is incorporated herewith by reference.
• the BCI uses eye movement detection as input signal.
• said eye movement detection is based on EOG measurement.
• the output of the artificial central pattern generator may advantageously be used to control movements related to human locomotion.
• the system of the invention may be included in an active lower limb prosthesis or orthosis comprising actuators, said actuators being controlled by the output of the artificial CPG.
• the artificial central pattern generator is a programmable central pattern generator (PCPG) , so that it may easily be adapted to particular individual, gait or speed.
• PCPG programmable central pattern generator
• the PCPG is continuously adapted to any walking speed by continuously adapting amplitude and/or frequency of the generated periodic movement. This continuous adaptation is obtained by interpolation between measured values of the walking speed.
• the method of the invention uses more than one PCPG, for adapting the periodic pattern to different gaits.
• different gaits correspond to speed changes, one PCPG being used for low speed walking and another PCPG being used for higher speed.
• the method of the invention comprises the step of measuring at least one resulting movement parameter, in order to adapt the periodic pattern with other limb movements.
• This adaptation is advantageously used in a feedback loop.
• the measured feature may be either directly related to the PCPG generated movement, or related to a feature with which the PCPG should be synchronised. In the case of an ankle orthesis for example, the PCPG may be synchronised with hip or knee angle measurement.
• FIG. 1 An example of such portable measurement system 4 is represented in Fig. 1, wherein six dry electrodes 1-2 are fixed by means of flexible supports onto goggles 5.
• four electrodes 1 are disposed above and below each eyes, in order to measure the vertical movements of both eyes, and two electrodes 2 are disposed on both sides of the face, in order to measure lateral displacements.
• an additional electrode could be used between the eyes.
• a light sensor 3 is advantageously added between the eyes for compensating EOG signal artifacts induced by changes in ambient light intensity.
• the signals originating from the different sensors/electrodes are directed by means of conductors 6 (only three are represented on fig.l for the sake of clarity) to an electronic device 7 for treating the different signals.
• said electronic device comprises means for amplifying the signals (pre-amplifier) and a digital signal processing unit (DSP) for real-time EOG signal processing.
• pre-amplifier means for amplifying the signals
• DSP digital signal processing unit
• two accelerometers 8 measuring rotations of the head along two axes are fixed on the goggles 4 for compensating EOG signal artifacts caused by physical activity.
• the relevant rotation axes ACCy and ACCz relative to the user's head are represented on Fig. 2.
• the DSP comprises means for streaming the processed EOG signals to a remote device over communication means such as Bluetooth to drive other systems .
• the electrodes measure the resting potential that is generated by the positive cornea (front of the eye) and negative retina (back of the eye) .
• the dipole rotates as well.
• SNR signal to noise ratio
• Eye movements can be classified as following: fixations, saccades and eye blinks.
• Fixations are the stationary states of the eyes during which gaze is focusing on a particular point on the screen. Saccades are very quick eye movements between two fixations points. The duration of a saccade depends on the angular distance the eyes travel during this movement. For a distance of 20 degrees, the duration is between 10 ms and 100 ms . Eye blinks cause a huge variation in the potential in the vertical electrodes around the eyes. Those movement types, which last between 100 ms and 400 ms, can be used to control an HCI .
• Saccade detection is used to construct the eye-tracker.
• Figure 3 illustrates the pipeline executed for detecting saccades present in both the vertical and horizontal EOG signals:
• angular resolution is defined as the limit angle under which two gaze directions are not distinguishable anymore using the EOG signals. In practice, this value was taken as the accuracy of the system at N centimetres maximum deviation from the target.
• the screen was divided in a 5 by 5 grid, resulting in 25 potential target positions, which were selected randomly. The jump between the centre and the target of each trial was considered correct when the Euclidean distance between the EOG-based estimation point on the screen and the actual point was lower than N centimetres.
• the angular resolution was then obtained by looking at the wanted precision, typically 95% or 99% and by converting centimetres on the screen into angles.
• PCPGs Programmable Central Pattern Generators
• this oscillating system is able to change the frequency and magnitude of any given periodic walking pattern it has learned in a smooth way and is robust to noise and to perturbations.
• supervised learning techniques are used in order to determine the CPG parameters.
• the desired rhythmic pattern that the CPG should produce is known (target pattern) .
• the desired pattern can then be used to define an explicit error function to be minimized.
• Examples of learning techniques include (but are, not limited to) :
• a PCPG is a kind of Fourier series decomposition and is composed of several adaptive oscillators.
• the CPG algorithm is governed by the following equation system:
• oscillators are coupled between each other compared to an origin phase based on the Ri coupling parameters. They are composed of adaptive magnitude coefficient and frequency parameters
• sensors are used to synchronise the CPG with the other limb movements.
• This adaptation have the advantage of coupling the CPG with the natural CPGs of a user in a walking process.
• gait cycles are usually not perfectly identical. This fact and numerous perturbations can induce phase mismatch between the perfectly periodic CPG output and the real walking pattern. If this mismatch is too important, the
• the aim of this phase resetting is to pave the way to allow the orthosis to adapt to the patient as quickly and smoothly as possible aiming at increasing the subject comfort.
• the phase resetting consists in resynchronizing the PCPG state according to special events. Therefore, the PCPG will be phase reset on the HS to allow the system to recover the correct phase in a smooth way at the time of the T 0 .
• Two approaches are available: a hard and a soft phase resetting.
• the hard phase resetting relies on a direct modification of the integrated values: in each oscillator i, Xi and i are put to standard values corresponding to the HS event.
• the main advantage of this approach is the quick phase-locking whereas the disadvantages are a more sensitive reaction to noise in the measurement itself and perturbations due to small variations in gait cycles at constant speeds or and instability of the user because of rapid modifications.
• the actuator is not commanding the system and thus, the latter disadvantage is mitigated.
• the reference oscillator is as follows:
• the present invention have been used in a biologically inspired process to control a lower limb prosthesis 10 by BCI based signal (including EOG) as depicted in Figure 8.
• BCI based signal including EOG
• the experimental process is composed of a high-level command system based on EOG signals and a pattern generation to control this prosthesis (or orthosis) .
• the orthosis 10 of this example is made of several components: a custom-fit plastic shell for the shank 11, and another plastic sheel for the foot 13 a flexure joints 12, a linear actuator 19 fixed to the shank shell 11 by means of fastening means 20, a ball-link transmission 16, a load cell 17 to measure the actuator force, and two force sensors 14,15 installed in the orthosis sole, under the heel and the toes.
• the plastic shells 11,13 were designed using a 3D scan of the right foot and leg of a healthy subject, adding mounting surfaces for the actuator, the flexure joints, and the mechanical transmission 18.
• the actuator includes a position control unit that can be driven by an external analog signal in the range of 0 to 10 V.
• Eye gaze detection can provide the precise direction of eye movements in real time. These movements can thus be labelled as left, right, up or down.
• the Levenshtein distance between two given strings is defined as the number of deletions, insertions and substitutions required to transform one of them into the other one.
• the string is built by the concatenation of each labelled state of the eyes (e.g. the string associated to a left-right movement would be LR) .
• a second interest of EOG signals resides in the high speed of eye movements. The user can thus very quickly activate or deactivate a high-level command generation environment.
• This aims at decreasing the subject attention load to actually command the prosthesis. For example, when the patient wants to change the speed, he enters in this environment by means of a certain sequence of blinks and executes the correct eye sequence to really change the speed .
• the pattern should be adapted in terms of frequency and magnitude, i.e. respectively the stepping frequency and stride-related length between two heel strikes whatever the walking speed.
• Kinematics data were thus recorded with the same subject and apparatus for 10 different speeds, from 1.5 to 6 km/h, by step of 0.5 km/h.
• the present invention discloses method for determining a periodic or quasi-periodic movement based on EOG signal (or high-level BCI) .
• the disclosed method have been shown to be adapted to drive a lower limb prosthesis.
• This method is composed of two main steps. At first, an EOG-based eye tracking system generates high-level commands (faster, slower, stop, ...) on the basis of specific eye movement sequences executed by the user, and then, the determined command is used to determine PCPG parameters such as speed and/or amplitude by the mapping between PCPG parameters and real walk patterns.
• a PCPG After learning average walking patterns (angles of elevation of the different parts of the leg as a function of time) , a PCPG provides an adaptive kinematics output to drive the artificial limb, according to the walking speed desired by the user. Unlike current sophisticated active prostheses, the user's intent is fully taken into account in this case.
• a method to process raw EOG signals in order to detect eye movement was also described.
• a method to determine, for a given subject, the minimum angular resolution achievable with his or her EOG signals is proposed.
• the recognition of eye movements sequences comprises the step of evaluating confidence level in this recognition procedure and said confidence level is integrated in the control system itself. For instance, if the decision to increase the speed is sure at 75 %, 75 % of the speed increase is actually performed.
• the generated PCPG signal may be used by a shaping neural network leading to EMG signals. These signals could be the input of a Functional Electrical Stimulation device (FES) .
• FES Functional Electrical Stimulation device
• This type of device may be useful in case of disabled patient having still their limbs but having nerves dysfunction such as disrupted spinal cord.

Applications Claiming Priority (1)

Publications (1)

Family

ID=44543170

Family Applications (1)

Country Status (2)

Families Citing this family (4)

Family Cites Families (8)

• 2011
  • 2011-02-10 EP EP11704756.3A patent/EP2672931A1/fr not_active Withdrawn
  • 2011-02-10 WO PCT/EP2011/051997 patent/WO2012107096A1/fr active Application Filing

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events