WO2016053444A1 - Système et procédé de réduction des artefacts de mouvements par le biais de l'électromyographie de surface - Google Patents

Système et procédé de réduction des artefacts de mouvements par le biais de l'électromyographie de surface Download PDF

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Publication number
WO2016053444A1
WO2016053444A1 PCT/US2015/040543 US2015040543W WO2016053444A1 WO 2016053444 A1 WO2016053444 A1 WO 2016053444A1 US 2015040543 W US2015040543 W US 2015040543W WO 2016053444 A1 WO2016053444 A1 WO 2016053444A1
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Prior art keywords
signal
raw
semg
physiological
physiological signal
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PCT/US2015/040543
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English (en)
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Laurence Richard OLIVIER
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Lifeq Global Limited
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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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
    • 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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6824—Arm or wrist
    • 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 pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • 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
    • A61B5/1102—Ballistocardiography
    • 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
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/333—Recording apparatus specially adapted therefor
    • 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
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Definitions

  • the present invention relates to the field of non-invasive digital health monitoring and signal processing.
  • a system and method for motion artifact reduction in measured human physiological signals is introduced.
  • the invention comprises a wearable device which may be placed on an area of the body including, but not limited to, the wrist, the forearm or the upper arm and is composed of a plurality of surface electromyography (sEMG) electrodes.
  • the wearable device comprises a senso (s) for measuring physiological signals such as photoplethysmog- raphy (PPG), electrocardiography (ECG) and balistocardiography (BCG) .
  • Motion compensation is achieved in the time and/or frequency domain by using a derivative of the raw sEMG signal and through adaptive filtering. This method is particularly useful in situations when physiological signals recorded by, but not limited to, PPG, ECG and BCG sensors are distorted by movement during everyday activities such as typing on a keyboard or operating a mobile device.
  • measurements such as heart rate were only applicable in a fixed medical/hospital setting, however, there is an increasing demand for wearable devices which can provide continuous monitoring in almost any environment.
  • this data can be used to motivate and guide subjects to achieve or maintain personal health, wellness and fitness goals and/or to guide healthcare practitioners in their medical decisions.
  • this data may also be useful in a scientific and clinical research setting.
  • ECG-based monitors While wearable ECG-based monitors are proven to provide an accurate measure of heart rate, they are limited by their chest strap property and by the discomfort that may arise from wearing the strap for extended periods of time. ECG- based monitors therefore do not provide a seamless user experience.
  • a wristwatch form factor is generally a more favorable option, however, with this requirement comes the challenge of measuring heart rate under conditions which may cause distortion to the signal. Measuring heart rate on the extremities includes correcting for the motion of the extremities, a problem which does not affect chest strap recordings. Since the PPG technique is traditionally applied to measurements which arc taken from a motionless subject, situations in which the subject is no longer stationary are not accounted for.
  • a wrist-worn device can provide a number of challenges with regard to fine hand and finger movements that distort the optical signal and which are part of everyday activities such as typing. There are a number of ways to go about compensating for motion artifacts in a physiological signal and therefore some of the most relevant prior art items focused on such techniques are reviewed below.
  • Inertial motion sensors such as accelerometers
  • accelerometers are a popular tool for mea- suring motion and/or activity. They therefore form part of a number of inventions aiming to compensate for the motion artifacts known to corrupt physiological signals.
  • a chest-worn heart rate monitor which includes an accelerometer is disclosed. The accelerometer signal is processed to generate a body motion signal which is then used for motion artifact cancellation to generate an acceleration-based heart rate measurement.
  • a motion compensated pulse oximeter is described which also incorporates an accelerometer to measure the changes induced by motion between the light emitter and detector.
  • An attenuation factor is then calculated using a combination of the accelerometer data, an equation related to a model distance between light emitter and detector and a model based on the expected behavior of light.
  • a look-up table is then used to find a motion measurement that corresponds to the attenuation factor. This measure is then used to better calculate the physiological parameter of interest.
  • optical techniques have also been utilized to achieve the same end. In patent US 20140213863 to Texas Instruments Inc. a motion compensation method for a wearable PPG device which employs an optical motion signal is described.
  • the light detected from a second LED acts as a reference motion signal.
  • This reference signal is used to reduce noise and motion artifact by subtracting it from the desired heart rate signal.
  • Accelerometer data is further used to determine if the signal does indeed contain noise and whether processing should occur to compensate for this.
  • the patent focuses specifically on sensor displacement or changes in position of the sensor in the context of a wrist-worn device. A similar method is also employed by US 20120150052 A 1 to Schoshe Industries Inc.. Another approach described by US 7020507 explains how specific digital signal processing steps can be used for motion artifact removal.
  • This method comprises transforming the data into the frequency domain and selecting candidate spectral cardiac peaks, along with their harmonics. These peaks arc then reconstructed in the time domain and the second order derivative is computed to separate unwanted artifacts that are generated by motion. Importantly, this method does not use a separate noise channel in addition to the PPG signal.
  • digital filters can also be used to achieve a motion-corrected signal.
  • US 5853364 describes a method which employs model-based adaptive filtering, more specifically a Kalman filter, to estimate what the output signal should be under noisy conditions. The method employs mathematical models to describe how the measured physiological signal changes in time and how it is related to measurements affected by motion artifact.
  • US 8655436 describes a heart rate meter and a signal processor which, using a bandpass filter, passes a frequency component in a pre-delermined narrow band to remove noise and motion artifact.
  • sEMG represents a good candidate for capturing a physiologically relevant human motion signal and therefore forms the basis of the current invention.
  • sEMG is a non-invasive method for measuring the electrical activity of the muscles. While the forearm contains numerous muscles which are responsible for flexion, extension and pronation of the hand and fingers, only the superficial muscle activity can be measured with sEMG and therefore electrodes must be placed strategically.
  • sEMG has many applications comprising physical rehabilitation, the detection of neuromuscular disease, analysis of signals for prosthetic devices and the analysis of the mechanics of human movement i.e.
  • sEMG may provide a solution for compensating for the motion artifacts that are observed when particular muscles are neurologically activated.
  • a search of the prior art has shown that sEMG has not been used specifically for the reduction of motion artifacts in optical physiological signals. This is however not the case with regard to ECG-based measurements. Since both EMG and ECG use the same recording modality, unwanted sEMG signals are often picked up in addition to heart rate. Methods have therefore been described for canceling noise generated by sEMG from the ECG signal (US5337753 and CA 2236877) .
  • US5337753 applies specifically to a hollow cylindrical bar containing electrodes for use in exercise apparatus whereby the subject grips the cylinder with their hands for a heart rate measurement.
  • An sEMG recording is also taken from each electrode pad to account for noise due to muscular movement and the recordings are subtracted from one another to generate a zero output reading from the sEMG.
  • CA2236877 applies specifically to an ECG-based heart rate monitor which removes unwanted EMG signals by a learning adaptive threshold detection system. This method further removes EMG peaks and enhance ECG peak since ECG peaks have steeper slopes and sharper tips than typical EMG waves.
  • patent CN103654774 describes a system which includes at least two EMG electrodes placed around the wrist. In addition to gesture control the system is able to relay information concerning muscular strength and fatigue.
  • US 20140135631 to Fitbit Inc. discloses a method for activating a wearable heart rate monitor on demand by a user interaction such as moving the hand- wearing device in a defined motion pattern.
  • US 20140142437 discloses a method which uses EMG recordings taken from the footpad electrodes of a weighing-scale ballistocardiograph (BCG) device to detect the motion of the user while he/she stands on the device. This method does not directly use the EMG signal to improve the heart rate measurement, but rather uses the information to assess whether the subject's movement is excessive and if it would be of interest to take the reading again.
  • BCG ballistocardiograph
  • the present invention overcomes problems and obstacles associated with motion artifacts present in physiological signals measured using techniques including, but not limited to, PPG, ECG and BCG.
  • motion compensation is achieved by exploiting a simultaneously measured sEMG signal.
  • EMG is defined as a measure of the electrical activity of skeletal muscle and can be measured nori-invasively using surface electrodes.
  • Muscular contraction and/or relaxation, from which certain motion artifacts originates, can be efficiently captured by EMG equipment and provides a physiologically relevant signal for use in motion compensation.
  • the process of muscular contraction is initiated by the motor neurons of the anterior horn of the spinal cord which carry nerve impulses to the muscles. Action potentials are transmitted across the neuromuscular junction and are propagated throughout the muscle.
  • EMG action potentials have a conduction velocity of 2-6rn/sec and the signal captured by sEMG electrodes and equipment can be described as as a burst of activity centered around a resting voltage.
  • this signal can be transformed into a continuous signal which is easily subtracted from a physiological signal in a similar manner in which accelerometer data can be subtracted to achieve motion compensation.
  • the current invention has shown to provide a more accurate prediction than an accelerometer and in addition, the current inven- tion may provide a more power efficient and compact measure of signal distortion compared to other methods.
  • This motion compensation method can be applied to the measurement of metric comprising, but not limited to, heart rate, heart rate variability, oxygen saturation, breathing rate and pulse transit time.
  • Figure 1 A schematic representation of an exemplary embodiment of the overall process of the current invention illustrated by a flow diagram.
  • Figure 2 A schematic representation of an exemplary embodiment of a wearable device comprising sEMG electrodes (8) and a PPG sensor (9) .
  • Figure 3 A basic embodiment of the invention in the context of mobile and internet technologies.
  • Figure 4 (A) Measured signals from a heart rate sensor, accelcrometer, sEMG electrodes and an optical near infrared (NIR) light and sensor. (B) A plot showing the correlation between the noise in the heart rate monitor signal and the measured accelerometer, sEMG and NIR signals over time.
  • FIG. 5 Two different graphs showing heart rate output, where either the accelcrometer or sEMG signal is used as a noise reference signal in an adaptive filter.
  • Figure f depicts an exemplary embodiment of the current invention which shows a flow diagram of the process of the current invention.
  • Both the raw sEMG signal (1) and physiological signal (2) measured by techniques including, but not limited to, PPG, BCG and EGG may be subjected to a pre-processing step (3) .
  • This step may comprise taking the derivative of the raw signals in the time and/or frequency domain.
  • the processed sEMG signal may be subtracted from the physiological signal in order to compensate for signal distortion due to motion.
  • This process may make use of an adaptive filter (4) to produce a motion compensated signal (5) .
  • the method of the current invention may be applied to the measurement of metrics comprising heart rate (HR) , heart rate variability (HRV), oxygen saturation (Sp02), breathing rate (BR) and pulse transit time (PTT) (6) .
  • HR heart rate
  • HRV heart rate variability
  • Sp02 oxygen saturation
  • BR breathing rate
  • PTT pulse transit time
  • Figure 2 depicts an exemplary embodiment of a wearable device (7) which shows how sEMG electrodes (8) can be placed in a band configuration around the wrist. Finger flexion and extension motions can be easily identified in the sEMG signal, with index finger movement providing a particularly distinct sEMG signal.
  • the sEMG electrodes may be incorporated into one or more devices which uses a sensor (9) to measures physiological signals such as heart rate.
  • physiological signals may be measured based on techniques including, but not limited to, photo- plethysmography, balisto cardiography and/or electrocardiography
  • the electrodes may be incorporated into a band with the physiological sensor which contacts the skin and may be worn on parts of the body including, but not limited to, the wrist, forearm and upper arm.
  • FIG. 3 depicts a basic embodiment of the invention where (7) is the wearable electronic device containing the necessary sensor means to measure a physiological and sEMG signal.
  • the wearable device optionally contains a display (10) and is capable of transmitting data to a mobile device (11) and or directly to an internet based platform (12).
  • the data can be stored and further processed on a server (13) for future retrieval and to be viewed on a computing platform exemplified by the personal computer (14), the mobile phone (11) and or wearable device (7) .
  • Figure 4A is an exemplary embodiment of the invention which depicts the measured signals from a PPG-based heart rate sensor (15), accelerometer (16) , sEMG electrodes (17) and an optical near infrared (NIR) light and sensor (18) .
  • a device with the above mentioned sensors was placed around the wrist of a subject.
  • the bursts of activity seen between baseline readings are generated from specific hand gestures such as typing and were interspersed with rest periods.
  • Figure 4B depicts a correlation plot (19), generated from said second embodiment, between the noise in the heart rate sensor signal and the measured acceleroineter, sEMG and NIR signals over time. It is ideal for a reference signal to have a very low correlation during resting periods and a high correlation during movement, which then makes it a good contender for use in a noise canceling adaptive filter.
  • the IR reference has a very high correlation with the heart rate sensor during rest because the HR is picked up in both sensors. During movement, the correlation decreases but still remains high, partly since the nature of the corruption in the heart rate sensor and IR signals are similar. This makes it difficult to compare the IR reference with regards to correlations, but it was still included for comparison. Both the accelerom- eter and sEMG signal provide a similar level of correlation with the noise signal from the heart rate sensor. In some places sEMG leads to higher correlations, while in other places the accelerometer does better.
  • FIG. 5 depicts an exemplary embodiment which shows two different heart rate output graphs based on PPG heart rate sensor data. These graphs compare heart rate predications which use either an accelerometer or an sEMG signal as the noise reference in an adaptive filter to provide a motion compensated heart rate. The signals are compared to each other and to a set of heart rate data collected simultaneously with an ECG chest strap. The above graph (20) shows that with this particular dataset using the sEMG signal as a reference outperforms using the accelerometer. The opposite is found in the graph below (21) .

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Abstract

La présente invention concerne un système et un procédé de réduction des artefacts de mouvement par le biais de l'EMG de surface. Le procédé de l'invention est destiné à être appliqué à une analyse des signaux physiologiques. Le système et le procédé de l'invention peuvent compenser les artefacts de mouvement qui altèrent les signaux physiologiques, mesurés par des dispositifs portables, pendant le mouvement.
PCT/US2015/040543 2014-10-02 2015-07-15 Système et procédé de réduction des artefacts de mouvements par le biais de l'électromyographie de surface WO2016053444A1 (fr)

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Cited By (1)

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EP4420605A3 (fr) * 2023-02-22 2024-10-23 Meta Platforms Technologies, LLC Utilisation d'électrodes d'électromyographie (emg) pour des mesures physiologiques

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110870769B (zh) * 2018-09-03 2022-08-09 香港理工大学深圳研究院 一种肌肉疲劳等级的检测方法及设备
TWI833097B (zh) * 2020-08-04 2024-02-21 臺北醫學大學 判定疲憊指數之方法和設備

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US20030125635A1 (en) * 2001-12-27 2003-07-03 General Electric Company Method and apparatus for noise reduction of electromyogram signals
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US20070129915A1 (en) * 2003-11-19 2007-06-07 Urban Blomberg Method, device and computer program product for filtering an emg signal out of a raw signal
US7433718B2 (en) * 2002-06-19 2008-10-07 Ntt Docomo, Inc. Mobile terminal capable of measuring a biological signal
US20100113960A1 (en) * 2008-04-15 2010-05-06 Christopher Scheib Method and system for jointly monitoring physiological conditions
US20110028823A1 (en) * 2009-07-28 2011-02-03 Gilmore L Donald Biomedical electrode configuration for suppressing movement artifact
WO2012061707A2 (fr) * 2010-11-04 2012-05-10 The Cleveland Clinic Foundation Dispositif de rétroaction biologique de poche et procédé d'autorégulation d'au moins un état physiologique d'un sujet
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US5630425A (en) * 1995-02-17 1997-05-20 Ep Technologies, Inc. Systems and methods for adaptive filtering artifacts from composite signals
US6224549B1 (en) * 1999-04-20 2001-05-01 Nicolet Biomedical, Inc. Medical signal monitoring and display
US20030125635A1 (en) * 2001-12-27 2003-07-03 General Electric Company Method and apparatus for noise reduction of electromyogram signals
US20040073098A1 (en) * 2002-01-07 2004-04-15 Widemed Ltd. Self-adaptive system for the analysis of biomedical signals of a patient
US7433718B2 (en) * 2002-06-19 2008-10-07 Ntt Docomo, Inc. Mobile terminal capable of measuring a biological signal
US20070129915A1 (en) * 2003-11-19 2007-06-07 Urban Blomberg Method, device and computer program product for filtering an emg signal out of a raw signal
US20100113960A1 (en) * 2008-04-15 2010-05-06 Christopher Scheib Method and system for jointly monitoring physiological conditions
US20110028823A1 (en) * 2009-07-28 2011-02-03 Gilmore L Donald Biomedical electrode configuration for suppressing movement artifact
WO2012061707A2 (fr) * 2010-11-04 2012-05-10 The Cleveland Clinic Foundation Dispositif de rétroaction biologique de poche et procédé d'autorégulation d'au moins un état physiologique d'un sujet
US20140094675A1 (en) * 2012-09-29 2014-04-03 Aliphcom Arrayed electrodes in a wearable device for determining physiological characteristics

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Publication number Priority date Publication date Assignee Title
EP4420605A3 (fr) * 2023-02-22 2024-10-23 Meta Platforms Technologies, LLC Utilisation d'électrodes d'électromyographie (emg) pour des mesures physiologiques

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