Adjusting settings of a haircare appliance

Abstract

A method and system comprising obtaining 102 image data representative of an image of hair of a user, analysing 104 the image data to determine hair properties, such as hair thickness, hair alignment, hair lustre, hair colour, hair length, hair curl and / or hair style, and adjusting 106 settings of a haircare appliance (901, fig.9, 1001, fig.10), such as flow rate of an airflow generator (1004, fig.10), temperature of a heater (905, fig.9), duration of a styling sequence and / or a removable attachment (1124, 1126, fig.11) setting, based on the determined properties. The haircare appliance may include a hair property sensor, such as a distance sensor (1012, fig.10), a temperature sensor (907, fig.9) or a moisture sensor, which is calibrated based on the determined properties. A user profile (712-716, fig.7a) may be selected based on the determined properties (702, 704, fig.7a), possibly along with information (706, fig.7a) entered on a user interface, which is then used to adjust the settings (722-726, fig.7a). The image data may be obtained by a camera (1404, fig.14) of a computing device (1432, fig.14), such as a smartphone or a tablet, which may transmit setting values to the haircare appliance.

Description

BACKGROUND Some existing haircare appliances allow a user to adjust settings of the haircare appliance. However, it is desirable to improve upon existing haircare appliances. SUMMARY According to a first aspect of the present invention, there is provided a computer implemented method, the method comprising: obtaining image data representative of an image of hair of a user; analysing the image data to determine one or more properties of the hair of the user; and adjusting one or more settings of a haircare appliance based on the one or more determined properties, thereby to tailor the settings to the hair of the user. This provides for automatic tailoring of one or more settings of the haircare appliance to the hair of the individual user. Through such tailoring, the operation of the haircare appliance can be optimised for the specific hair of the individual user. Analysing the image data representative of an image of the user’s hair allows for the properties of the hair to be objectively determined. This may be more accurate and / or reliable than, for example, relying solely on a user to input the properties of their hair, or to determine appropriate settings adjustments. In turn, this may allow for the settings to be accurately and reliably tailored to the user’s hair. Further, this may improve the ease with which a user can interact with the haircare appliance, as this may reduce or eliminate the need for the user to input hair properties and / or setting adjustments to the haircare appliance. An improved haircare appliance is accordingly provided. Optionally, adjusting the one or more settings comprises adjusting one or more of a flow rate setting and a temperature setting of the haircare appliance. For example, the flow rate setting may set a rate of airflow generated by an airflow generator of the haircare appliance. Adjusting a flow rate setting may allow the haircare appliance to operate in a way that is tailored to the hair of the individual user. For example, the haircare appliance may be a hair dryer comprising an airflow generator configured to generate an airflow that is expelled from the haircare appliance to dry a user’s hair. As another example, the haircare appliance may be a curling device comprising an airflow generator configured to generate an airflow that is expelled around a barrel of the curling device in order to cause a hair to wrap around the barrel. In any case, the airflow settings may be adjusted for optimal performance for the specific hair of the individual user. For example, in the case of a hair dryer, in order to be dried within similar timescales, relatively thick and / or coarse hair may need to be exposed to a relatively higher airflow rate than relatively thin and / or fine hair. In another example, in the case of a curling device where an expelled airflow causes hair to wrap around a barrel of the curling device, relatively higher airflow rate may be needed to cause relatively thick hair to wrap around the barrel as compared to relatively thin hair. Accordingly, in examples, the airflow rate settings may be adjusted to a relatively high airflow rate where the user is determined to have relatively thick and / or coarse hair and at a relatively flow airflow rate where the user is determined to have relatively thin and / or fine hair. As another example, the temperature setting may set a temperature to which a heater of the haircare appliance is heated. Adjusting a temperature setting of the haircare appliance may allow the haircare appliance to operate in a way that is tailored to the hair of the individual user. For example, the haircare appliance may be a hair straightener comprising a heater configured to heat opposing plates between which the user’s hair is pressed in use in order to straighten the user’s hair. As another example, the haircare appliance may be a hair dryer comprising a heater configured to heat an airflow. The heated airflow may then be expelled from the haircare appliance to dry a user’s hair. As another example, the haircare appliance may be a curling device comprising a heater configured to heat a body around which user hair is wrapped in order to set a curl into the user’s hair. In any case, the temperature setting may be adjusted for optimal performance for the specific hair of the individual user. For example, relatively thin hair may be susceptible to heat damage at lower temperatures than relatively thick hair. Accordingly, in examples, the temperature setting may be adjusted to a relatively high temperature where the user is determined to have relatively thick hair and to a relatively low temperature where the user is determined to have relatively thin hair. Optionally, the haircare appliance comprises a styling sequence having a predefined sequence of modes, and adjusting the one or more settings comprises adjusting one or more of a flow rate setting, a temperature setting and a duration of at least one of the modes. For example, the haircare appliance may employ a predefined sequence of operational modes in order to produce a particular effect on the hair. For example, the haircare appliance may comprise a hair curling device that employs a predefined sequence of operational modes in order to set curls into the hair effectively. In any case, the predefined sequence of modes may comprise a first mode comprising operating the haircare appliance according to a first temperature setting and / or a first airflow rate setting for a first duration; and a subsequent second mode of the sequence may comprise operating the haircare appliance according to a second temperature setting and / or a second airflow rate setting for a second duration. Adjusting the settings may comprise adjusting one or more of the first temperature setting, the first airflow rate setting, the first duration, the second temperature setting, the second airflow rate setting, and the second duration. For example, the settings of the styling sequence may be adjusted to be optimized for different hair types. For example, a haircare appliance may employ a predefined sequence of modes to set curls into hair, for example: a wrap mode in which hair is caused to be wrapped around a body of the haircare appliance by a first airflow; followed by a dry mode in which hair wrapped around the body of the haircare appliance is caused to be dried by a second airflow; followed by a cold shot mode in which the hair wrapped around the body of the haircare appliance is caused to be set into a curl by a third airflow. Thick, curly hair is typically relatively hard to cause to wrap around the body and hence for thick, curly hair the flow rate of the first airflow in the wrap mode may be set relatively high in order to cause the hair to wrap around the body effectively. Thick, curly hair also typically retains a relatively high level of moisture after becoming wet and hence for thick, curly hair the dry mode may be set to last for a relatively long time in order to dry the hair effectively. Thick, curly hair is also typically relatively difficult to penetrate with an airflow and hence for thick, curly hair the cold shot mode may be set to last for a relatively long time in order to effectively set the curl into the hair. Accordingly, where it is determined that the hair is thick and curly, the settings may be adjusted so that the flow rate of the wrap mode is relatively high, and the duration of the dry mode and the cold shot mode are relatively long. As another example, fine hair is typically relatively easy to cause to wrap around the body and hence for fine hair the flow rate of the first airflow in the wrap mode may be set relatively low. Fine hair also typically retains a relatively low level of moisture after becoming wet and hence for fine hair the dry mode may be set to last for a short time. Additionally, fine hair is typically less able to retain curls, but this can be mitigated by applying relatively higher temperatures in the dry mode. Hence for fine hair the temperature of the second airflow in the dry mode may be set relatively high. Fine hair also typically has a relatively low thermal mass and is typically relatively easier to penetrate with an airflow and hence for fine hair the cold shot mode may be set to last for a relatively short time. Accordingly, where it is determined that the hair is fine, the settings may be adjusted so that the flow rate of the wrap mode is relatively low, so that the duration of the dry mode is relatively short and / or the temperature of the second airflow in the dry mode is relatively high, and so that the duration of the cold shot mode is relatively short. Other examples are possible. Optionally, the haircare appliance comprises an airflow generator for generating an airflow, and a heater for heating the airflow, and wherein adjusting the flow rate setting comprises controlling the airflow generator to adjust the flow rate of the airflow, and adjusting the temperatures setting comprises controlling the heater to adjust the temperature of the airflow. For example, the haircare appliance may be a hair dryer comprising an airflow generator configured to generate an airflow, and a heater configured to generate the airflow. The heated airflow may be expelled from the haircare appliance to dry a user’s hair. As another example, the haircare appliance may be a curling device comprising an airflow generator configured to generate an airflow that is expelled around a barrel of the curling device in order to cause hair to wrap around the barrel; and a heather configured to heat the airflow before it is expelled. In any case, the temperature setting and the airflow rate setting may be adjusted for optimal performance for the specific hair of the individual user, for example as per the examples described above. Optionally, the haircare appliance comprises a sensor for sensing a property of the hair of the user, and adjusting the one or more settings comprises adjusting a calibration setting of the sensor. For example, this may allow a sensor of the haircare appliance to provide accurate measurements even when used by different users with different hair properties. For example, a particular calibration setting may correspond to a particular look-up table used to convert an output of the sensor to a particular sensed quantity or value. In these cases, adjusting the calibration setting may comprise changing the particular look-up table that is used with a particular sensor. Accordingly, the particular sensed quantity may be accurately determined in different hair environments. Optionally, the sensor is one of a distance sensor for sensing a distance of the hair of the user, a temperature sensor for sensing a temperature of the hair of the user, and a moisture sensor for sensing a moisture content of the hair of the user. For example, the calibration setting may set a distance calibration according to which a distance sensor of the haircare appliance is calibrated. For example, the haircare appliance may be a hair dryer comprising a distance sensor configured to measure a distance from the hair dryer to the user’s hair or head. For example, the distance sensor may comprise a lightbased sensor, such as a time-of-flight sensor, configured to measure a distance from the hair dryer to the user’s hair or head. When used with a user having curly hair, the output of the sensor may be systematically lower than the output of the sensor when used with a user having straight hair. Accordingly, in order to determine an accurate measurement of the distance from the sensor to the user’s hair or head, it may be beneficial to use a different calibration for different hair types. Accordingly, in examples, a distance calibration may be set relatively low when the user is determined to have relatively curly hair and may be set relatively high when the user is determined to have relatively straight hair. In some examples, there may be different calibration look-up tables to convert a reading of the distance sensor to a particular distance value, each look-up table having been determined for a different hair type. In these examples, adjusting a calibration setting may comprise selecting the particular calibration look-up table that corresponds to the hair type determined for the user. As another example, where a time-of-flight sensor is used to determine the distance of the haircare appliance from the user’s hair, hair having different three-dimensional forms may result in different readings or ranges of readings. For example, straight hair may provide a relatively flat target and hence may result in a relatively low number, or a single, time-of-flight reading for a given position of the haircare appliance relative to the user’s hair. However, other hair, such as kinky, curly or coily hair, may provide a relatively more complex three-dimensional form, and hence may result in a wider range of time-of-flight readings for a given position of the haircare appliance relative to the user’s hair, as well as a wider range of time-of flight readings as the haircare appliance is moved relative to the user’s hair. Accordingly, determining the type and / or form of the hair of the user may allow different calibrations to be applied to the readings in order to determine plausible positions of the haircare appliance relative to the user’s hair. For example, where the hair is determined to be relatively straight, a first reading filtering and / or averaging algorithm may be applied to determine the distance output, whereas where the hair is determined to be relatively kinky, curly or coily, a second reading filtering and / or averaging algorithm may be applied to determine the distance output. For example, where the hair is determined to be relatively kinky, curly or coily, filtering may be applied to remove the outer edges of the range of sensor readings, in order to more accurately determine the distance of the hair from the haircare appliance. Other examples are possible. As another example, the calibration setting may set a temperature and / or moisture calibration according to which a temperature and / or moisture sensor of the haircare appliance is calibrated. For example, a haircare appliance may comprise a temperature sensor configured to sense a temperature of the user’s hair. For example, haircare appliance may comprise an Infra-Red (IR) sensor, such as an IR thermopile, a multi-pixel IR sensor and / or a hyperspectral sensor. For example, the IR sensor may passively read the spectral emission from hair in order to infer the temperature. This inference may involve the emissivity of the hair. However, hair having different properties may have different emissivity. For example, hair of different types, colours, ages, chemical alterations and damage may have different emissivity, and therefore the relationship or calibration between intensity of detected radiation and temperature may be different. Accordingly, by determining one or more properties of the user’s hair such as type, colour, age, chemical alteration and damage, an appropriate emissivity can be chosen, and hence the IR sensor may be appropriately calibrated. As a result, the accuracy of the temperature measurement may be improved. As another example, a haircare appliance may comprise a moisture sensor configured to measure a moisture of the user’s hair. For example, the moisture of a user’s hair may be inferred from a temperature response of a heated surface when put in contact with the user’s hair. For example, the temperature of the heated surface may drop relatively quickly when placed in contact with relatively moist hair, as compared to relatively dry hair. As another example, the moisture of a user’s hair may be inferred from a temperature response of the user’s hair, that is, a change in temperature of the user’s hair given a particular applied heat. For example, the temperature of relatively moist hair may increase relatively slowly for a given applied heat as compared to relatively dry hair. When the moisture sensor is used with relatively thick and / or coarse hair, the output of the moisture sensor may systematically indicate a higher moisture content than when used with relatively thin and / or fine hair. Accordingly, in order to determine an accurate measurement of the moisture of a user’s hair, it may be beneficial to use a different calibration for different hair types. Accordingly, in examples, a calibration for the moisture sensor may be set relatively low (that is, to indicate a relatively lower moisture content for a given sensor reading) when the user is determined to have relatively thick and / or coarse hair, and may be set relatively high (that is, to indicate a relatively higher moisture content for a given sensor reading) when the user is determined to have relatively thin and / or fine hair. In some examples, there may be different calibration look-up tables to convert a reading of the sensor to a moisture value, each look-up table having been determined for a different hair type. In these examples, adjusting a calibration setting may comprise selecting the particular calibration look-up table that corresponds to the hair type determined for the user. Optionally, adjusting the one or more settings comprises adjusting one or more attachment settings employed by the haircare appliance when a removable attachment is attached to the haircare appliance. For example, the haircare appliance may be configured to operate according to different settings according to whether a removeable attachment and / or which of a plurality of removeable attachments is attached to the haircare appliance. For example, the haircare appliance may be a hair dryer having a one or a plurality of different attachments that can be individually attached to an airflow outlet of a main body of the hair dryer in order to disperse or concentrate the airflow expelled therefrom in use in different ways. The haircare appliance may be configured to use a first set of temperature and / or airflow rate settings if no attachment (or a first of a plurality of attachments) is attached; and a second set of temperature and / or airflow rate setting if an attachment (or a second of a plurality of attachments) is attached. The settings that are used when a particular removable attachment is attached to the haircare appliance may be adjusted depending on the particular properties of the user’s hair. For example, when a diffuser is attached to a hairdryer, the haircare appliance may operate according to a particular temperature setting and airflow rate setting. These settings may be adjusted depending on the properties of the hair of the user. For example, if it is determined that the user has relatively curly hair, the temperature setting may be increased, and the airflow rate setting may be decreased, in order to provide optimal performance for diffusing curls. As another example, where it is determined that the user has hair that has relatively tight coils and / or damaged hair, and hence which is relatively prone to frizz, the airflow rate setting may be decreased in order to reduce frizz. Optimum performance for the specific hair of the individual user may therefore be provided. Other examples are possible. Optionally, adjusting the one or more settings comprises: selecting a particular user profile from a plurality of user profiles based on the one or more determined properties; and adjusting the one or more settings based on the selected user profile. This may allow for settings adjustments appropriate for an individual user to be efficiently determined. For example, a particular user profile, for example including a particular hair type and condition, may be associated with particular adjustments to settings of the haircare appliance that will provide optimum performance for a user having that particular user profile. Determining the particular user profile may accordingly allow those particular adjustments appropriate for the individual user to be identified and implemented in an efficient manner. In some examples, selecting the user profile may comprise comparing one or more of the determined properties with a respective one or more properties specified in each of a plurality of prestored user profiles. This may provide for efficient selection of the appropriate user profile. The user profile selected based on the comparison. For example, the pre-stored user profile among the plurality that specifies properties that are the most similar to the determined properties may be selected as the user profile. This may help ensure the most appropriate of the pre-stored user profiles is selected. In other examples, the profile for the user may be selected using a trained machine learning model. For example, a machine learning model may have been trained to map a particular set of input properties onto a particular one of a plurality of profiles. Using a trained machine learning model may help provide flexible and / or robust determination of the profile for the user. Optionally, the method comprises obtaining information entered on a user interface, and selecting the user profile based additionally on the information. This may allow for an appropriate profile for the user to be determined more accurately. For example, this additional information may include user age, user ethnicity, a desired style. This information may be combined with the objective hair properties to select an appropriate profile for the user, such as a particular hair type and condition. Optionally, the image data comprises data representative of an image of hair fibres of the hair of the user, and the one or more properties comprise one or more of hair fibre thickness, hair fibre alignment, hair fibre lustre, and hair fibre colour. This may allow properties of individual fibres, or a collection of a few individual fibres, to be determined. This may allow for small-scale properties of the hair to be accurately determined, which may, in turn, allow for the settings to be accurately and appropriately tailored to the individual user. For example, the hair fibre thickness and alignment may be used to determine a hair type of the user. In examples, the image of the user’s hair may be captured by a camera of the haircare appliance, or by a camera of a computing device external to and in communication with the haircare appliance, such as a mobile device or other device. For example, the haircare appliance may be a hair straightener comprising opposing plates between which hair is pressed in use in order to straighten the hair. In these examples, the camera may be located within one of the plates and configured to capture an image of the hair fibres when the hair is pressed between the plates. As another example, the external computing device, such as a smart-phone or other device, may comprise a camera configured to capture an image of hair fibres of the user when the camera is brought up to the hair fibres. For example, in either case, the camera may comprise a microscopic lens so that the image can be appropriate focused on the hair fibres. Optionally, the image data comprises data representative of an image of a head of the user, including the hair of the user, and the one or more properties comprise one or more of hair length, hair curl, and hair style. This may allow properties of the head of hair of the user to be determined. This may allow for larger-scale or macroscopic properties of the hair to be accurately determined, such as hair length, hair curl, and hair style, which may, in turn, allow for the settings to be accurately and appropriately tailored to the individual user. In examples, the image of the user’s hair may be captured by a camera of the haircare appliance, or by a camera of a computing device external to and in communication with the haircare appliance, such as a mobile device or other device. For example, the haircare appliance may be a hair dryer comprising a camera facing in the same direction as the expelled airflow and configured to capture one or more images of the user’s head prior to or during use of the hairdryer. As another example, the external computing device, such as a smart-phone or other device, may comprise a camera configured to capture one or more images of the head of the user when the camera faces towards the user. In examples, multiple images of the user’s head may be captured from different angles, which may provide for more complete information on the user’s hair. In cases where multiple images are captured, data from these images may combined into a single set of image data representative of the user’s hair. In examples, the camera may be a depth camera (and accordingly the captured images may be depth images). This may provide for a three-dimensional representation of the user’s hair. This may provide further detail on the user’s hair, allowing for more accurate determination of the properties of the user’s hair. Optionally, the method comprises determining one or more setting values based on the one or more determined properties, transmitting the one or more setting values to the haircare appliance, receiving at the haircare appliance the one or more setting values, and adjusting the one or more settings of the haircare appliance based on the received one or more setting values. For example, determining one or more setting values based on the one or more determined properties and transmitting the one or more setting values to the haircare appliance may be performed by a computing device external to and in communication with the haircare appliance. For example, the computing device may be a mobile device such as a smart-phone or other device. Receiving the one or more setting values and adjusting the one or more settings based on the received one or more setting values may be performed by the haircare appliance. This may provide that processing involved in determining the setting values based on the one or more determined properties need not be performed by a processing unit of the haircare appliance itself, and may be performed instead, for example, by an external computing device, such as a smart phone, which may have greater processing power than the haircare appliance. This may help reduce the processing burden on the haircare appliance, whose performance may be limited. This may help improve the speed at which the method can be performed. Alternatively or additionally, this may help allow for the improvements in the operation of the haircare appliance to be provided, without necessarily increasing the processing capability of the haircare appliance. This may be a cost-effective arrangement. Optionally, the method comprises operating the haircare appliance according to the adjusted one or more settings. This provides operation of the haircare appliance that is tailored to the specific hair of the individual user. An improved haircare appliance is provided. According to a second aspect of the present invention, there is provided a system comprising one or more processing units configured to perform the method according to the first aspect. For example, the system may comprise one processing unit configured to perform the method according to the first aspect. In this case, the processing unit may store instructions which, when executed by the processing unit cause the processing unit to perform the method according to the first aspect. As another example, the system may comprise a first processor configured to perform some of the steps of the method according to the first aspect, and one or more second processors in communication with the first processing unit and configured to perform the other steps of the method according to the first aspect. In this case, the processing units may store respective instructions which, which executed by the respective processing units, cause the respective processing units to perform the respective steps of the method according to the first aspect. In either case, the instructions may be stored on a computer readable medium. In some examples, the system comprises a haircare appliance comprising at least one of the processing units. In some examples, the system comprises a computing device, separate to and in communication with the haircare appliance, comprising at least one of the processing units. For example, the method according to the first aspect may be performed by the processing unit of the haircare appliance alone. For example, the haircare appliance may store the settings according to which it is to operate, and the processing unit of the haircare appliance may adjust these settings as appropriate. As another example, the method according to the first aspect may be performed by the processing unit of the separate computing device (for example a mobile device or server) alone. For example, the computing device may store the settings according to which the haircare appliance is to operate, and the haircare appliance may communicate with the computing device in order to determine the settings. In this example, the processing unit of the computing device may adjust the settings stored at the computing device as appropriate. In another example, the settings may be stored at the haircare appliance, and the computing device communicate with the haircare appliance to adjust the settings of the haircare appliance. In some examples, the haircare appliance and the computing device may together perform the method according to the first aspect. For example, some of the steps may be performed by the processing unit of the haircare appliance, and the remaining steps may be performed by the computing device. In some examples, the haircare appliance may comprise a camera configured to capture the image of the hair of the user. The camera may output image data representative of the image. The image data representative of the image may be provided to the processing unit of the haircare appliance (or to the processing unit of the computing device in communication with the haircare appliance), and the respective processing unit may thereby obtain the image data. In some examples, the computer device comprises a camera configured to capture the image of the hair of the user. The camera may output image data representative of the image. The image data representative of the image may be provided to the processing unit of the computing device (or to the processing unit of the haircare appliance in communication with the computing device), and the respective processing unit may thereby obtain the image data. In any case, a system which allows for the tailoring of one or more settings of a haircare appliance to the hair of an individual user is provided for. Optionally, the computing device comprises a camera for obtaining the image data, and the at least one processing unit of the computing device is configured to: analyse the image data to determine the one or more properties of the hair of the user; determine one or more setting values based on the one or more determined properties; and transmit the one or more setting values to the haircare appliance, and wherein the at least one processing unit of the haircare appliance is configured to adjust the one or more settings of the haircare appliance based on the received one or more setting values. This may help reduce the processing burden on the processing unit of the haircare appliance, whose performance may be limited. This may help improve the speed at which the method can be performed. Alternatively or additionally, this may help allow for the improvements in the operation of the haircare appliance to be provided, without necessarily increasing the processing capability of the processing unit of the haircare appliance. This may be a cost-effective arrangement. Optionally, the haircare appliance comprises an airflow generator for generating an airflow, and a heater for heating the airflow, and wherein the at least one processing unit of the haircare appliance is configured to control the airflow generator to adjust a flow rate setting of the airflow, and to control the heater to adjust a temperature setting of the airflow based on the received one or more setting values. Accordingly, the temperature setting and the airflow rate setting may be adjusted for optimal performance for the specific hair of the individual user, for example as per the examples described above. Optionally, the computing device is a handheld device, and wherein optionally the handheld device is a smartphone or computing tablet. In examples, the computing device being a handheld device may allow for a user to capture images of the user’s hair in a convenient manner. In some examples, the computing device may itself be a haircare appliance, such as a smart hairbrush. For example, the computing device may be a hairbrush and the haircare appliance may be a hairdryer. This may allow, for example, for one haircare appliance (which may anyway interact with the hair of the user) to control or adjust the settings of another haircare appliance. This may provide a convenient arrangement. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a flow diagram illustrating a method for adjusting one or more settings of a haircare appliance, according to an example, Figure 2 is an illustrative image of hair fibres of a user, according to an example; Figure 3 is a schematic diagram illustrating an image of a head of a user including the hair of the user, according to an example; Figure 4 is a radial plot of hair fibre orientation, according to an example; Figure 5 is a histogram of a distribution of hair fibre thickness, according to an example; Figure 6 is two illustrative images of hair fibres of different users having different hair shine, according to an example; Figure 7a is a schematic diagram illustrating the determination of a setting of a haircare appliance based on one or more properties, according to an example; Figure 7b is a schematic diagram illustrating the determination of a setting of a haircare appliance based on one or more properties, according to another example; Figure 8 is a schematic diagram illustrating the determination of settings of a haircare appliance based on one or more properties, according to an example; Figure 9 is a schematic diagram illustrating a perspective view of hair straighteners according to an example; Figure 10 is a schematic diagram illustrating a side view of a hair dryer according to an example; Figure 11 is a schematic diagram illustrating a side view of a hair dryer according to another example; Figure 12 is a schematic diagram illustrating a side view of a hair curler according to an example; and Figure 13 is a schematic diagram illustrating a system according to an example; Figure 14 is a schematic diagram illustrating a system according to another example. DETAILED DESCRIPTION Referring to Figure 1, there is illustrated a method of adjusting one or more settings of a haircare appliance. As described herein with reference to Figures 9 to 14, the method may be performed by one or more processing units 912, 1006, 1106, 1310, 1206, 1310, 1406, 1436 of a system 900, 1000, 1100, 1200, 1300, 1400. In some examples, the method may be performed by a processing unit 912, 1006, 1106, 1310, 1206, 1310, 1406 of a haircare appliance 901, 1001, 1101, 1201, 1300, 1402. In some examples, the method may be performed by a processing unit 1436 of a computing device 1432 that is separate to and in communication with the haircare appliance 1402. In some examples, the method may be performed together by the processing unit 1406 of the haircare appliance 1402 and the processing unit 1436 of the computing device 1432. In any case, in broad overview, the method comprises: - in step 102, obtaining image data representative of an image of hair of a user; - in step 104, analysing the image data to determine one or more properties of the hair of the user; and in step 106, adjusting one or more settings of a haircare appliance based on one or more of the determined properties. As mentioned, in step 102, the method comprises obtaining image data representative of an image of hair of a user. In some examples, the image may comprise an array of pixels each having one or more pixel values. The image data representing the image may comprise the one or more pixel values for each pixel (or a sub-set of the pixels) of the image. In some examples, the image may be a grey-scale image captured by a camera. In this case, or otherwise, the pixel values for each pixel may comprise an intensity value indicative of an intensity with which light is incident at the pixel location when the image was captured. In some examples, the image may be a colour image captured by a colour camera. In this case, or otherwise, the pixel values for each pixel may comprise one or more values indicative of a colour of light incident at the pixel location when the image was captured. For example, for each pixel there may be a Red, Green, and Blue (RGB) value indicative of the relative intensity of Red, Green and Blue coloured light incident at the pixel location when the image was captured. In some examples, the image may be a depth image, for example captured by a depth camera. In this case, or otherwise, the pixel values for each pixel may comprise a depth value indicative of a distance of the object represented by the pixel from the depth camera. In any case, image data is obtained that is representative of an image of hair of a user. Figure 2 illustrates an image 202 of hair of a user, according to an example. In this example, the image 202 is an image of hair fibres 204 of hair of a user. For example, the hair fibres 204 may be of a portion of a hair tress of a user. As described herein with reference to Figures 9 to 14, the image 202 of the user’s hair may be captured by a camera of a haircare appliance, or by a camera of a computing device external to and in communication with the haircare appliance, such as a mobile device or other device. For example, as described herein with reference to Figure 9, the haircare appliance may be a hair straightener 901 comprising opposing plates 904, 906 between which hair is pressed in use in order to straighten the hair. In these examples, the camera 902 may be located within one of the plates 906 and configured to capture an image 202 of the hair fibres when the hair is pressed between the plates 904, 906. As another example, as described with reference to Figure 14, the external computing device 1432, such as a smart-phone or other device, may comprise a camera 1434 configured to capture an image 202 of hair fibres of the user when the camera is brought up to the hair fibres. For example, in any case, the camera may comprise a microscopic lens so that the image can be appropriately focused on the hair fibres. Figure 3 illustrates an image 302 of hair 306 of a user, according to another example. In this example, the image 302 is an image of the head 304 of the user, including the hair 306 of the user. As described herein with reference to Figures 9 to 14, the image 302 of the user’s hair may be captured by a camera of a haircare appliance, or by a camera of a computing device external to and in communication with the haircare appliance, such as a mobile device or other device. For example, as described herein with reference to Figure 10, the haircare appliance may be a hair dryer 1001 comprising a camera 1012 facing in the same direction as the hair-drying airflow 1014 and configured to capture one or more images 302 of the user’s head prior to or during use of the hairdryer. As another example, as described with reference to Figure 14, the external computing device 1432, such as a smart-phone or other device, may comprise a camera 1434 configured to capture one or more images 302 of the head of the user when the camera faces towards the user. In some examples, multiple images 302 of the user’s head may be captured from different angles. In such cases, data from the images may combined into a single set of image data representative of the user’s hair, for example using image stitching techniques. In some examples, the image or images of the hair of the user may be somewhere between the examples of Figure 2 and 3. In any case, image data representative of an image of hair of a user is obtained. Returning to Figure 1, as mentioned, the method comprises, in step 104, analysing the image data to determine one or more properties of the hair of the user. In examples, the image data may be analysed in a variety of different ways in order to determine a variety of different properties of the hair of the user. Examples of properties that may be determined from the analysis include hair fibre alignment indicative of an extent to which hair fibres are waved or curled; hair fibre thickness indicative of a thickness of the hair fibres of the user; hair shine or lustre indicative of an extent to which the hair reflects light; split-end factor indicative of an extent to which the hair fibres have split-ends; hair colour indicative of a colour of the hair; hair length indicative of a length of the hair; hair curl indicative of an extent to which the hair of the user is curled; and hair style indicative of a style in which the hair is formed. In some examples, analysing the image data may comprise applying one or more image processing algorithms to the image data to determine the one or more properties. For example, one or more rules may be applied to the image data or a processed version of the image data in order to determine one or more properties. As mentioned, an example property is hair fibre alignment indicative of an extent to which hair fibres are waved or curled. In some examples, determining the hair fibre alignment may comprise applying a hair fibre alignment algorithm to the data representing the image. For example, the hair alignment algorithm may comprise identifying a plurality of hair fibres or portions of hair fibres in the image or a portion of the image 202, 302, determining an orientation of each hair fibre or hair fibre portion relative to a reference orientation, and determining hair fibre alignment based on the determined orientations. In examples, individual hair fibres or portions of hair fibres may be identified using a Hough Line transform, a two-dimensional Fast Fourier transform, marching squares and / or other contouring or segmentation techniques. In examples, the algorithm may comprise determining a spread of the orientations and determining the hair fibre alignment based on the spread of orientations. For example, if the spread of orientations is relatively narrow then it may be determined that the hair is relatively straight, whereas if the spread of orientations is relatively wide then it may be determined that the hair is relatively wavy or curly. Referring to Figure 4, there is illustrated a result of applying a hair alignment algorithm to image data representing the image 202 of Figure 2. Figure 4 shows a radial graph, on which is plotted the distribution 404 of orientations of each identified hair fibre or portion of hair fibre in the image 202 of Figure 2. As indicated in Figure 4, this distribution 404 is associated with a spread (e.g., a standard deviation) 406 of 22.5 degrees. This spread indicates that the hair is neither poker straight nor curly but rather has a slight wave. In this example, the determined property may be the spread of orientations, e.g., 22.5 degrees, or the inferred hair type, e.g., slight wave. In some examples, hair type may be indicated by an alphanumeric assignment, such as la-4c. For example, 1 may indicate straight, 2 may indicate wavy, 3 may indicate curly, and 4 may indicate very curly to kinky. Within each numeric assignment, there may be three sub-assignments a-c. For example, la indicated poker straight, lb indicates straight with a slight wave, and 1c indicates straight with a slight wave and some visible S-waves. For example, based on the spread of orientations of 22.5 degrees, the hair in Figure 2 may be assigned hair type 1, or more specifically, lb for example. As mentioned, an example property is hair fibre thickness indicative of a thickness of the hair fibres of the user. In some examples, determining the hair fibre thickness may comprise applying a hair fibre thickness algorithm to the data representing the image. For example, the hair fibre thickness algorithm may comprise identifying a plurality of hair fibres or portions of hair fibres in the image or a portion of the image 202, 302, determining a width or thickness of each hair fibre or hair fibre portion relative to a reference orientation, and determining hair fibre thickness based on the determined thicknesses. Individual fibres or fibre portions may be identified as described above. The thickness of a fibre may be determined, for example, based on the number of pixels that the fibre spans in its width, which may be calibrated to thickness in another unit, such as meters. In examples, the algorithm may comprise determining the average thickness of the identified fibres or fibre portions and determining the hair fibre thickness as the average thickness. In some examples, the standard deviation of the thickness may also be determined, which may be indicative of the spread of hair fibre thicknesses of the hair of the user. Referring to Figure 5, there is illustrated a result of applying a hair fibre thickness algorithm to image data representing the image 202 of Figure 2. Figure 5 shows a histogram. The x axis indicates a succession of bins of 10 micrometres width (e.g., 20-30 micrometres; 30-40 micrometres; 40-50 micrometres etc.) and the y axis indicates for each bin the number of hair fibres or hair fibre portions of the image 202 determined as having a thickness falling within the bin. For example, a hair fibre having a thickness determined as 25 micrometres would add 1 to the count of the 20-30 micrometre bin. The histogram of Figure 5 shows that there is a distribution 502 of hair thicknesses, having an average hair fibre thickness of 60 micrometres. This may indicate the hair is of a medium thickness. In this example, the determined property may be the determined average fibre thickness, e.g., 60 micrometres, or an inferred hair thickness type, e.g., medium. In some examples, hair thickness type may be indicated by label, such as coarse, medium and fine. For example, coarse may indicate relatively thick hair fibres, fine may indicate relatively think hair fibres, and medium may indicate hair fibres of an intermediate thickness. For example, based on an average hair fibre thickness of 60 micrometres, the hair thickness type of the hair of Figure 2 may be determined as ‘medium’. As mentioned, an example property is hair shine or lustre indicative of an extent to which the hair reflects light. In some examples, determining the hair shine may comprise applying a hair shine algorithm to the data representing the image. For example, the hair shine algorithm may comprise determining an extent to which the hair represented by the image data reflects light incident on it. In examples, the incident light may be provided by background light and / or by a light source provided with the camera. In examples, applying the hair shine algorithm may comprise determining a distribution of intensity values of the pixels of the image. For example, where the distribution indicates that some of the pixels of the image (e.g., pixels of a certain region of the image) have a relatively high intensity value relative to the other pixels, this may indicate that the hair shine is relatively high. On the other hand, where the distribution indicates that most or all of the pixels of the image have a relatively similar intensity value, this may indicate that the hair shine is relatively low. In some examples, a High Pass Fast Fourier Transform filter may be applied to the data representing the image, for example in order to sharpen the image, before the hair shine or lustre is determined. In some examples, the image may have been captured under certain lighting conditions and / or under conditions in which the hair fibres are physically bent, in order to accentuate the hair shine or lustre. Referring to Figure 6, there are illustrated two images 602 and 604. A first 602 of the images is an image of hair having relatively low shine, and a second 604 of the images is an image of hair having a relatively high shine. As can be seen, the second image 604 comprises an area 606 of relatively high pixel intensity value, indicating that the hair has a relatively high shine. In this example, the determined property may be the determined intensity value distribution, or a value representative thereof (such as a width of the distribution), or an inferred hair shine level. In some examples, hair shine level may be indicated by label, such as low, medium and high. For example, low may indicate relatively low fine, high may indicate relatively high shine, and medium may indicate an intermediate shine. For example, based on applying a hair shine algorithm to the hair of Figure 2, the hair shine may be determined as ‘high’. As mentioned, an example property is split-end factor indicative of an extent to which the hair fibres have split-ends. In some examples, determining the split-end factor may comprise applying a split-end algorithm to the data representing the image. For example, the split-end algorithm may comprise identifying a plurality of hair fibres or portions of hair fibres in the image or a portion of the image 202, 302, determining a number or proportion of the identified hair fibres whose ends are split, and determining the split-end factor based on the determined number or proportion. Individual fibres or fibre portions may be identified as described above. Whether or not a hair fibre has a split end may be determined by a continues hair fibre at some point along its length becomes two separate hair fibres. In this example, the determined property may be the determined number or proportion of hair fibres having split-ends, e.g., 50% or an inferred split-end factor type, e.g., low, medium, high. As mentioned, an example property is hair colour indicative of a colour of the hair. In some examples, determining hair colour comprises determining an average pixel colour value for the pixels or a portion of the pixels of the image (e.g., an average RGB value). In some examples, determining hair colour may comprise mapping the determined pixel colour value onto one of a plurality of pre-defined colours. For example, the plurality of pre-defined colours may comprise ‘brown’, ‘red’, and ‘blue’. In some examples, determining hair colour may comprise determining whether the hair has been artificially coloured. For example, if the determined colour is ‘blue’, or another hair colour that does not occur naturally, it may be inferred that the hair has been coloured. As mentioned, an example property is hair length indicative of a length of the hair. In some examples, the hair length may be determined from one or more images of the type illustrated in Figure 3. For example, image segmentation may be applied to segment the face of the user and the hair of the user. The length of the hair may then be determined relative to the length of the face of the user, for example. In some examples, hair length may be determined from one or more images of the type illustrated in Figure 2. For example, in some examples, the hair may be relatively short and hair fibres from root to tip and within the field of view of the camera and hence as represented in the image. In this case, the length of the hair may be determined based on a number of pixels over which the hair fibres extend. As another example, hair length may be determined from a succession of images of hair fibres. For example, the camera may be moved along the hair tress from towards the root of the hair to towards the tips of the hair, and the length of hair may be inferred. For example, a specific point or feature may be tracked in a succession of images as the camera is moved along the hair. This may be repeated each time a new portion of the hair tress enters the image field of view as the camera is moved along the hair. The number of pixels over which the hair extends may be converted into a distance, which in turn may represent the length of the hair. In some examples, the hair length may be determined as a specific length, such as in centimetres, or a label inferred from the specific length, such as one of ‘short’, ‘medium’ and Tong’. As mentioned, an example property is hair style indicative of a style in which the hair is formed. In some examples, the hair style may be determined from one or more images of the type illustrated in Figure 3. For example, image segmentation may be applied to segment the hair from the image. The shape of the hair segment may then be analysed to infer a hair style. For example, the hair segment may be compared to a database of hair styles in order to select a hair style based on a shape similarity. In some examples the hair style may be determined from one or more images of the type illustrated in Figure 2. For example, based on a length and / or type of hair determined as described above, a particular hair style may be inferred. In some examples, analysing the image data may comprise applying one or more trained machine learning models to the image data to determine the one or more properties. For example, each trained machine learning model may be a trained regression or classification machine learning model. For example, a machine learning model such as a neural network may have been trained to, based on input image data representing hair of a user, output one or more properties of the hair (e.g., any of the properties described above). For example, the machine learning model may have been trained using supervised learning techniques using training data. For example, the training data may comprise, for example, a plurality of sets of training image data, each set representative of an image of hair of a user, and each set being associated with a ground-truth hair property (e.g., the actual hair thickness of the hair represented in the image). In this case, the training may involve adjusting parameters of the machine learning model (such as adjusting weights of nodes of layers of the neural network, for example using backpropagation) to optimize a loss function between the property predicted by the machine learning model for each input training image data set (e.g., the hair thickness predicted by the model) and the respective ground truth property associated with each training data set (e.g., the actual hair thickness). In some examples, there may be a trained machine learning model for each of the one or more determined properties (e.g., one each for hair thickness, hair alignment, etc.). In some examples, one machine learning model may be configured to determine multiple properties of the hair (e.g., each of hair thickness, hair alignment, etc). In any case, the obtained image data is analysed to determine one or more properties of the hair of the user. Returning to Figure 1, as mentioned, the method comprises, in step 106, adjusting one or more settings of the haircare appliance based on one or more of the determined properties. As described herein with reference to Figures 9 to 14, in examples, adjusting the one or more settings may comprise adjusting one or more of a flow rate setting and a temperature setting of the haircare appliance; adjusting a duration of at least one mode of a styling sequence; adjusting a calibration setting of a sensor of the haircare appliance; and / or adjusting one or more attachment settings employed by the haircare appliance when a removable attachment is attached to the haircare appliance.. Other settings are possible and may be adjusted. In some examples, the method comprises operating the haircare appliance according to the adjusted one or more settings. In some examples, the one or more settings may be adjusted directly based on the determined property. For example, the one or more determined properties may be mapped directly onto a particular value of a particular setting, and the particular setting of the haircare appliance may be adjusted to be at the particular value. For example, where the hair fibre thickness is determined to be X micrometres, a heater setting that sets the temperature to which a heater of the haircare appliance is heated may be adjusted to be at Y degrees Celsius. However, in some examples, adjusting the one or more settings may comprise, selecting a particular user profile from a plurality of user profiles based on the one or more determined properties, and adjusting the one or more settings based on the selected user profile. For example, each particular user profile may be associated with one or more values of one or more settings that are appropriate for a user having hair that fits the particular user profile. Referring to Figure 7a, there is illustrated an example of selecting a particular one 714 of a plurality of user profiles 712-716 based on one or more of the determined properties 702, 704 of the hair of the user. In this example, each of the plurality of user profiles 712-716 is pre-stored. Selecting the particular user profile 714 for the user comprises comparing one or more of the determined properties 702, 704 with a respective one or more properties specified in each of the plurality of pre-stored user profiles. The user profile for the user 714 may be selected by selecting a particular one 714 of the profiles 712-716 based on the comparison. For example, the pre-stored profile 714 among the plurality 712-716 that specifies properties that are the most similar to the determined properties 702, 704 may be selected as the user profile for the user. In some examples, different weights may be attributed to different properties, and the selection may be based on the weights. For example, a score may be generated for each profile 712 based on (i) the extent to which the determined properties match with the properties specified in the profile, and (ii) the weight attributed to each specified property. For example, for each profile 712-716 the score may be a weighted sum (that is, weighted on the basis of the weights attributed to each property) of the similarity of the determined property to the specified property. In this case, the profile with the highest weighted sum may be selected as the profile for the user. In some examples, each user profile 712-716 may specify a respective different range for each of one or more properties, and the profile for the user 714 may be determined by selecting a particular one 714 of the profiles 712-716 based on whether and / or the extent to which the determined properties fall within the respective ranges specified by the respective profiles 712-716. For example, if the determined properties fall within 4 of the ranges specified by a first profile 714 but only 3 of the ranges specifies by a second profile 712, the first profile 714 may be selected as the profile for the user. As a specific example, and referring to Figure 7a, the haircare appliance may be a hair straightener, and the one or more determined properties of the user’s hair may be a hair fibre thickness 702 of 60 micrometres and a hair fibre alignment spread 704 of 22.5 degrees. A first profile 712 (e.g., corresponding to a first hair type) may specify a hair fibre thickness range of 20-50 micrometres and a hair fibre alignment spread range of 0-20 degrees. This first profile 712 may be associated with a relatively low hair straightener heater temperature setting 722 of, say, 130 degrees Celsius, in order to prevent damage to the fine and relatively straight hair represented by this first profile 712. A second profile 714 (e.g., corresponding to a second hair type) may specify a hair fibre thickness range of 50-80 micrometres and a hair fibre alignment spread range of 20-40 degrees. This second profile 714 may be associated with an intermediate hair straightener heater temperature setting 724 of, say, 140 degrees Celsius, in order to provide a balance between effective straightening and limiting hair damage for hair of this type. A third profile 716 (e.g., corresponding to a third hair type) may specify a hair fibre thickness range of 80-110 micrometres and a hair fibre alignment spread range of 40-60 degrees. This third profile 716 may be associated with a relatively high hair straightener heater temperature setting 726 of, say, 150 degrees Celsius, in order to provide effective straightening for this relatively coarse and wavy hair, which is relatively more resistant to heat damage. In this case, since the determined properties 702, 704 fall into the respective ranges specified by the second profile 714, the second profile 724 may be selected as the profile for the user. Accordingly, the settings of the haircare appliance may be adjusted to correspond with the settings 724 associated with the second profile 714. In some examples, the profile for the user may be selected using a trained machine learning model. Referring to Figure 7b, there is illustrated an example of selecting a particular one 714 of a plurality of profiles (not shown in Figure 7b) as a profile for the user based at least in part on one or more of the determined properties 702, 704 of the hair of the user, using a trained machine learning model 750. For example, the trained machine learning model 750 may have been trained to, based on an input of data representing the one or more determined properties 702, 704 of the hair of the user, output a particular one 714 of a plurality 712-716 of profiles. In this example, the trained machine learning model 750 may be a classification machine learning model, trained to classify the determined properties to a particular one of the profiles. The machine learning model 750 may have been trained using supervised learning techniques using training data. For example, the training data may comprise, for example, a plurality of sets of training property data, each set representative of one or more properties of hair of a user, and each set being associated with a ground-truth profile (e.g., the profile to which it is intended that a user having those one or more hair properties is mapped). In this case, the training may involve adjusting parameters of the machine learning model (such as adjusting weights of nodes of layers of the neural network, for example using backpropagation) to minimise a loss function between the profile predicted by the machine learning model for each input training property data set (e.g., the profile predicted by the model) and the respective ground truth profile associated with each training data set (e.g., the intended profile for those properties). Other types of machine learning model and training may be used. In some examples (not shown in the Figures), selecting the profile for the user may comprise inputting the obtained image data representative of hair of the user into a trained machine learning model that has been trained to, based on an input of image data representing the hair of the user, output a particular one of a plurality of profiles. For example, the machine learning model may have been trained using supervised learning techniques using training data. For example, the training data may comprise, for example, a plurality of sets of training image data, each set representative of an image of hair of a user, and each set being associated with a ground-truth profile (e.g., the profile to which it is intended that a user having the hair depicted in the image is mapped). In this case, the training may involve adjusting parameters of the machine learning model (such as adjusting weights of nodes of layers of a neural network, for example using backpropagation) to optimize a loss function between the profile predicted by the machine learning model for each input training image data set (e.g., the profile predicted by the model) and the respective ground truth profile associated with each training image data set (e.g., the intended profile for that image). Other types of model and training may be used. It will be appreciated that in these examples, the determination of the one or more properties of the hair of the user may be implicit in the application of the trained machine learning model to the image data. In some examples, the method may comprise obtaining information entered on a user interface, and selecting the user profile 714 based additionally on the information. For example, this additional information may include user age, user ethnicity, and / or a desired style. This information (e.g., user age) may be added as a further property to the one or more properties determined by analysing the image. For example, in Figures 7a and 7b, this additional information (e.g., user age) is represented by a third property 706. This property 706 may be added to the determined one or more properties 702, 704, and used in the same way as the one or more determined properties 702, 704 to determine the profile 714 for the user. For example, this further property (or one or more such further properties) may be taken as one of the properties used to select the profile for the user 714 in the manner of any of the examples described with reference to Figures 7a and 7b. As described herein with reference to Figures 9 to 14, the user interface via which the user enters the information may be a user interface of the haircare appliance and / or a user interface of a computing device external to and in communication with the haircare appliance. In examples where there are a plurality of settings, adjusting the settings may comprise determining a plurality of sub-profiles for the user based on one or more of the determined properties of the hair of the user, and for each of a plurality of the settings, adjusting the setting based on a respective one of the determined sub-profiles. For example, referring to Figure 8, there is illustrated an example in which a plurality of properties 802-810 are used to determine three sub-profiles 822, 824, 826 for the user. Each sub-profile 822, 824, 826 is associated with a respective different setting 842, 844, 846 of the haircare appliance. In this example the first property 802 (e.g., hair fibre alignment) and the second property 804 (e.g., hair fibre thickness) are used to determine the first sub-profile 822 for the user (e.g., hair type 822). A first setting 842 of the haircare appliance (e.g., a temperature to which a heater of the haircare appliance is heated) is adjusted based on the determined first sub-profile 822. The third property 806 (e.g., hair shine) and the fourth property 808 (e.g., split end factor) are used to determine the second sub-profile 824 for the user (e.g., hair condition 824). A second setting 844 of the hair care appliance (e.g., whether a tip protect mode is activated) is adjusted based on the determined second sub-profile 824. For example, when a tip protect mode is activated, the temperature to which the tips of the hair (which represent relatively old hair) are exposed is reduced to prevent damage and / or excessive drying. The fifth property 810 (e.g., hair colour) is used to determine a third sub-profile 826 for the user (e.g., whether the hair has been artificially coloured 826). A third setting 846 of the hair care appliance (e.g., whether a colour protect mode is activated) is adjusted based on the determined third sub-profile 846. For example, when a colour protect mode is activated the temperature to which coloured portions of the hair are exposed is reduced in order to prevent colour loss from heat exposure. Other examples and combinations are possible. Different settings may be adjusted based on different sub-profiles determined based on different combinations of determined properties. Referring to Figure 9, there is illustrated an example haircare appliance 901. In this example, the haircare appliance is a hair straightener 901. The appliance comprises a camera 903, a processing unit 912, an input interface 908, a display 910, atemperature sensor 907, a heater 905, and a pair of opposing plates 904, 906. The input interface 908 comprises a plurality of buttons by which a user may adjust one or more of the settings of the haircare appliance 901. The display 910 may display information to the user, such as a present operating mode of the appliance 901. The heater 905 is configured to heat the pair of opposing plates 904, 906. The temperature sensor 907 may be configured to determine a temperature of one or both of the plates 904, 906. During use, the opposing plates 904, 906 may be heated by the heater 905, a hair tress (not shown) may be brought between the plates 904, 906, and the appliance operated to bring the plates 904, 906 together to press the hair tress between the plates to straighten the hair. One of the plates 906 defines an aperture 903 in which the camera 902 is located. The camera 902 faces the opposing plate 904. When a hair tress is placed in between the plates 904, 906, the camera 902 may capture an image of the user’s hair. The camera 902 may provide data representing the image of the user’s hair to the processing unit 912. The processing unit 912 may perform the method according to any of the examples described above with reference to Figures 1 to 8, to adjust one or more settings of the haircare appliance 901. Specifically, in the example of Figure 9, the method may comprise adjusting a temperature setting of the haircare appliance 901. Specifically, the temperature setting may set a temperature to which the heater 905 is heated. For example, relatively thin hair may be susceptible to heat damage at lower temperatures than relatively thick hair. Accordingly, in examples, the heater settings may be adjusted so that the heater 905 heats the plates 904, 906 to a relatively higher temperature (e.g., as sensed by the temperature sensor 907) where the user is determined to have relatively thick hair (and / or a profile or sub-profile is determined for the user that specifies a relatively high heater temperature setting) and to a relatively lower temperature where the user is determined to have relatively thin hair (and / or a profile or sub-profile is determined for the user that specifies a relatively low heater temperature setting). As another example, one or more of the settings may set whether and / or to what extent the appliance 901 operates in a tip protect mode. In the example of Figure 9, the temperature sensor 907 and the processing unit 912 may together implement a moisture sensor. Specifically, the moisture of a user’s hair may be inferred from a temperature response of the heated plate 906 (as measured by the temperature sensor 907) when put in contact with the user’s hair. For example, the temperature of the heated plate 906 may drop relatively quickly when placed in contact with relatively moist hair, as compared to relatively dry hair. When the moisture sensor is used with relatively thick and / or coarse hair, the output of the moisture sensor may systematically indicate a higher moisture content than when used with relatively thin and / or fine hair. Accordingly, in this example, the method may comprise adjusting a calibration setting of the sensor. For example, the calibration setting may be a setting according to which the sensor is calibrated. Specifically, in this example, the calibration setting may set a moisture calibration according to which the moisture sensor of the haircare appliance 901 is calibrated. For example, a calibration for the moisture sensor may be set relatively low (that is, to indicate a relatively lower moisture content for a given sensor output) when the user is determined to have relatively thick and / or coarse hair, and may be set relatively high (that is, to indicate a relatively higher moisture content for a given sensor output) when the user is determined to have relatively thin and / or fine hair. In some examples, a haircare appliance may comprise another type of moisture sensor (not shown in the Figures). For example, a haircare appliance (such as a hair dryer for example) may comprise a temperature sensor configured to measure a temperature of the user’s hair. For example, the moisture of a user’s hair may be inferred from a temperature response of the user’s hair, that is, a change in temperature of the user’s hair given a particular applied heat. For example, the temperature of relatively moist hair may increase relatively slowly for a given applied heat as compared to relatively dry hair. Again, when the moisture sensor is used with relatively thick and / or coarse hair, the output of the moisture sensor may systematically indicate a higher moisture content than when used with relatively thin and / or fine hair. Accordingly, the method may comprise adjusting one or more calibration settings according to which the moisture sensor is calibrated, for example as described above. Referring to Figure 10, there is illustrated a haircare appliance 1001 according to another example. In this example, the haircare appliance is a hair dryer 1001. The appliance 1001 comprises an air inlet 1002, an airflow generator 1004, a processing unit 1006, a heater 1008, a camera 1010, a distance sensor 1012, and an air outlet 1009. The airflow generator 1004 is configured to draw air in through the air inlet 1002 to generate an airflow which is expelled from the outlet 1009 in a particular direction 1014. The heater 1008 is configured to heat the airflow before it is expelled from the outlet 1009. In use, a user directed the heated expelled airflow towards their hair in order to dry the hair. The camera 1010 faces in the same direction 1014 as the expelled airflow 1014. As the user uses the hairdryer, the camera 1010 may capture one or more images of the hair 1018 of the user’s hair and provide data representing the one or more images to the processing unit 1006. The distance sensor 1012 also faces in the same direction 1040 as the expelled airflow. The distance sensor 1012 is configured to measure a distance 1016 from the distance sensor 1012 to the user’s head. For example, the distance sensor 1012 may comprise a light-based sensor, such as a time-of-flight sensor, configured to measure a distance from the hair dryer 1001 to the user’s head. The processing unit 1006 may perform the method according to any of the examples described above with reference to Figures 1 to 8, to adjust one or more settings of the haircare appliance 1001. Specifically, in the example of Figure 10, the method may comprise adjusting one or more of a flow rate setting of the haircare appliance 1001, a temperature setting of the haircare appliance 1001, and a calibration setting of the sensor 1012 of the haircare appliance. For example, the temperature setting may set a temperature to which the heater 1008 is heated; the flow rate setting may set a rate of the airflow generated by the airflow generator 1004; and the calibration setting may set a distance calibration according to which the distance sensor 1012 is calibrated.. In some examples, adjusting the flow rate setting comprises controlling the airflow generator 1004 to adjust the flow rate of the airflow, and adjusting the temperature setting comprises controlling the heater 1008 to adjust the temperature of the airflow. For example, in order to be dried within similar timescales, relatively thin hair may be dried using an airflow with a relatively low temperature and / or a relatively low airflow rate, as compared to relatively thick or coarse hair. Accordingly, in some examples, the temperature setting may be adjusted so that the heater 1008 heats the airflow to a relatively low temperature and / or the flow rate setting may be adjusted so the airflow generator generates an airflow with a relatively low flow rate where the user is determined to have relatively fine hair. Conversely, the temperature setting may be adjusted so that the heater 1008 heats the airflow to a relatively high temperature and / or the flow rate setting may be adjusted so the airflow generator generates an airflow with a relatively high flow rate where the user is determined to have relatively thick and / or coarse hair. As another example, when used with a user having relatively curly hair, the output of the distance sensor 1012 may be systematically lower than the output of the sensor when used with a user having relatively straight hair. Accordingly, in examples, the distance calibration may be set relatively low when the user is determined to have relatively curly hair and may be set relatively high when the user is determined to have relatively straight hair. In some examples, there may be different calibration look-up tables to convert a reading of the distance sensor 1012 to a particular distance value, each look-up table having been determined for a different hair type. In these examples, adjusting a calibration setting may comprise selecting the particular distance calibration look-up table that corresponds to the hair type determined for the user. Referring to Figure 11, there is illustrated a haircare appliance 1101 according to another example. In this example, the haircare appliance is a hair dryer 1101. In this example, the appliance 1101 comprises a main body 1103 comprising an air inlet 1102, an airflow generator 1104, a processing unit 1106, a heater 1108, an air outlet 1109, and a display 1107. There are a plurality of attachments 1124, 1126 that are attachable to the haircare appliance 1101, specifically, attachable to the main body 1103 of the haircare appliance 1101. The airflow generator 1104 is configured to draw air in through the air inlet 1102 to generate an airflow which is expelled from the outlet 1109. The heater 1008 is configured to heat the airflow before it is expelled from the outlet 1109. In use, a user directs the heated expelled airflow towards their hair in order to dry the hair. One (or neither) of the attachments 1124 may be attached to the main body 1103 at the air outlet 1109. A first 1124 of the attachments is a diffuser configured to, when attached to the main body 1103, diffuse the airflow expelled from the air outlet 1109 over a relatively large area. A second 1126 of the attachments is a concentrator configured to, when attached to the main body 1103, concentrate the airflow expelled from the airflow outlet 1109 onto a relatively small area. The processing unit 1106 may perform the method according to any one of the examples described above with reference to Figures 1 to 8, to adjust one or more settings of the haircare appliance 1101. Specifically, in the example of Figure 11, the method comprises adjusting one or more attachment settings employed by the haircare appliance when a removable attachment 1124, 1126 is attached to the haircare appliance 1101. For example, the haircare appliance may be configured to use a first set of temperature and / or flow rate settings if no attachment (or a first of a plurality of attachments 1124, 1126) is attached; and a second set of temperature and / or flow rate settings if an attachment (or a second of a plurality of attachments 1124, 1126) is attached. The settings that are employed when a particular removable attachment 1124, 1126 is attached to the haircare appliance 1101 may be adjusted depending on the particular properties of the user’s hair. For example, when the diffuser 1124 is attached to the haircare appliance 1101, the haircare appliance may operate according to a particular temperature setting and flow rate setting. These settings may be adjusted depending on the properties of the hair of the user. For example, if it is determined that the user has relatively curly hair, the temperature setting may be increased, and the airflow rate setting may be decreased, in order to provide optimal performance for diffusing curls. Other examples are possible. Referring to Figure 12, there is illustrated a haircare appliance 1201 according to another example. In this example, the haircare appliance is a curling device 1201. The curling device 1202 comprises an air inlet 1202, an airflow generator 1204, a processing unit 1206, a heater 1208, and a barrel section 1209 comprising a plurality of air outlets 1212 and a camera 1210. The airflow generator 1204 is configured to draw air in through the air inlet 1202 to generate an airflow that is expelled through the outlets 1212 of the barrel section 1209. The heater 1208 is configured to heat the airflow before it is expelled. In use, the airflow is expelled around the barrel section 1209 of the curling device 1201 in order to cause a hair to wrap 1214 around the barrel section 1209 via the Coanda effect. The heated airflow may cause the hair wrapped around the barrel section 1209 to set in a curl. The camera 1210 is located in the barrel section 1209 and faces outwardly of the barrel section 1209. When hair is wrapped around the barrel section 1209, the camera 1210 may capture an image of the hair. The camera 1210 may provide image data representing the image of the hair to the processing unit 1206. The processing unit 1206 may be configured to perform the method according to any of the examples described above with reference to Figures 1 to 8, to adjust one or more settings of the haircare appliance 1201. Specifically, in the example of Figure 12, the method may comprise adjusting a temperature setting that sets a temperature to which the hater 1208 is heated, and / or a flow rate setting that sets a rate of airflow generated by the airflow generator 1204. In examples, the haircare appliance 1201 comprises a styling sequence having a predefined sequence of modes, and adjusting the one or more settings may comprise adjusting one or more of the flow rate setting, the temperature setting and a duration of at least one of the modes. For example, the haircare appliance 1201 may be configured to employ a predefined sequence of operational modes in order to produce a particular effect on the hair, such as to set curls into the hair effectively. The predefined sequence of modes may comprise a first mode comprising operating the haircare appliance according to a first temperature setting and / or a first airflow rate setting for a first duration; and a subsequent second mode of the sequence may comprise operating the haircare appliance according to a second temperature setting and / or a second airflow rate setting for a second duration. Adjusting the settings may comprise adjusting one or more of the first temperature setting, the first airflow rate setting, the first duration, the second temperature setting, the second airflow rate setting, and the second duration. For example, the settings of the styling sequence may be adjusted to be optimized for different hair types. For example, the haircare appliance 1201 may employ a predefined sequence of modes to set curls into hair, for example: a wrap mode in which hair is caused to be wrapped 1214 around the barrel section 1209, of the haircare appliance 1201 by a first airflow expelled around the barrel section 1209 via the Coanda effect; followed by a dry mode in which hair wrapped around the barrel section 1209 of the haircare appliance 1202 is caused to be dried by a second, heated, airflow expelled around the barrel section 1209; followed by a cold shot mode in which the hair wrapped around the barrel section 1209 is caused to be set into a curl by a third, unheated, airflow expelled around the barrel section 1209. Thick, curly hair is typically relatively hard to cause to wrap around the barrel section 1209 and hence for thick, curly hair the flow rate of the first airflow in the wrap mode may be set relatively high in order to cause the hair to wrap around the barrel section 1209 effectively. Thick, curly hair also typically retains a relatively high level of moisture after becoming wet and hence for thick, curly hair the dry mode may be set to last for a relatively long time in order to dry the hair effectively. Thick, curly hair is also typically relatively difficult to penetrate with an airflow and hence for thick, curly hair the cold shot mode may be set to last for a relatively long time in order to effectively set the curl into the hair. Accordingly, where it is determined that the hair is thick and curly, the settings may be adjusted so that the flow rate of the wrap mode is relatively high, and the duration of the dry mode and the cold shot mode are relatively long. As another example, fine hair is typically relatively easy to cause to wrap around the body and hence for fine hair the flow rate of the first airflow in the wrap mode may be set relatively low. Fine hair also typically retains a relatively low level of moisture after becoming wet and hence for fine hair the dry mode may be set to last for a short time. Additionally, fine hair is typically less able to retain curls, but this can be mitigated by applying relatively higher temperatures in the dry mode. Hence for fine hair the temperature of the second airflow in the dry mode may be set relatively high. Fine hair also typically has a relatively low thermal mass and is typically relatively easier to penetrate with an airflow and hence for fine hair the cold shot mode may be set to last for a relatively short time. Accordingly, where it is determined that the hair is fine, the settings may be adjusted so that the flow rate of the wrap mode is relatively low, so that the duration of the dry mode is relatively short and / or the temperature of the second airflow in the dry mode is relatively high, and so that the duration of the cold shot mode is relatively short. Other examples are possible. As described with reference to Figures 9 to 12, the method may be used to adjust one or more of a variety of settings of a variety of different haircare appliances based on a variety of different properties. It will be appreciated that the method need not be performed with a particular one of these example haircare appliances and that in other examples, other settings of other haircare appliances may be adjusted based on other determined properties. Referring to Figure 13, there is illustrated a system 1300 according to an example. In this example, the system 1300 comprises a processing unit 1310. The processing unit 1310 is configured to perform the method according to any of the examples described above. In this example, the processing unit 1310 comprises a processor 1308 and a memory 1306. The memory may store instructions which, when executed by the processor, cause the processor to perform the method according to any of the examples described above with reference to Figures 1 to 12. In this example, the processing unit 1310 also comprises an input interface 1312 and an output interface 1314. For example, the input interface 1312 may receive the data representing an image of hair of the user, for example as per any of the examples described above. For example, this data may be received from a camera 1302 configured to capture the image, for example as per any of the examples described above. As another example, the input interface 1312 may receive information entered by the user to a user interface 1304, for example as per any of the examples described above. The processor 1308 may be in communication with a memory location 1316 (which may be the same as or different from the memory 1306) in which the one or more haircare appliance settings are stored. In some examples, the processor 1308 may be in communication with the memory location 1316 via the output interface 1314. The processor 1308 may be configured to adjust the one or more settings, for example as per any of the examples described above. For example, the processor 1308 may adjust the one or more settings by overwriting the respective settings stored in the memory location 1316. As another example, the processor may output control instructions which cause the one or more settings to be adjusted (e g., overwritten with provided settings by another processor, not shown in Figure 13). One or more operational elements 1320, 1322, 1324, 1326 of a haircare appliance may access the memory location 1316 (which access may be via the processor 1308 or another processor not shown in Figure 13) to determine the settings according to which they are to operate. For example, the one or more operational elements may comprise one or more of a heater 1320, an airflow generator 1322, and a sensor 1324 of a haircare appliance, as per any of the examples described above. In examples, the processing unit 1310 may be part of a haircare appliance. For example, the processing unit 1310 may be used as the processing unit 912, 1006, 1106, 1206 of any of the example haircare appliances described above with reference to Figures 9 to 12. In examples, the processing unit 1310 may be part of a computing device external to and in communication with a haircare appliance. Referring to Figure 14, there is illustrated a system 1400 according to an example. The system 1400 comprises a haircare appliance 1402 comprising a first processing unit 1406, and a computing device 1432 comprising a second processing unit 1436. Either or both of the first processing unit 1406 and the second processing unit 1436 may be the same or similar to the processing unit 1310 described with reference to Figure 13. The computing device 1432 may be, for example, a mobile device or server or another device. In examples, the computing device 1432 may be a handheld device, such as smartphone or computing tablet. In some examples, the computing device 1432 may itself be a haircare appliance, such as a smart hairbrush or the like. As one example, the computing device 1432 may be a hairbrush and the haircare appliance 1402 may be a hairdryer. The computing device 1432 is separate to and in communication with the haircare appliance 1402. Specifically, in this example, the computing device 1432 and the haircare appliance 1402 are in communication with one another via network 1450. For example, the network 1450 may comprise a wired and / or wireless network, such as Bluetooth® or the Internet. Specifically, in this example, the haircare appliance 1402 comprises an RO interface 1412 and the computing device 1432 comprises an VO interface 1440. The communication may be established between the VO interfaces 1412, 1432 via the network 1450. In some examples, the haircare appliance 1402 comprises a camera 1404 configured to capture the image of the hair of the user. The camera 1404 may output image data representative of the image. The image data representative of the image may be provided to the first processing unit 1406 of the haircare appliance 1402 (or to the second processing unit 1436 of the computing device 1432 via the network 1450), and the respective processing unit 1406, 1436 may thereby obtain the image data. In some examples, the computing device 1432 comprises a camera 1434 configured to capture the image of the hair of the user. The camera 1434 may output image data representative of the image. The image data representative of the image may be provided to the second processing unit 1436 of the computing device 1432 (or to the first processing unit 1406 of the haircare appliance 1402 via the network 1450), and the respective processing unit 1436, 1406 may thereby obtain the image data. In some examples, the method according to any of the examples described above may be performed by the first processing unit 1406 of the haircare appliance 1402 alone. For example, the first processing unit 1406 may obtain image data representative of an image of hair of a user from the camera 1404 of the haircare appliance 1406 or from the camera 1434 of the computing device 1432 via the communications network 1450. The haircare appliance 1402 may comprise a memory location 1408 storing the settings according to which the haircare appliance is to operate, and the first processing unit 1406 of the haircare appliance 1402 may adjust these settings as appropriate, according to any one of the examples described above. As another example, the computing device 1432 may comprise a storage location 1438 storing the settings according to which the haircare appliance 1402 is to operate. The haircare appliance 1402 (e.g., the first processing unit 1406) may communicate with the computing device 1432 in order to determine the settings. The first processing unit 1406 of the haircare appliance 1402 may communicate with the computing device 1432 (e.g., the second processing unit 1436) via the network 1450 to adjust the settings as appropriate, according to any one of the examples described above. In some examples, the method according to any of the examples described above may be performed by the second processing unit 1436 of the computing device 1432 alone. For example, the second processing unit 1436 may obtain image data representative of an image of hair of a user from the camera 1434 of the computing device 1432 or from the camera 1404 of the haircare appliance 1402 via the communications network 1450. The computing device 1432 may comprise a storage location 1438 storing the settings according to which the haircare appliance 1402 is to operate, and the haircare appliance 1402 (e.g., the first processing unit 1406) may communicate with the computing device 1432 in order to determine the settings. The second processing unit 1436 may adjust the settings stored at the storage location 1438 of the computing device 1438 as appropriate, according to any one of the examples described above. As another example, the settings may be stored at the storage location 1408 of the haircare appliance 1402, and the second processing unit 1436 of the computing device 1432 may communicate with the haircare appliance 1402 (e.g., the first processing unit 1406) via the network 1450 to adjust the settings of the haircare appliance as appropriate, according to any one of the examples described above. In some examples, the haircare appliance 1402 and the computing device 1432 may together perform the method according to any of the examples described above. For example, some of the steps may be performed by the first processing unit 1406 of the haircare appliance 1402, and the remaining steps may be performed by the second processing unit 1436 of the computing device 1432. In some examples, one of the first processing unit 1406 and the second processing unit 1436 may analyse the image data to determine one or more properties of the hair of the user, and the other of the first processing unit 1406 and the second processing unit 1436 may adjust the one or more settings of the haircare appliance based on the one or more properties, for example according to any of the examples described above. As a specific example, the step of analysing the image data may be performed by the second processing unit 1436 of the computing device 1432, such as a server, and the one or more determined properties (or a profile or sub-profiles for the user determined by the computing device 1432) may be provided to the first processing unit 1406 via the network 1450. The first processing unit 1406 of the haircare appliance 1402 may then adjust the settings based on the determined properties (or based on the determined profile or sub profiles for the user), for example according to any of the examples described above. In some examples, the method may comprise determining one or more setting values based on the one or more determined properties, transmitting the one or more setting values to the haircare appliance 1402, receiving at the haircare appliance 1402 the one or more setting values, and adjusting the one or more settings of the haircare appliance based on the received one or more setting values. For example, determining one or more setting values based on the one or more determined properties and transmitting the one or more setting values to the haircare appliance 1402 may be performed by the computing device 1432, for example by the second processing unit 1436. Receiving the one or more setting values and adjusting the one or more settings based on the received one or more setting values may be performed by the haircare appliance 1402, for example by the first processing unit 1406. In some examples, the computing device 1432 may comprise a camera for obtaining the image data, and the second processing unit 1435 of the computing device 1432 may be configured to: analyse the image data to determine the one or more properties of the hair of the user; determine one or more setting values based on the one or more determined properties; and transmit the one or more setting values to the haircare appliance 1402. In these examples, the first processing unit 1406 of the haircare appliance 1402 may be configured to adjust the one or more settings of the haircare appliance 1402 based on the received one or more setting values. For example, the haircare appliance 1402 may comprise an airflow generator for generating an airflow (not shown in Figure 14), and a heater for heating the airflow (not shown in Figure 14); and the first processing unit 1406 of the haircare appliance may be configured to control the airflow generator to adjust a flow rate setting of the airflow, and to control the heater to adjust a temperature setting of the airflow based on the received one or more setting values. Other examples are possible. Whilst particular examples have been described, it should be understood that these are illustrative examples only and that various modifications may be made without departing from the scope of the invention as defined by the claims.

Claims

1. A computer implemented method comprising:obtaining image data representative of an image of hair of a user;analysing the image data to determine one or more properties of the hair of the user; andadjusting one or more settings of a haircare appliance based on the one or more determined properties.

2. The method according to claim 1, wherein adjusting the one or more settings comprises adjusting one or more of a flow rate setting and a temperature setting of the haircare appliance.

3. The method according to claim 1 or 2, wherein the haircare appliance comprises a styling sequence having a predefined sequence of modes, and adjusting the one or more settings comprises adjusting one or more of a flow rate setting, a temperature setting and a duration of at least one of the modes.

4. The method according to claim 2 or claim 3, wherein the haircare appliance comprises an airflow generator for generating an airflow, and a heater for heating the airflow, and wherein adjusting the flow rate setting comprises controlling the airflow generator to adjust the flow rate of the airflow, and adjusting the temperatures setting comprises controlling the heater to adjust the temperature of the airflow.

5. The method according to any one of claim 1 to claim 4, wherein the haircare appliance comprises a sensor for sensing a property of the hair of the user, and adjusting the one or more settings comprises adjusting a calibration setting of the sensor.

6. The method according to claim 5, wherein the sensor is one of a distance sensor for sensing a distance of the hair of the user, a temperature sensor for sensing a temperature of the hair of the user, and a moisture sensor for sensing a moisture content of the hair of theuser.

7. The method according to any one of claim 1 to claim 6, wherein adjusting the one or more settings comprises adjusting one or more attachment settings employed by the haircare appliance when a removable attachment is attached to the haircare appliance.

8. The method according to any one of claim 1 to claim 7, wherein adjusting the one or more settings comprises:selecting a user profile from a plurality of user profiles based on the one or more determined properties; andadjusting the one or more settings based on the selected user profile.

9. The method according to claim 8, wherein the method comprises obtaining information entered on a user interface, and selecting the user profile based additionally on the information.

10. The method according to any one of claim 1 to claim 9, wherein the image data comprises data representative of an image of hair fibres of the hair of the user, and the one or more properties comprise one or more of hair fibre thickness, hair fibre alignment, hair fibre lustre, and hair fibre colour.

11. The method according to any one of claim 1 to claim 10, wherein the image data comprises data representative of an image of a head of the user, including the hair of the user, and the one or more properties comprise one or more of hair length, hair curl, and hair style.

12. The method according to any one of claim 1 to claim 11, wherein the method comprises determining one or more setting values based on the one or more determined properties, transmitting the one or more setting values to the haircare appliance, receiving at the haircare appliance the one or more setting values, and adjusting the one or more settings of the haircare appliance based on the received one or more setting values.

13. The method according to any one of claim 1 to claim 12, wherein the method comprises operating the haircare appliance according to the adjusted one or more settings.

14. A system comprising one or more processing units configured to perform the method according to any one of claim 1 to claim 13.

15. The system according to claim 14, wherein the system comprises a haircare appliance comprising at least one of the processing units.

16. The system according to claim 15, wherein the system comprises a computing device, separate to and in communication with the haircare appliance, comprising at least one of the processing units.

17. The system according to claim 16, wherein the computing device comprises a camera for obtaining the image data, and the at least one processing unit of the computing device is configured to:analyse the image data to determine the one or more properties of the hair of the user;determine one or more setting values based on the one or more determined properties; andtransmit the one or more setting values to the haircare appliance, andwherein the at least one processing unit of the haircare appliance is configured to adjust the one or more settings of the haircare appliance based on the received one or more setting values.

18. The system according to claim 17, wherein the haircare appliance comprises an airflow generator for generating an airflow, and a heater for heating the airflow, and wherein the at least one processing unit of the haircare appliance is configured to control the airflow generator to adjust a flow rate setting of the airflow, and to control the heater to adjust a temperature setting of the airflow based on the received one or more setting values.

19. The system according to any one of claim 16 to claim 18, wherein the computing device is a handheld device, and wherein optionally the handheld device is a smartphone or computing tablet.

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