Patent Description:
Movement sensors can be used to determine motion and/or a current position of a structure. There may be noise in the output of such movement sensors.

<CIT> discloses fusing, or otherwise aggregating, sensor data from an optical sensors configured to detect movement of the wearable computing device relative to the skin surface of a user to detect finer grained movements of the wearable computing device relative to the skin surface. The sensor data captured by one or more of the optical sensors may also be fused with data captured or otherwise obtained from an accelerometer configured to sense linear movements of the wearable computing device or a gyroscope configured to sense rotation of the wearable computing device.

<CIT> fusing multiple input data indicative of the user's movements, supplied by one or more wearable sensors and a handheld device, to form one logical input stream that is presented to an application which is expecting input from a single device.

<CIT> discloses a cross-modal sensor fusion approach to track mobile devices and the users carrying them by matching motion features captured using sensors on a mobile device to motion features captured in images of the device in order to track the mobile device and/or its user.

According to some aspects, there is provided the subject matter of the independent claims. Some further aspects are defined in the dependent claims.

The Figures illustrate an apparatus <NUM> comprising means for: receiving <NUM> at least one first measurement signal and receiving <NUM> at least one second measurement signal. The first measurement signal is received from a first movement sensor <NUM> and the second measurement signal is received from a second movement sensor <NUM> wherein the first movement sensor <NUM> and the second movement sensor <NUM> are provided on the same structure <NUM>. The means are also for identifying <NUM> one or more correlations between the measurement signals; and using <NUM> the identified one or more correlations to adjust at least one output signal provided by at least one detector <NUM>. The detector <NUM> could be, for example an imaging module or an audio capture module. The apparatus <NUM> could be for reducing the noise in the output of the at least one detector <NUM>.

<FIG> schematically illustrates an apparatus <NUM> according to examples of the disclosure. The apparatus <NUM> illustrated in <FIG> may be a chip or a chip-set. In some examples the apparatus <NUM> may be provided within devices such as an audio capture devices or an image capturing device.

In the example of <FIG> the apparatus <NUM> comprises a controller <NUM>. In the example of <FIG> the implementation of the controller <NUM> may be as controller circuitry. In some examples 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).

As illustrated in <FIG> the controller <NUM> may be implemented using instructions that enable hardware functionality, for example, by using executable instructions of a computer program <NUM> in a general-purpose or special-purpose processor <NUM> that may be stored on a computer readable storage medium (disk, memory etc.) to be executed by such a processor <NUM>.

The memory <NUM> is configured to store a computer program <NUM> comprising computer program instructions (computer program code <NUM>) that controls the operation of the apparatus <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 <NUM> 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 apparatus <NUM> therefore comprises: at least one processor <NUM>; and at least one memory <NUM> including computer program code <NUM>, the at least one memory <NUM> and the computer program code <NUM> configured to, with the at least one processor <NUM>, cause the apparatus <NUM> at least to perform: receiving <NUM> at least one first measurement signal from a first movement sensor <NUM>; receiving <NUM> at least one second measurement signal from a second movement sensor <NUM> wherein the first movement sensor <NUM> and the second movement sensor <NUM> are provided on the same structure <NUM>; identifying <NUM> one or more correlations between the measurement signals; and using <NUM> the identified one or more correlations to adjust at least one output signal provided by at least one detector <NUM>.

As illustrated in <FIG> the computer program <NUM> may arrive at the apparatus <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 apparatus <NUM> may propagate or transmit the computer program <NUM> as a computer data signal. In some examples the computer program <NUM> may be transmitted to the apparatus <NUM> using a wireless protocol such as Bluetooth, Bluetooth Low Energy, Bluetooth Smart, 6LoWPan (IPv<NUM> over low power personal area networks) ZigBee, ANT+, near field communication (NFC), Radio frequency identification, wireless local area network (wireless LAN) or any other suitable protocol.

The computer program <NUM> comprises computer program instructions for causing an apparatus <NUM> to perform at least the following: receiving <NUM> at least one first measurement signal from a first movement sensor <NUM>; receiving <NUM> at least one second measurement signal from a second movement sensor <NUM> wherein the first movement sensor <NUM> and the second movement sensor <NUM> are provided on the same structure <NUM>; identifying <NUM> one or more correlations between the measurement signals; and using <NUM> the identified one or more correlations to adjust at least one output signal provided by at least one detector <NUM>.

The computer program instructions may be comprised in a computer program <NUM>, a non-transitory computer readable medium, a computer program product, a machine readable medium. In some but not necessarily all examples, the computer program instructions may be distributed over more than one computer program <NUM>.

References to "computer-readable storage medium", "computer program product", "tangibly embodied computer program" etc. or a "controller", "computer", "processor" etc. should be understood to encompass not only computers having different architectures such as single/multi- processor architectures and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field-programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other processing circuitry.

<FIG> illustrates an example system <NUM> that could be used to implement examples of the disclosure. The system <NUM> comprises a first movement sensor <NUM>, a second movement sensor <NUM>, at least one detector <NUM>, a structure <NUM> and an apparatus <NUM>. In the example system of <FIG> the first movement sensor <NUM> and the detector <NUM> are provided within a first device <NUM> while the second movement sensor <NUM> is provided within a second device <NUM>.

The second movement sensor <NUM> is located apart from the first device <NUM>, for example in a second device <NUM>, even when provided on the same structure <NUM>. The second movement sensor <NUM> can move relative to the first movement sensor <NUM> and the first device <NUM> even when provided on the same structure <NUM>.

The first device <NUM> and the second device <NUM> could be independent devices <NUM>, <NUM>. The devices <NUM>, <NUM> may be independent of each other in that they can be removed from the structure <NUM> and moved independently of each other without the need for any specialist tools or inputs. For example the first device <NUM> and the second device <NUM> could be temporarily attached to the structure <NUM> for the period of time that the detector <NUM> is detecting information and then could be removed from the structure <NUM>.

The devices <NUM>, <NUM> could be independent of each other in that they can be configured to perform functions independently of each other. For instance, the first device <NUM> could be a portable electronic device such as a telephone or imaging device which can be operated to enable functions such as image capture and/or communications without any input from the second device <NUM>. Similarly the second device <NUM> could be a wearable electronic device such as a watch, headset, chest strap or other wearable device which could be configured to perform functions such as monitoring a user's biometric parameters without input from the first device <NUM>. Other types of device <NUM>, <NUM> could be used in other examples of the disclosure.

In some examples of the disclosure a communication link <NUM> may be provided between the first device <NUM> and the second device <NUM>. The communication link <NUM> could be a direct communication link <NUM> between the first device <NUM> and the second device <NUM>. For instance, the communication link <NUM> could be short range communication link <NUM> such as Bluetooth, Bluetooth Low Energy, Bluetooth Smart, 6LoWPan (IPv<NUM> over low power personal area networks) ZigBee, ANT+, near field communication (NFC), or any other suitable communication link <NUM>. In other examples the communication link <NUM> could be an indirect communication link <NUM> in which the first device <NUM> and the second device <NUM> could communicate via one or more intervening devices and/or networks.

The communication link <NUM> could enable the first device <NUM> and the second device <NUM> to be synchronised. This could enable the time at which a measurement is made by the first movement sensor <NUM> to be synchronised with the time a measurement is made by the second movement sensor <NUM>.

In some examples the communication link could enable information obtained by the movement sensors <NUM>, <NUM> to be transmitted between the devices <NUM>, <NUM>. For instance, information obtained by the second movement sensor <NUM> in the second device <NUM> could be transmitted to the first device <NUM>. This could enable a first measurement signal from the first movement sensor <NUM> and a second measurement signal from the second movement sensor <NUM> to be processed by an apparatus <NUM> in the first device <NUM>.

The communication link <NUM> may be enabled by at least one transceiver at the device <NUM> and at least one transceiver at the device <NUM>. The transceivers may comprise any suitable means for receiving and/or transmitting information. The information that is transmitted may be transmitted with or without local storage of the data in memory at the devices <NUM>, <NUM> and with or without local processing of the data by circuitry or processors at the devices <NUM>, <NUM>. The transceivers may comprise, respectively, one or more transmitters and/or receivers. The transceivers may enable a wireless connection between the devices <NUM>, <NUM>. The wireless connection could be via short-range radio communications such as, for example, Wi-Fi, Bluetooth, Bluetooth Low Energy, Bluetooth Smart, 6LoWPan (IPv6 over low power personal area networks) ZigBee, ANT+, near field communication (NFC), or any other suitable type of connection.

The movement sensors <NUM>, <NUM> may comprise any means which may be configured to sense movement and/or a position and provide a measurement signal indicative of the sensed movement and/or position. The movement sensors <NUM>, <NUM> may be configured to sense the movement and/or the position of the structure <NUM> of the system <NUM>. In some examples the movement sensors <NUM>, <NUM> could be configured to sense the movement and/or the position of the devices <NUM>, <NUM> within the system <NUM>. The movement sensors <NUM>, <NUM> could be configured to sense the geographical location, the angular orientation, the elevation or any other suitable position or change in position of the respective components of the system <NUM>.

The movement sensors <NUM>, <NUM> could comprise any suitable type of movement sensors which can produce an output measurement signal indicative of a position or change in position. For example the movement sensors <NUM>, <NUM> could comprise any one or more of accelerometers, gyroscopes, magnetometers or any other suitable means.

In some examples the first movement sensor <NUM> and the second movement sensor <NUM> could comprise the same type of sensor. In other examples the first movement sensor <NUM> and the second movement sensor <NUM> could comprise different types of sensor.

In the system <NUM> shown in <FIG> the first movement sensor <NUM> is independent of the second movement sensor <NUM> in that a measurement made by one of the movement sensors <NUM>, <NUM> does not affect a measurement being made by the other movement sensor <NUM>, <NUM>. That is the first movement sensor <NUM> can obtain a measurement of position and/or movement without affecting a measurement made by the second movement sensor <NUM> and similarly the second movement sensor <NUM> can obtain a measurement of position and/or movement without affecting a measurement made by the first movement sensor <NUM>. The first movement sensor <NUM> may be independent of the second movement sensor <NUM> in that the first movement sensor <NUM> is provided in a first device <NUM> which is independent of the second device <NUM> which comprises the second movement sensor <NUM>.

In the example system of <FIG> the detector <NUM> may also be provided within the first device <NUM>. The detector <NUM> may comprise any means for detecting an input and providing a corresponding output signal. The detector <NUM> could be configured to detect information about the environment in which the detector <NUM> is located. The information could comprise images, audio or any other suitable information.

In some examples the detector <NUM> could comprise an imaging module. The imaging module may comprise any means which may be configured to obtain images. The imaging module may comprise an image sensor which may be configured to convert light incident on the image sensor into an electrical signal to enable an image to be produced. The image sensor may comprise, for example, digital image sensors such as charge-coupled-devices (CCD) or complementary metal-oxide-semiconductors (CMOS). The images which are obtained may provide a representation of a scene and/or objects which are positioned in front of the imaging module. In some examples the imaging module could also comprise one or more optical devices such as lenses which could be configured to focus the light incident on the image sensor.

In some examples the imaging module may comprise a plurality of image sensors which may be configured to enable three dimensional images to be obtained. In such cases it may be useful to know the precise location of the image sensors when they are capturing the images.

In some examples the one or more detectors <NUM> could comprise an audio capture module. The audio capture module could comprise one or more microphones which may be configured to capture an audible signal and transduce the audible signal into an electrical output signal. The audio capture module could comprise an array of microphones which could be configured to capture spatial audio signals. In such cases it may be useful to know the precise location of the microphones when they are capturing the audio signals.

It may be useful to know the precise location of the detector <NUM> when it is detecting an input, from which is derived a corresponding output signal.

The first and second measurement signals which are output by the first and second movement sensors <NUM>, <NUM>, for example as a result of background processes, while the detector <NUM> detects said input, for example as a foreground process, can be used to determine the precise location of the detector <NUM>.

During a period of time in which the detector <NUM> detects the at least one input to which the at least one provided output signal corresponds, the first movement sensor <NUM> is controlled to provide the first measurement signal and the second movement sensor <NUM> is caused to provide the second measurement signal. For example, the device <NUM> could communicate a request for the second measurement signal to the second movement sensor <NUM> (or device <NUM> comprising the second movement sensor <NUM>) via the communication link <NUM>. This may trigger the provision of the second measurement signal which can be transmitted to the device <NUM> via the communication link <NUM>. The detection of the at least one input by the detector <NUM> may be a foreground process and the provision of the first measurement signal and the receipt of the second measurement signal may be background processes.

A foreground process may be one which is designated to run by the user and/or which a user is currently utilising. A background process may be one which runs without user intervention, in the background, and is transparent or substantially transparent to the user in that a user interface (for example that of the device <NUM>, comprising the detector <NUM>) is substantially unchanged as a result of the process running.

In the example system <NUM> of <FIG> the first movement sensor <NUM> is coupled to the detector <NUM>. The first movement sensor <NUM> may be coupled to the detector <NUM> so as to restrict movement of the first movement sensor <NUM> relative to the detector <NUM>. The first movement sensor <NUM> may be coupled to the detector <NUM> so that any movement of the first movement sensor <NUM> is matched by a movement of the detector <NUM>. In the example system <NUM> of <FIG> the first movement sensor <NUM> is coupled to the detector by both the first movement sensor <NUM> and the detector <NUM> being provided within the same device <NUM>. For instance, the first movement sensor <NUM> and the detector <NUM> could both be provided within the same communication device or imaging device. As both the first movement sensor <NUM> and the detector <NUM> are provided within the same device, if the position of the detector <NUM> changes then the position of the first movement sensor <NUM> also changes.

Both the first movement sensor <NUM> and the second movement sensor <NUM> are provided on the same structure <NUM>. The structure <NUM> comprises a physical body which can support both the first movement sensor <NUM> and the second movement sensor <NUM>. The structure <NUM> may be configured to bear, or at least partially bear, the weight of both the first movement sensor <NUM> and the second movement sensor <NUM>. The first movement sensor <NUM> and the second movement sensor <NUM> may be provided on the same structure <NUM> by attaching the first device <NUM> and the second device <NUM> to the structure <NUM>.

The structure <NUM> may be configured to kinetically link both the first movement sensor <NUM> and the second movement sensor <NUM>. The kinetic linking may ensure that there is a correlation between movements detected by the different movement sensors <NUM>, <NUM>. For example, if a first movement is made by the first movement sensor <NUM> then the kinetic linking will define what movement should be made by the second movement sensor <NUM>. The movements that are detected by the different movement sensors <NUM>, <NUM> could be different.

In some examples the structure <NUM> kinetically links the first movement sensor <NUM> and the second movement sensor <NUM> such that if one of the movement sensors <NUM>, <NUM> moves then the second movement sensor will also move. For instance the structure <NUM> could be configured so that if movement of the structure <NUM> causes movement of one of the movement sensors <NUM>, <NUM> then it will also cause a related movement of the other movement sensor <NUM>, <NUM>.

The kinetic linking of the structure <NUM> may be determined so that the expected relationship between the movement of the movement sensors <NUM>, <NUM> can be determined. For example it may be determined how the structure <NUM> causes the movements of the different movement sensors <NUM>, <NUM> to be correlated.

In some examples the structure <NUM> could be the body of a user of the devices <NUM>, <NUM>. For instance, a user could hold a first device <NUM> in their hand and attach the second device <NUM> to a different part of their body. In such examples the second device <NUM> could be attached by a strap, or other suitable means, to the user's arm or torso or any other suitable part of the body. In such examples the movement of the hand that is holding the first device <NUM> is linked to the movement of the other parts of the user's body. Machine learning, or any other suitable process could be used to determine how the structure <NUM> causes the movements of the two devices <NUM>, <NUM> to be correlated.

In some examples the structure <NUM> could be a combination of a plurality of different components that are connected together. For instance, the first device <NUM> could be mounted on a first component and the second device <NUM> could be mounted on a second component. The first and second components could be coupled together temporarily so that the components can be used separately from each other. In some examples the first and second components could be coupled together by one or more intervening components. As an example the structure <NUM> could comprise a user holding a selfie stick. In such examples the first device <NUM> could be mounted on the selfie stick while the second device <NUM> is attached to the user's arm or other part of their body. The selfie stick and the user's body form a single structure <NUM> because the user is holding the selfie stick.

In some examples the structure <NUM> could be a physical structure such as drone. In such examples the drone could comprise two or more measurement sensors <NUM>, <NUM> provided at any suitable locations within the drone.

In some examples the structure could be a physical structure such as a building. For instance a first device <NUM> could be located in a first part of a building and a second device <NUM> could be located in a second part of the building. In such examples the measurement signals from the movement sensors <NUM>, <NUM> could be used to adjust for movement of a building caused by earthquakes, high wind or any other suitable factors.

In the example system <NUM> of <FIG> the first device <NUM> comprises an apparatus <NUM>. The apparatus <NUM> could be as described in relation to <FIG>. The apparatus <NUM> may be configured to obtain the measurement signals from the movement sensors <NUM>, <NUM> and use these to adjust the output of the detector <NUM>.

In other examples the apparatus <NUM> could be provided in a different location. For instance, in some examples the apparatus <NUM> could be provided in the second device <NUM> which does not contain the detector <NUM>. In such examples the measurement signal from the first movement sensor <NUM> and the output from the detector <NUM> could be transmitted to the second device <NUM>. In other examples the apparatus <NUM> could be provided in a different device. For example, the apparatus <NUM> could be in a remote server or distributed across a network and need not be provided on the structure <NUM>.

It is to be appreciated that the system <NUM> shown in <FIG> is an example system and that variations could be made to this system <NUM>. For instance, in the example of <FIG> the system <NUM> comprises two movement sensors <NUM>, <NUM>. In other examples more than two movement sensors <NUM>, <NUM> could be provided. Also in some examples more than one detector <NUM> could be provided, for instance a detector <NUM> could be provided in each of the devices <NUM>, <NUM> within the system <NUM>. In other examples the detector <NUM> might not be provided within either of the devices <NUM>, <NUM>. For instance, the one or more detectors <NUM> could be provided in separate devices which could be coupled to the movement sensors <NUM>, <NUM> via the structure <NUM> and/or any other suitable means. In other examples the one or more detectors <NUM> need not be coupled to the movement sensors <NUM>, <NUM>. For example both of the movement sensors <NUM>, <NUM> could be provided in dedicated sensing devices which could be configured to communicate with the one or more detectors <NUM> to enable the output signal to be adjusted.

<FIG> illustrates an example method that could be implemented using the example apparatus <NUM> and systems <NUM> as described above. The blocks of the example method shown in <FIG> could be implemented by an apparatus <NUM> or by any other suitable device.

The method comprises, at block <NUM>, receiving a first measurement signal from a first movement sensor <NUM> wherein the first movement sensor <NUM> is coupled to at least one detector <NUM>. The first measurement signal may comprise information indicative of a change in position, a change in orientation, a speed of the movement or any other suitable information. The change in position and/or orientation could comprise changes in multiple axes. For example the movement sensor could measure the position along three perpendicular axes and could measure the orientation relative to these axes.

The first measurement signal may provide an indication of the position and/or change in position of the first movement sensor <NUM>. As the first movement sensor <NUM> is coupled to the at least one detector <NUM> this measurement signal also provides an indication of the position and/or change in position of the detector <NUM>.

The method also comprises, at block <NUM>, receiving a second measurement signal from a second movement sensor <NUM> wherein the first movement sensor <NUM> and the second movement sensor <NUM> are provided on the same structure <NUM>. The second measurement signal may comprise information indicative of a change in position, a change in orientation, a speed of the movement or any other suitable information. For example the movement sensor could measure the position along three perpendicular axis and could measure the orientation relative to these axis.

The second measurement signal may provide an indication of the position and/or change in position of the second movement sensor <NUM>. As the second movement sensor <NUM> is coupled to the at first movement sensor <NUM> via the structure <NUM> there will be a relationship between the first measurement signal and the second measurement signal. The relationship may comprise features in each of the measurement signals that are caused by a movement of the structure <NUM>. The relationship may comprise a correlation between the first measurement signal and the second measurement signal that is caused by a movement of the structure <NUM>.

The second measurement signal could be transmitted via a communication link <NUM> so that it is received by the apparatus <NUM>. This enables the apparatus <NUM> to process both the first measurement signal and the second measurement signal.

The apparatus <NUM> may be configured to process both the first measurement signal and the second measurement signal so that the method also comprises, at block <NUM>, identifying one or more correlations between the measurement signals. The block of identifying one or more correlations between the measurement signals may enable any relationship between the first measurement signal and the second measurement signal to be determined. This can enable noise patterns within the measurement signals to be determined.

The block <NUM> of identifying one or more correlations between the measurement signals may comprise identifying movements common to both of the measurement signals. The movements common to both of the measurement signals could be identified from features within the measurement signals that are caused by the same motion of the structure <NUM>. For instance, where the structure <NUM> comprises a user's body and the first movement sensor <NUM> is in a device <NUM> held in the user's hand and the second movement sensor <NUM> is in a device <NUM> attached to the user's arm the movement sensors <NUM>, <NUM> could sense the user moving their arm, for example they could sense the user raising their arm or making any other suitable movement. In this example, the device <NUM> is attached to the same arm as the hand that is holding the device <NUM> such that if the user raises their arm then both the first movement sensor <NUM> and the second movement sensor <NUM> will be elevated at the same time although they may be elevated by different amounts due to being coupled to different parts of the user's arm. The apparatus <NUM> may be configured to detect features within the measurement signals that indicate the correlated movements and use these detected features to identify noise within the measurement signals.

In some examples the block <NUM> of identifying one or more correlations between the measurement signals may comprise using information about the structure <NUM> which supports both the first movement sensor <NUM> and the second movement sensor <NUM> to identify common features in both of the measurement signals. For instance a model of the structure <NUM> can be used to predict how the movements detected by the first movement sensor <NUM> should be related to the movements detected by the second movement sensor <NUM>. The difference in the measured signals and the predicted signals can be used to identify the noise.

The model of the structure <NUM> could be a theoretical model which could be obtained using a modelling process for the structure <NUM>. In other examples real measurements of the structure <NUM> and how the structure <NUM> can move can be used. The real measurements could be used for example where the structure <NUM> is a manufactured entity such as a drone or building which has been manufactured to have specific dimensions and properties.

In some examples the block <NUM> of identifying one or more correlations between the measurement signals comprises using machine learning. The machine learning process may be configured to obtain data during a learning phase which enables the relationship between the first measurement signal and the second measurement signal to be learned.

The machine learning 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.

In some examples the machine learning may comprise comparing the first measurement signal and the second measurement signal when the structure <NUM> makes a known movement and identifying corresponding features within the measurement signals.

In some examples the known movement comprises a predefined gesture that is performed by the structure <NUM>. The predefined gesture could be a movement in which the time, direction vectors and amplitude provide sufficient information to computationally link the measurement signals. For example, where the structure <NUM> comprises the user's body the user could be instructed to perform a predefined gesture at a predetermined time. In such examples instructions could be provided to the user via a user interface of one of the devices <NUM>, <NUM> in the system requiring the user to make the gesture. The gesture could be moving their arm, or any other part of their body, in a prescribed manner. While the user is making the gesture the apparatus <NUM> can compare the respective measurement signals received from the movement sensors <NUM>, <NUM> to determine how the structure <NUM> links the movement sensors <NUM>, <NUM>.

In some examples the known movement could comprise a repeated movement that is made by the structure <NUM>. For example it could be the user walking or running or performing some other activity which causes a movement to be repeated over a plurality of cycles. The machine learning can obtain data over a plurality of cycles of the movement and use these to identify corresponding features within the measurement signals and determine how the structure <NUM> links the movement sensors <NUM>, <NUM>.

In some examples the machine learning process could comprise determining a kinetic linking between the first movement sensor <NUM> and the second movement sensor <NUM>. The kinetic linking is dependent upon the structure <NUM> that the movement sensors <NUM>, <NUM> are provided on. The kinetic linking provides a predictive model of how the respective measurement signals should be linked. The kinetic linking determines how the movement sensors <NUM>, <NUM> move relative to each other. For example, if a known movement is made the kinetic linking can provide an indication of the measurements that should be provided in response to the known movement.

Identifying one or more correlations between the measurement signals comprises determining a displacement of the second movement sensor <NUM> from the first movement sensor <NUM> over time. The determined displacement may be an estimate of the displacement. The displacement may be determined based on the first measurement signal and the second measurement signal.

Synchronised measurements made by the first movement sensor <NUM> and the second movement sensor <NUM> can be compared in order to determine the displacement of the second movement sensor <NUM> from the first movement sensor <NUM> at a given time.

The value of the first measurement signal at each given time during a period of measurement can be subtracted from the value of the second measurement signal at each given time. The resultant time series data can be smoothed to reduce fluctuations in the determined displacement resulting from noise on the individual measurement signals. The smoothing can be achieved by using a moving average. The moving average may be a central moving average and/or a weighted moving average. Alternatively, the first and second measurement signals can be smoothed, for example using a moving average, before being used to determine the displacement of the first and second movement sensors <NUM>, <NUM>.

The second measurement signal is translated based on the determined displacement of the second movement sensor <NUM> from the first movement sensor <NUM> over time. The translated second measurement signal therefore simulates a measurement signal as if from the second movement sensor <NUM> had it been placed at the location of the first movement sensor <NUM> and moved in the same manner as the first movement sensor <NUM> during the period of measurement. Thus, the first measurement signal and the translated second measurement signal are both indicative of the movement and/or position of the first movement sensor <NUM>.

The values of the first measurement signal and the translated second measurement signal can be averaged at corresponding time points to reduce variation from the true value indicating the true movement and/or position of the first measurement sensor <NUM>.

The time series data resulting from said averaging is indicative of the movement and/or position of the first movement sensor <NUM> and has reduced noise compared to the first measurement signal output by the first movement sensor <NUM>. The resultant time series data has an increased signal-to-noise ratio compared to the first measurement signal.

Thus, a more precise position and/or location of the first movement sensor <NUM> can be determined from the resultant time series data than from the first measurement signal. If the detector <NUM> is coupled to the first movement sensor <NUM> so that the movement and/or position of the first movement sensor <NUM> is matched by the detector <NUM>, then a more precise position and/or location of the detector <NUM> can likewise be determined.

The resultant time series data can be used to adjust at least one output signal of the detector <NUM>. For example, information about the position and/or location of the at least one detector <NUM> determined from the resultant time series data can be used to adjust at least one output signal provided by the at least one detector <NUM>.

The signal-to-noise ratio can be further increased by obtaining measurement signals from more than two kinetically linked movement sensors, whereby each measurement signal is translated based on an estimated displacement of the respective movement sensor from the first movement sensor <NUM> over time before averaging over these more than two measurement signals.

Besides averaging, other noise removal techniques can be used in conjunction with the first measurement signal and the translated second measurement signal.

It is to be appreciated that while the second measurement signal has, in the foregoing, been described as being translated based on the estimated displacement of the second movement sensor <NUM> from the first movement sensor <NUM> over time, alternatively both the first and second measurement signal could be translated based on respective displacements of the first and second movement sensors <NUM>, <NUM> from a third object or location.

The displacement of the second movement sensor <NUM> from the first movement sensor <NUM> over time can alternatively be estimated, for use in translating the second measurement signal, by other methods.

For example, the movement between the first and second sensors <NUM>, <NUM> can be classified in accordance with a library of predefined gestures. The predefined gestures in the library may be associated with respective functions of respective predefined forms. The value of the coefficients in these functions are to be determined. Once it is determined which predefined gestures the movement corresponds to, the respective function can be selected and the coefficients of this function obtained by fitting this function to the input first and second measurement signals using regression analysis. The fitted function, for which the coefficients are known, thus describes an estimate of the displacement of the second movement sensor <NUM> from the first movement sensor <NUM> over time.

The classification of the movement between the first and second sensors <NUM>, <NUM> in accordance with a library of predefined gestures may be performed by a classification algorithm. The classification algorithm may be a trained machine learning model produced during a calibration or learning phase. In this example the machine learning is in the form of supervised learning. The model is trained using labelled and paired training data from the first and second movement sensors <NUM>, <NUM> wherein the labels correspond to the predefined gestures. The labelling may be effected by instructing the user to make the predefined gesture and measuring the signals output from the first and second movement sensors <NUM>, <NUM> while the user responds to this instruction.

The method shown in <FIG> also comprises, at block <NUM>, using the identified one or more correlations to adjust at least one output signal provided by the at least one detector <NUM>.

In some examples using the identified one or more correlations to adjust at least one output signal provided by the detector <NUM> comprises at least partially removing noise from one or both of the measurement signals. The relationship between the two measurement signals can be used to identify noise within the signals and then remove that noise from the measurement signals. This enables more accurate information about the position of the detector <NUM> to be obtained. This more accurate position information can then be used to adjust the output from the detector <NUM>.

The adjusting of the output signal provided by the detector <NUM> may comprise correcting output signals from the detector <NUM>. The corrections could be made to take into account movement and/or changes in position which are measured by the movement sensors <NUM>, <NUM>. This could reduce aberration or blurring in images obtained by an imaging module for example.

In some examples the adjusting of the output signal could comprise associating metadata with the output signal. The metadata could comprise information about the location of the detector <NUM> which may be determined from the measurement signals from the movement sensors <NUM>, <NUM>. As the measurement signals have been corrected to reduce the noise in the signals this may provide more accurate metadata. This could provide for improved outputs from the detectors <NUM>, for example it may enable more realistic and higher quality three dimensional images to be obtained and/or may enable more realistic and higher quality spatial audio to be obtained.

The output signal could initially be associated with metadata comprising information about the location of the detector <NUM> which is determined from the first measurement signals from the first movement sensors <NUM>. This metadata can later, for example in post-processing, be updated with information about the location of the detector <NUM> which is determined from the time series data resulting from averaging the first measurement signal with, at least, the translated second measurement signal.

It is to be appreciated that variations of the method could be used in implementations of the disclosure. For example, in the method of <FIG> a first measurement signal and a second measurement signal are obtained. In other examples more than two measurement signals can be obtained from more than two different movement sensors. Also in the example method of <FIG> only a single detector <NUM> has the output adjusted. In other examples there may be a plurality of detectors <NUM> and the outputs of each of the one or more detectors <NUM>, or a subset of the detectors <NUM>, could be adjusted using the reduced noise measurement signals.

<FIG> illustrates another example system <NUM> that could be used to implement examples of the disclosure. In this example system <NUM> the first device <NUM> is a mobile telephone which is held in the hand <NUM> of a user and the second device <NUM> is a watch which is attached to the user's wrist <NUM>. In this example system <NUM> the watch <NUM> is attached to the same arm as the hand <NUM> that is holding the first device <NUM>. In this case the user's arm <NUM> provides the structure <NUM> that kinetically links the first device <NUM> and the second device <NUM>. The respective movement sensors <NUM>, <NUM>, detectors <NUM> and the apparatus <NUM> would be provided internally of the devices <NUM>, <NUM> and so are not shown in <FIG>.

In the example system <NUM> of <FIG> the user is instructed to make a predefined gesture. In the example of <FIG> the gesture is indicated by the dashed arrows <NUM>. The gesture comprises moving the arm upwards and downwards in a direction that is vertical, or substantially vertical. Other gestures could be used in other examples of the disclosure. In some examples the user could be required to make a sequence of predefined gestures in order to enable sufficient measurement signals to be obtained.

When the user makes the predefined gestures the apparatus <NUM> can use machine learning, or any other suitable process, to determine a kinetic linking between the first device <NUM> and the second device <NUM>. The kinetic linking could provide an indication of the distance between the devices <NUM>, <NUM> and any pivot points <NUM> in the structure.

The kinetic linking could also provide an indication of the axis <NUM> along which the devices <NUM>, <NUM> can be moved.

Once the kinetic linking has been determined this can be used to compare the measurement signals from the first movement sensor <NUM> and the second movement sensor <NUM>. The kinetic linking can give an indication of the noise in the measurement signals as it can give an indication of the signal that should be expected for a given movement. This noise can then be removed from the measurement signals and the corrected measurement signals can be used to adjust the outputs from detectors <NUM> within the first device <NUM>.

Examples of the disclosure therefore provide apparatus <NUM>, systems <NUM> and methods for reducing noise in measurement signals which can then be used to provide higher quality output signals from detectors <NUM>. For example it can enable movement of the detector <NUM> to be accounted for in the output signal.

As machine learning can be used to reduce the noise in the measurement signals this can provide for an efficient way of improving the output signal.

If it is intended to use 'comprise' with an exclusive meaning then it will be made clear in the context by referring to 'comprising only one. ' or by using 'consisting'.

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 unless 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.

Whilst endeavoring in the foregoing specification to draw attention to those features believed to be of importance it should be understood that the Applicant may seek protection via the claims in respect of any patentable feature or combination of features hereinbefore referred to and/or shown in the drawings whether or not emphasis has been placed thereon.

Claim 1:
A device (<NUM>) comprising:
a first movement sensor (<NUM>) configured to provide a first measurement signal indicative of movement and/or position of the first movement sensor;
at least one detector (<NUM>) configured to detect an input and provide at least one corresponding output signal; and
means for:
receiving a second measurement signal from a second movement sensor (<NUM>) which is located apart from the device (<NUM>) and movable relative to the first movement sensor (<NUM>) while kinetically linked to the first movement sensor (<NUM>) by a structure (<NUM>), the second measurement signal indicative of movement and/or position of the second movement sensor;
determining a displacement between the first movement sensor (<NUM>) and the second movement sensor (<NUM>) based on the first measurement signal and the second measurement signal;
translating the second measurement signal based on the displacement between the first movement sensor (<NUM>) and the second movement sensor (<NUM>), wherein the translated second measurement signal simulates a measurement signal as if from the second movement sensor (<NUM>) had it been placed at the location of the first movement sensor (<NUM>) and moved in the same manner as the first movement sensor (<NUM>) during a period of measurement;
determining information about the position and/or a location of the at least one detector (<NUM>) based on the first measurement signal and the translated second measurement signal; and
using the information about the position and/or location of the at least one detector (<NUM>) to adjust at least one output signal provided by the at least one detector (<NUM>).