Patent Description:
It may be desirable to measure respiration of a subject, for example a human or another animal with lungs, that respires by inhaling and exhaling. When the subject inhales the lungs fill with air and the chest wall moves outwards and when the subject exhales the lungs deflate and the chest wall moves outwards.

<CIT> discloses a system and method for monitoring respiration, comprising a respiration sensing module <NUM> and a supplementary sensing module <NUM>. The system is configured to be fixed to a subject's body (for example, the torso area).

The motion sensor types used in the respiration sensing module may include a force sensing resistor, a capacitor, a hall-effect probe, a piezoelectric sensor, a microphone and an ultrasonic sensor. The supplementary sensing module uses an accelerometer to distinguish between overall body motion of a subject and motion due to a subject's respiration.

Technical paper "Simple to complex modelling of breathing volume using a motion sensor", discloses that motion sensors such as accelerometers may be useful to estimate VE (ventilation).

Both documents disclose the application of machine learning when monitoring respiration.

According to a first aspect, it is provided a respiratory volume measurement system comprising means for: receiving at least one accelerometer sensor output signal dependent upon respiratory motion of a chest wall of a subject; and producing a respiration measurement output, different to the at least one accelerometer sensor output signal, that provides a measure of respiration volume of the subject, wherein the respiration measurement output is produced using machine learning, and takes as input the time differential of the accelerometer sensor output signal.

According to a second aspect, it is provided a computer implemented method comprising:receiving an accelerometer sensor output signal dependent upon respiratory motion of a chest wall of a subject; and producing a respiration measurement output, different to the accelerometer sensor output signal, that provides a measure of respiration volume of the subject, wherein the respiration measurement output is produced using machine learning, and takes as input the time differential of the accelerometer sensor output signal.

According to a third aspect, it is provided a computer program comprising instructions that when executed by a processor cause determination, based on machine learning, of a respiration measurement that provides a measure of respiration volume of a subject using as input the time differential of a received accelerometer sensor output signal dependent upon respiratory motion of a chest wall of the subject.

It would be desirable to measure respiration of a subject by measuring motion of a chest wall of a subject. The subject may be a human or an animal.

Motion of the chest wall of a subject may, for example, be measured by attaching a motion sensor to the subject's chest wall. The sensor then produces an output signal dependent upon the respiratory motion of the chest wall of the subject. Subsequent processing of the sensor output signal can then be used to produce a respiration measurement output that provides a measure of respiration volume of the subject.

A measure of respiration volume is a value that is dependent upon respiration volume and from which a value for respiration volume can be obtained. For example, the respiration volume is a measure of the respiration volume. For example, a respiration flow-rate is a measure of the respiration volume as it can be integrated to obtain the respiration volume. Therefore, subsequent processing of the sensor output signal can then be used to produce a respiration measurement output that provides a respiration volume of the subject. Therefore, subsequent processing of the sensor output signal can then be used to produce a respiration measurement output that provides a respiration flow rate of the subject.

Other parameters may also be measured such as inspiration/expiration phase start, duration of inspiration phase, duration of expiration phase, duration of respiration cycle.

Motion of the chest wall of a subject may, for example, be measured by attaching multiple motion sensors to the subject's chest wall. Each sensor then produces an output signal dependent upon the respiratory motion of the chest wall of the subject. Subsequent processing of the sensor output signals can then be used to produce a respiration measurement output that provides a measure of respiration volume of the subject.

In the example described below, a respiratory volume measurement system <NUM> uses a machine learning system <NUM> to process a sensor output signal <NUM> or to process sensor output signals <NUM>. The machine learning system <NUM> produces as a respiration measurement output a measure of respiration volume of a subject <NUM>. It can also provide a measure of respiration rate of the subject <NUM>.

A measure of respiration volume is a value that is dependent upon respiration volume and from which a value for respiration volume can be obtained. For example, the respiration volume is a measure of the respiration volume. For example, a respiration flow-rate is a measure of the respiration volume as it can be integrated to obtain the respiration volume. Therefore, in some examples, the machine learning system <NUM> produces, as a respiration measurement output, a respiration volume of the subject. Therefore, in some examples, additionally or alternatively, the machine learning system <NUM> produces, as a respiration measurement output, a respiration flow rate of the subject. Other parameters may also be measured such as inspiration/expiration phase start, duration of inspiration phase, duration of expiration phase, duration of respiration cycle.

For example, <FIG> illustrates an example of a respiratory volume measurement system <NUM> comprising one or more sensors <NUM> and a machine learning system <NUM>. At least one of the motion sensors is configured to be placed on a chest wall <NUM> of the subject <NUM> (see <FIG>) to produce a sensor output signal <NUM> dependent upon respiratory motion of the chest wall <NUM> of the subject <NUM>. If there is more than one motion sensor <NUM> then each of the motion sensors <NUM> may be configured to be placed on the chest wall <NUM> of the subject <NUM> to produce a respective sensor output signal <NUM> dependent upon respiratory motion of the chest wall <NUM> of the subject <NUM>.

The machine learning system <NUM> is configured to receive the sensor output signal <NUM> (or sensor output signals <NUM>) as an input and to produce a respiration measurement output <NUM>, different to the sensor output signal <NUM>, that provides a measure of respiration volume of the subject <NUM>.

The respiratory measurement output <NUM> quantifies the respiration volume of the subject <NUM>. This quantification may, for example, be relative to the subject <NUM> measuring for example a percentage increase or decrease relative to a previously determined respiration volume of the subject. Alternatively, the respiratory measurement output <NUM> may quantify the respiration volume of the subject relative to an absolute volume scale, for example ml (milliliters) or ml/s (milliliters per second). The term 'absolute' implies measurement against a reference scale rather than a relative measurement. It does not necessarily imply a higher accuracy of measurement.

<FIG> illustrates an example of a subject <NUM>. In this example a motion sensor <NUM> is configured to be positioned and held at a sternum <NUM> of the human subject <NUM> and to move with respiratory motion of the chest wall <NUM>. In this example, the motion sensor <NUM> is configured to measure both dorsal-ventral movement (anterior-posterior movement) as indicated by the z-axis ( reference z in the Fig) and also to measure longitudinal measurement as indicated by the x-axis (reference x in the Fig). The direction of longitudinal movement is head-toe and is orthogonal to the dorsal-ventral movement. In other examples only dorsal-ventral movement may be measured or only longitudinal movement may be measured. The one or more sensors <NUM> may be held in position at the sternum <NUM> by a belt (not illustrated for clarity).

The sensor output signal <NUM> from a sensor may be post-processed by the sensor <NUM> or pre-processed by the machine learning system <NUM> or elsewhere, to produce a new sensor output signal <NUM>. This processing will be described in further detail in relation to <FIG>. However, it may for example be desirable to differentiate a sensor output signal <NUM> that represents acceleration in the dorsal-ventral direction and/or the longitudinal direction. <FIG> illustrates an example in which the sensor output signal <NUM><NUM> represents a time-variable acceleration signal and the sensor output signal <NUM><NUM> represents the first-order time-differential of that signal. One or both of the sensor output signals <NUM><NUM>, <NUM><NUM> may for example be used by the machine learning system <NUM> to obtain the respiration measurement output <NUM>.

<FIG> illustrates an example of a motion sensor <NUM> that comprises one or more sensors for measuring motion. The motion may, for example, be a time-variable displacement (linear and/or angular), or a first or second derivative thereof, measured relative to one, two or three orthogonal axes. The motion sensor <NUM> may, for example, measure the time-variable displacement (linear and/or angular) directly or measure a force that depends upon the time-variable displacement (linear and/or angular) and which can be converted to the time-variable displacement (linear and/or angular).

For example, a linear motion sensor, for example an accelerometer <NUM> can measure linear acceleration and produce a sensor output signal <NUM> that is proportional to acceleration in a particular direction.

For example, an angular motion sensor, for example an accelerometer <NUM> can measure angular acceleration (rate of change of angular velocity) and produce a sensor output signal <NUM> that is proportional to acceleration about a particular axis.

For example, an angular motion sensor, for example a gyroscope <NUM> can measure orientation or angular velocity about a particular axis.

The axis of an accelerometer <NUM> is therefore defined, for a linear accelerometer, as the direction of linear acceleration that is measured and for a rotational accelerometer or gyroscope , as the axis of rotation about which rotation is measured.

A multiple axis accelerometer measures acceleration in relation to multiple orthogonal axes.

<FIG> illustrates that multiple motion sensors <NUM> may be aligned along multiple different orthogonal axes to form a multiple axis motion sensor. In this example a three-axis system is described. For example, one of the axis can be aligned with the dorsal-ventral (anterior-posterior) direction of the subject <NUM>, another axis can be aligned with a lateral direction of the subject <NUM> and another axis can be aligned with a longitudinal direction.

In some examples only linear motion sensors <NUM> (e.g. accelerometers) may be used, in other examples only angular motion sensors <NUM>( e.g. angular accelerometers or gyroscopes) may be used and in other examples both linear and angular accelerometers may be used. One advantage of using angular accelerometers or gyroscopes is that they are not susceptible or are less susceptible to the effects of gravity. The effects of gravity may produce an error in the operation of linear accelerometers. However, gravity is constant and this constant error can be removed by differentiation of the sensor output signal <NUM>.

Small scale linear accelerometers and angular accelerometers or gyroscopes are used in mobile electronic devices such as mobile telephones and similar sensors can be used as sensors <NUM>.

A single device may be used as a linear accelerometer and an angular accelerometer or gyroscope.

<FIG> illustrates an example of processing of a sensor output signal <NUM> before it is processed by an algorithm of the machine learning system <NUM>. This processing may be performed as post-processing at the sensor <NUM> or as pre-processing at the machine learning system <NUM> or elsewhere.

The motion sensor <NUM> produces a sensor output signal <NUM><NUM>. The sensor output signal <NUM><NUM> is differentiated by the differentiator <NUM> to produce sensor output signal <NUM><NUM> The sensor output signal <NUM><NUM> is filtered by the filter <NUM> to produce sensor output signal <NUM><NUM>. and the sensor output signal <NUM><NUM> is normalized by normalizer <NUM> to produce the sensor output signal <NUM><NUM> which is used by the machine learning system <NUM>.

It should be appreciated that the filter <NUM>, the differentiator <NUM> and the normalizer <NUM> are optional and any one of them, or any combination of them may or may not be used. It should also be appreciated that they may perform their functionality in the analogue domain or in the digital domain.

It should be appreciated that the filter <NUM> and/or the differentiator <NUM> and/or the normalizer <NUM> can implemented as circuitry or as software or firmware performed by a processor.

The filter <NUM> may be a low pass filter that passes signals with a frequency of less than <NUM> or substantially <NUM> or lower or higher.

DC signals from the sensor <NUM> do not represent changes in the respiratory volume. Therefore, in some examples, the filter <NUM> may be a band-pass filter with a range greater than <NUM> or substantially <NUM>. For example, it may be a band-pass filter with low attenuation in the range <NUM> to <NUM> or substantially in the range <NUM> to <NUM>.

In some examples, the filter <NUM> may be an adaptive filter that varies. For example, it may be configured to change its band-pass in dependence upon a heart rate of the subject or increasing frequency of respiration so that the filter <NUM> can accommodate normal respiratory conditions at rest and also normal respiratory conditions during exercise.

The differentiator <NUM> is configured to receive the sensor output signal <NUM><NUM> as an input and to differentiate the sensor output signal <NUM><NUM> to produce an input parameter for the machine learning algorithm. In the example illustrated it produces the sensor output signal <NUM><NUM>. The differentiation may be a first order time differentiation or it may be higher order differentiation. In the example where the sensor output signal <NUM><NUM> represents acceleration measured by an accelerometer <NUM> then the first differential in time produced by the differentiator <NUM> represents a jerk (the rate of change of acceleration) and this is output as the sensor output signal <NUM><NUM>.

The normalizer <NUM> is configured to receive a sensor output signal <NUM><NUM> and normalize the signal to produce a sensor output signal <NUM><NUM>. This may be achieved by dividing the signal <NUM><NUM> by its maximum value and processing it so that it has a zero mean.

Although <FIG> illustrates only a single sensor <NUM>, it should be appreciated that a similar process may be performed for each sensor <NUM> where there are multiple sensors <NUM>. The outcome of the process illustrated in <FIG> is the production of an input parameter p for a machine learning algorithm used by the machine learning system <NUM>. Where multiple sensors <NUM>i are used the input parameters pi may be combined in different weighted combinations of the parameters and/or functions of the parameters either as pre-processing for input to the machine learning algorithm or as part of the machine learning algorithm. The parameters or combined parameters are, however, still sensor output signals <NUM>.

<FIG> illustrate the use of the machine learning system <NUM> to provide an absolute measurement of the respiration volume of the subject by calibration.

<FIG> illustrates the training process that calibrate the system. During the calibration process various sensor output signals <NUM> are provided to the machine learning algorithm along with corresponding absolute values of respiration volume as training data. The machine learning algorithm generates a function that takes as inputs the various sensor output signals <NUM> and produces as outputs correct estimates of the corresponding absolute values of respiration volume. This may be achieved using supervised learning using a suitable machine learning algorithm such as linear regression, quadratic regression, neural networks with back propagation.

Once the machine learning algorithm has been trained using the above-described supervised learning process, as illustrated in <FIG>, the calibrated machine learning system <NUM> is able to receive as an input a sensor output signal <NUM> and to produce automatically a respiration measurement output <NUM> that provides an absolute measurement of the respiration volume of the subject <NUM>.

<FIG> illustrate the use of the machine learning system <NUM> to identify a respiration pattern of the subject by classification.

<FIG> illustrates the training process. During the training process various sensor output signals <NUM> are provided to the machine learning algorithm. The machine learning algorithm generates a function that takes as inputs the various sensor output signals <NUM> and classifies the inputs at that time into one or multiple different classes. This may be achieved using unsupervised learning using a suitable machine learning algorithm such as logistic regression.

Once the machine learning algorithm has been trained using the above-described unsupervised learning process, as illustrated in <FIG>, the trained machine learning system <NUM> is able to receive as an input a sensor output signal <NUM> and to produce automatically a respiration measurement output <NUM> that classifies the input.

The trained machine learning system <NUM> can classify breathing patterns and identify breathing patterns that deviate from a reference breathing pattern(s) using the respiration measurement output <NUM>. It can, for example, detect a breathing pattern produced by sleep apnoea (also spelt apnea).

In the example illustrated in <FIG>, multiple motion sensors <NUM> are used and they are deliberately physically separated from each other. The purpose of this separation is so that they are both affected by gross movements of the subject <NUM> to the same or substantially the same extent but are subjected to different movements in response to movement of the chest wall <NUM> caused by respiration of the subject <NUM>. The difference between the sensor output signals <NUM> from the positionally offset sensors <NUM> is therefore substantially independent of the gross motion of the subject <NUM> and is dependent upon the motion of the chest wall <NUM> of the subject <NUM> as a consequence of respiration. This difference signal may therefore be used as a sensor output signal <NUM> provided to the machine learning system <NUM>. In this way, the effect of gross motion of the subject's body may be mitigated.

<FIG> illustrates an example of a method <NUM>.

At block <NUM>, the method <NUM> comprises receiving a sensor output signal <NUM> dependent upon respiratory motion of a chest wall <NUM> of a subject <NUM>.

At block <NUM>, the method <NUM> comprises producing a respiration measurement output <NUM>, different to the sensor output signal <NUM>, that provides a measurement of respiration volume of the subject <NUM>, wherein the respiration measurement output <NUM> is produced using machine learning.

As described before in relation to the preceding examples, the method <NUM> may quantify the respiration volume of the subject <NUM> using calibration or classification.

<FIG> illustrates an example of a controller <NUM>. Implementation of a controller <NUM> may be as controller circuitry. The controller <NUM> may be implemented in hardware alone, have certain aspects in software including firmware alone or can be a combination of hardware and software (including firmware).

The memory <NUM> stores a computer program <NUM> comprising computer program instructions (computer program code) that controls the operation of the machine learning system <NUM> when loaded into the processor <NUM>. The computer program instructions, of the computer program <NUM>, provide the logic and routines that enables the apparatus to perform the methods illustrated in <FIG>. The processor <NUM> by reading the memory <NUM> is able to load and execute the computer program <NUM>.

The machine learning system <NUM> therefore comprises:.

Quantification of the respiration volume of the subject <NUM> uses, for example, calibration or classification.

As illustrated in <FIG>, the computer program <NUM> may arrive at the machine learning system <NUM> via any suitable delivery mechanism <NUM>. The delivery mechanism <NUM> may be, for example, a machine readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, a record medium such as a Compact Disc Read-Only Memory (CD-ROM) or a Digital Versatile Disc (DVD) or a solid state memory, an article of manufacture that comprises or tangibly embodies the computer program <NUM>. The delivery mechanism may be a signal configured to reliably transfer the computer program <NUM>. The machine learning system <NUM> may propagate or transmit the computer program <NUM> as a computer data signal.

The computer program <NUM> comprises instructions that cause determination, based on machine learning, of a respiration measurement that provides a measure of respiration volume of a subject <NUM> using a received sensor output signal <NUM> dependent upon respiratory motion of a chest wall <NUM> of the subject <NUM>. The instructions cause quantification of the respiration volume of the subject <NUM> using, for example, calibration or classification.

The blocks illustrated in the <FIG> may represent steps in a method and/or sections of code in the computer program <NUM>. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some blocks to be omitted.

<FIG> illustrate different examples of implementation of the respiratory volume measurement system <NUM>.

In the example illustrated in <FIG> the respiratory volume measurement system <NUM> is housed entirely within a single apparatus <NUM> which comprises one or more motion sensors <NUM> and the machine learning system <NUM>. In this example the apparatus <NUM> may be affixed to the chest wall of the subject <NUM>. The apparatus <NUM> may have communication means for communicating in a network (not illustrated).

<FIG> illustrates that the respiratory volume measurement system <NUM> may be split into two different apparatus <NUM>, <NUM>. In this example the apparatus <NUM> comprises one or more motion sensors <NUM> and the apparatus <NUM> comprises the machine learning system <NUM>. Both the one or more sensors <NUM> and the machine learning system <NUM> are able to communicate between the apparatus <NUM> and the apparatus <NUM> via a communication channel <NUM>. This communication channel may, for example, be provided via a wireless interface. In this example the apparatus <NUM> may be placed upon the chest wall of the user. The apparatus <NUM> may be local but separated from the apparatus <NUM> or it may be separated at some distance from the apparatus <NUM> via a network, for example.

<FIG> illustrates a development of the system <NUM> illustrated in <FIG>. In this example the apparatus <NUM> is replaced by a distributed network <NUM> comprising multiple apparatus <NUM>.

It should be appreciated that the production of the sensor output signal(s) <NUM> and the production of the respiration measurement output <NUM> may be separated in time and/or space. For example, the machine learning system <NUM> may receive the sensor output signal <NUM> live and produce, in real time, a respiration measurement output <NUM>. However, in other examples the sensor output signals <NUM> may be stored in a memory system and the machine learning system <NUM> may at a later time process those signals to produce the respiration measurement output <NUM>.

Also, as previously described, the motion sensor <NUM> and the machine learning system may be separated in space or may be in the same location or apparatus.

In the description, features are illustrated as coupled. These features are operationally coupled and any number or combination of intervening elements can exist (including no intervening elements).

The controller <NUM> performs the function of the machine learning system <NUM> and may be replaced by any suitable process means. It may, for example, be a programmable processor controlled by software or firmware, circuitry, programmed logic gates, multiple processors.

The respiratory volume measurement system <NUM> comprising means for: receiving at sensor output signal <NUM> dependent upon respiratory motion of a chest wall <NUM> of a subject <NUM>; and for producing a respiration measurement output <NUM>, different to the sensor output signal <NUM>, that provides a measure of respiration volume of the subject <NUM>, wherein the respiration measurement output <NUM> is produced using machine learning.

The respiration volume of the subject <NUM> may, for example, be quantified using calibration or classification.

In some but not necessarily all examples, the machine learning system <NUM> is configured to communicate data from the machine learning system <NUM> with or without local storage of the data in a memory <NUM> at the machine learning system <NUM> and with or without local processing of the data by circuitry or processors at the machine learning system <NUM>.

The data may, for example, be measurement data from the sensors <NUM> or data produced by the processing of measurement data from the machine learning system <NUM>.

The data may be stored in processed or unprocessed format remotely at one or more devices. The data may be stored in a cloud-based storage system.

The data may be processed remotely at one or more devices. The data may be partially processed locally and partially processed remotely at one or more devices.

The data may be communicated to the remote devices wirelessly via short range radio communications such as Wi-Fi or Bluetooth, for example, or over long range cellular radio links. The apparatus may comprise a communications interface such as, for example, a radio transceiver for communication of data.

The machine learning system <NUM> may be part of the Internet of Things forming part of a larger, distributed network.

The processing of the data, whether local or remote, may be for the purpose of health monitoring, data aggregation, patient monitoring, vital signs monitoring or other purposes.

The processing of the data, whether local or remote, may involve artificial intelligence or machine learning algorithms. The data may, for example, be used as learning input to train a machine learning network or may be used as a query input to a machine learning network, which provides a response. The machine learning network may for example use linear regression, logistic regression, vector support machines or an acyclic machine learning network such as a single or multi hidden layer neural network.

The processing of the data, whether local or remote, may produce an output. The output may be communicated to the machine learning system <NUM> where it may produce an output sensible to the subject such as an audio output, visual output or haptic output.

The systems, apparatus, methods and computer programs may use machine learning which can include statistical learning. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The computer learns from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. The computer can often learn from prior training data to make predictions on future data. Machine learning includes wholly or partially supervised learning and wholly or partially unsupervised learning. It may enable discrete outputs (for example classification, clustering) and continuous outputs (for example regression). Machine learning may for example be implemented using different approaches such as cost function minimization, artificial neural networks, support vector machines and Bayesian networks for example. Cost function minimization may, for example, be used in linear and polynomial regression and K-means clustering. Artificial neural networks, for example with one or more hidden layers, model complex relationship between input vectors and output vectors. Support vector machines may be used for supervised learning. A Bayesian network is a directed acyclic graph that represents the conditional independence of a number of random variables.

The algorithms hereinbefore described may be applied to achieve the following technical effects: measurement of respiration volume of the subject. quantification of respiration volume of the subject, an absolute measurement of respiration volume of the subject, detection of breathing patterns of a subject.

The above described examples may find application as enabling components of:
automotive systems; telecommunication systems; electronic systems including consumer electronic products; distributed computing systems; media systems for generating or rendering media content including audio, visual and audio visual content and mixed, mediated, virtual and/or augmented reality; personal systems including personal health systems or personal fitness systems; navigation systems; user interfaces also known as human machine interfaces; networks including cellular, non-cellular, and optical networks; ad-hoc networks; the internet; the internet of things; virtualized networks; and related software and services.

Although embodiments have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims.

The term 'a' or 'the' is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising a/the Y indicates that X may comprise only one Y or may comprise more than one Y uncles the context clearly indicates the contrary. If it is intended to use 'a' or 'the' with an exclusive meaning then it will be made clear in the context. In some circumstances the use of 'at least one' or 'one or more' may be used to emphasis an inclusive meaning but the absence of these terms should not be taken to infer and exclusive meaning.

Thus 'example', for example', 'can' or 'may' refers to a particular instance in a class of examples.

Claim 1:
A respiratory volume measurement system (<NUM>) comprising means for:
receiving at least one accelerometer sensor output signal (<NUM>) dependent upon respiratory motion of a chest wall (<NUM>) of a subject (<NUM>); and
producing a respiration measurement output (<NUM>), different to the at least one accelerometer sensor output signal, that provides a measure of respiration volume of the subject, wherein the respiration measurement output is produced using machine learning, and takes as input the time differential of the accelerometer sensor output signal.