WO2019229023A2

WO2019229023A2 - System and method for configuring air flow in an air mask

Info

Publication number: WO2019229023A2
Application number: PCT/EP2019/063726
Authority: WO
Inventors: Xiaoming Zhou; Declan Patrick Kelly; Wei Su
Original assignee: Koninklijke Philips N.V.
Priority date: 2018-05-30
Filing date: 2019-05-28
Publication date: 2019-12-05
Also published as: CN112351822A; EP3801779A2; JP2021525146A; WO2019229023A3; CN112351822B

Abstract

The invention provides a system (10) for configuring an air flow into and/or out of an air mask (32) for configuring atmospheric conditions of air within the mask. The system includes a sensing means (14) and an air flow control means (16). A controller (20) receives readings from the sensing means and based on these controls the air flow level in order to move a level of the sensed atmospheric parameter towards a pre-set target level. The controller implements an iterative adjustment procedure, adjusting a level of the air flow in multiple steps, using feedback from the sensing means to guide each step.

Description

System and method for configuring air flow in an air mask

FIELD OF THE INVENTION

This invention relates to a system for configuring air flow in an air mask, and a method for the same.

BACKGROUND OF THE INVENTION

Air masks (or breathing masks) are used in a range of industries, for filtering or purifying air being inhaled by a user.

For example, air pollution is a worldwide concern. The World Health Organization (WHO) estimates that 4 million people die from air pollution every year. Part of this problem is the outdoor air quality in cities. Nearly 300 smog-hit cities fail to meet national air quality standards.

Official outdoor air quality standards define particle matter concentration as mass concentration per unit volume (e.g. pg/m³). A particular concern is pollution with particles having a diameter less than 2.5 pm (termed“PM2.5”) as they are able to penetrate into the gas exchange regions of the lung (alveoli), and very small particles (<100 nm) may pass through the lungs to affect other organs.

Since this problem will not improve significantly on a short time scale, a common way to deal with this problem is to wear a mask which provides cleaner air by filtration and the market for masks in China and elsewhere has seen a great surge in recent years. For example, it is estimated that by 2019, there will be 4.2 billion masks in China.

However, during use, the temperature and relative humidity inside the mask increase. Combined with the pressure difference inside the mask relative to the outside, this can make breathing uncomfortable.

To improve comfort and effectiveness, a fan can be added to the mask which draws in air through a filter. For efficiency and longevity reasons these are normally electrically commutated brushless DC fans. Fan assisted masks may be provided with an inhale fan or an exhale fan or both. An inhale fan assists in drawing air through a filter and enables a positive mask pressure to be obtained to prevent contaminants leaking into the mask volume. An exhale fan assists in the mask ventilation and ensures the exhaled carbon dioxide is fully expelled.

The benefit to the wearer of using a powered mask is that the lungs are relieved of the slight strain caused by inhalation against the resistance of the filters in a conventional non-powered mask.

Furthermore, in a conventional non-powered mask, inhalation also causes a slight negative pressure within the mask which leads to leakage of the contaminants into the mask, which leakage could prove dangerous if these are toxic substances. A powered mask delivers a steady stream of air to the face and may for example provide a slight positive pressure as mentioned above, which may be determined by the resistance of an exhale valve, to ensure that any leakage is outward rather than inward.

Fan assisted masks thus improve the wearing comfort by reducing the temperature, humidity and breathing resistance, the latter being achieved through control of air pressure inside the mask. These parameters may be termed‘atmospheric parameters’.

However, different users typically have different comfort preferences with regards to these atmospheric parameters, e.g. some may prefer hotter temperatures to others, some may prefer less humid air than others.

It has been suggested in the prior art to provide heaters or air conditioning devices to control the atmospheric conditions inside the mask. However, these are bulky and complex and add cost.

SUMMARY OF THE INVENTION

It has been found by the inventors that by controlling the level of air flow provided by the fan, it is in fact possible to actively adjust the level of the atmospheric parameters. Thus, fan assisted masks allow for customizing the air conditions inside the mask to provide maximum comfort to a user.

A problem with this concept however is that, in practice, it is difficult to reliably achieve the preferred comfort levels of a user. This is because the amount of air flow required to realize specific values of the atmospheric parameters varies depending upon the outside air conditions, and the starting conditions inside the mask. Using feedback sensors, it is possible to reach the preferred atmospheric conditions eventually, but typically only after a significant time delay, during which the user has already begun to experience significant discomfort. An aim of the present invention is to provide an improved means of enhancing wearing comfort of an air mask for a user.

The invention is defined by the claims.

According to an aspect of the invention, there is provided a system for configuring air flow into and/or out of an air mask, comprising:

sensing means for sensing at least one atmospheric parameter of air inside the mask;

air flow control means for controlling air flow into and/or out of the air mask; and

a controller operatively coupled with the air flow control means and the sensing means, and adapted to

acquire one or more measurements from the sensing means, and iteratively adjust a level of the air flow based on the measurements so as to adjust a value of the at least one atmospheric parameter toward a pre-determined target value.

The adjustment may comprise one or more (typically a plurality of) of iterative adjustment steps, and wherein the controller is adapted to acquire a sensor measurement from the sensing means before and/or after each iterative adjustment of the air flow level.

The iterative adjustment steps may comprise one or more (typically a plurality of) discrete changes of the air flow level, each of an amount being determined based at least partly on a current air flow level.

The invention is based on use of an active feedback loop for iteratively adjusting a level of an air flow in order to configure a level of at least one atmospheric condition of air inside the mask. The corresponding at least one atmospheric parameter may include for instance one or more of: air pressure, air temperature, air humidity, and carbon dioxide levels.

In contrast to prior art approaches, the air flow control device itself is used for controlling the air conditions, avoiding the added bulk and complexity of additional air conditioning elements.

Furthermore, rapid adjustment of the atmospheric conditions is realised by adopting an iterative adjustment approach. Rather than relying e.g. on pre -prepared lookup tables or correlation formulae to jump immediately to an estimated air flow level for the desired atmospheric conditions, the invention adopts a sensor based, step-wise approach. This avoids wasting energy in potentially driving the air flow control unit to initially too high a level before then being forced to reduce it. In most instances, it also speeds up realization of the desired atmospheric conditions, since it ensures constant convergence toward the desired parameter levels, which outcome is not otherwise guaranteed and can lead to inconsistent and unreliable results.

‘Iteratively’ in the context of the invention means to adjust the level in multiple steps. The multiple steps may for instance be separated by a time delay.

At least one atmospheric parameter of air inside the mask is configured.

‘Inside’ means within an interior region delimited by the mask, between the user’s face or mouth and the mask.

The at least one atmospheric parameter may include one or more of: air pressure, air temperature, air humidity, and carbon dioxide level. The atmospheric parameter may be a combination of individual parameters, for instance it may be defined as a point in a multi-parameter space, e.g. a vector in a multi-parameter space.

In examples, the air flow control means may be an air flow generator, such as a fluid pump, such as a fan or air blower. However, it may be a different kind of air flow control means; any element suitable for regulating flow of air into and/or out of the mask may be used.

The air flow control means may comprise an air flow generator. There may further be provided circuitry or a processor for driving such an air flow generator.

The sensing means may comprise one or more individual sensors. Each of the one or more individual sensors may be adapted to sense a different atmospheric parameter.

The controller may be adapted to continue the iterative adjustment until a value of the at least one atmospheric parameter is within a defined tolerance range of the target value.

The tolerance range may be pre-determined. It may be fixed in programming of the controller for instance. It may be an absolute value range. Alternatively, it may be defined in terms of a proportion of the target parameter value, e.g. +/-5% or +/- 10%.

The controller acquires one or more measurements from the sensing means. A ‘measurement’ simply means a reading or sensor output. The outputs or measurements of the sensing means may give a direct measurement of the atmospheric parameter, or may give an (indirect) indication of the atmospheric parameter, e.g. via a related parameter.

The controller may be adapted to acquire a sensor measurement before and/or after each iterative adjustment of the air flow level. According to one or more examples, upon reaching the target value of the at least one atmospheric parameter, the controller may be adapted to locally or remotely store the level of the air flow, and wherein the controller is adapted in accordance with at least one control mode to set an initial air flow level, in advance of any adjustment, being equal to a previously so stored air flow level.

According to one or more examples, the controller may be communicable with a reference dataset comprising air flow settings, and wherein

upon reaching the target value of the at least one atmospheric parameter, the controller is adapted to store the level of the air flow in said dataset, and

wherein the controller is adapted in accordance with at least one control mode to set an initial air flow level, in advance of any adjustment, being equal to an air flow level in the dataset.

The reference dataset may be stored in a memory comprised by the system for example. The reference dataset may be stored on a remote server with which the controller is communicable.

In accordance with advantageous embodiments, the air flow control means may have a maximum air flow level and minimum air flow level, and wherein the controller is adapted to set an initial air flow level, in advance of any adjustment, being part way between the minimum air flow level and the maximum air flow level. This provides an efficient approach to achieving rapid convergence at the target value of the atmospheric parameter.

The initial air flow level may for instance be set at half way between the minimum air flow level and maximum air flow level. In the absence of further information, this typically represents the most efficient starting point for an iterative adjustment of the air flow level.

Advantageously, in accordance with any embodiment, the iterative adjustment may comprise one or more discrete changes in the air flow level, each of a uniform amount. This approach involves changing the airflow level by one or more steps of a set amount or distance. This is an operationally simple approach, as it does not require determining a size of each adjustment.

In accordance with this set of examples, the adjustment procedure may comprise a linear iterative adjustment from the minimum air flow level (to minimize power consumption) to a level achieving a value of the atmospheric parameter being within a defined range of the target atmospheric parameter value. In accordance with an alternative set of examples, the iterative adjustment may comprise one or more discrete changes of the air flow level, each of an amount being determined based at least partly on a current air flow level.

Current may mean a most recently acquired measurement or reading of the parameter from the sensing means. Hence this may be based on a measurement that has already been acquired. Alternatively, the controller may acquire a dedicated measurement in advance of determining the size or amount of the adjustment step. This may be done before every step or before only a subset of one or more of them, e.g. just the first one, or every other one (to save resources).

Determining the size of each discrete adjustment based on a current level of the air flow provides for intelligent and efficient adjustment, since step sizes can be varied depending on how high the air flow level has already reached. For example, if the air flow level is currently already high, it may be advantageous to make further changes in relatively smaller steps.

In a particular set of examples for instance, the amount of each discrete change is a positive amount equal to a set portion of the difference between the current air flow level and the maximum air flow level of the air flow control means, or a negative amount equal to a set portion of the difference between the current air flow level and the minimum air flow level of the air flow control means.

The set portion may according to particular examples be a half. Alternatively, any other proportion may be used.

In particular, the controller may be adapted to determine whether the current value of the at least one atmospheric parameter is higher than the target or lower than the target. If higher, then (if the parameter is temperature or humidity for instance) the controller sets the amount of the next discrete adjustment of the air flow level to be half of the difference between the current air flow level and the maximum air flow level. If the current value is lower than the target value, then the next discrete adjustment may be set at an amount equal to half of the difference between the current level and the minimum air flow level.

In an alternative set of advantageous embodiments, the iterative adjustment comprises one or more discrete changes of the air flow level, each of an amount being determined based at least partly on a difference between the target value of the at least one atmospheric parameter and a current value of the at least one parameter. In this case, the air flow is effectively adjusted based on a distance from the current air parameter conditions to the target conditions.

The size of each iterative adjustment may be determined or set in proportion or in correlation with the determined difference between the current and target atmospheric conditions, or a normalized version of this, e.g. normalized according to the target value itself.

In one set of examples for instance, the amount of each discrete change is equal to f*Dr, where Dr = a set portion of the difference between the current air flow level and either the maximum or minimum air flow level of the air flow control means, and f = a weighting based on a difference between the target value of the at least one atmospheric parameter and a current value of the at least one parameter.

Whether the maximum or minimum air flow level is used may be selected depending upon whether the current atmospheric parameter level is higher or lower than the target level.

The weighting may for example be given by a normalized version of the difference between the target value of the at least one atmospheric parameter and a current value of the at least one parameter, for instance normalized according to the target atmospheric parameter value. In this case, f = (target value of atmospheric parameter - current value of atmospheric parameter) / target value of atmospheric parameter.

Again, current value may mean a most recently acquired measurement of the parameter.

The set (i.e. pre-determined) portion may in examples be a half. The amount of the portion may be set manually or automatically. It may be fixed, e.g. in programming of the controller. It may in examples be adjustable, for instance based on a user input or based on sensor feedback for instance.

In accordance with any embodiment, the controller may be adapted to set an initial air flow level, in advance of any adjustment, the level being determined based on a detected activity level of a user.

Activity level may be a qualitative or quantitative parameter. It relates to exercise or movement related activity. Activity level may be categorized and/or measured in discrete activity level types such as sitting, standing, walking, and running. Alternatively, it may be measured and assessed quantitatively. The system may comprise an activity detection means arranged in use to detect the activity level of the user. This may for example comprise a heart rate monitor, a PPG sensor, a pulse sensor, and/or a movement sensor such as an accelerometer.

In particular, examples, the controller may be adapted to detect any change in the activity level of the user, and to alter said initial level of the air flow in response to the detected change.

In accordance with one or more embodiments, the controller may be adapted to be communicable with a local or remote data store, storing personal information and/or preferences of a user, and wherein the controller is adapted to set an initial air flow level in advance of any adjustment being based on said personal information and/or preferences of the user.

In accordance with a set of embodiments of the invention, there may be provided an air mask comprising a system according to any of the embodiments of examples outlined above, or to be described below, the system being arranged in use for configuring air flow into and/or out of the air mask.

The air mask may be configured for filtering or purifying air being inhaled by a user.

The mask may in examples comprise a breathing inlet/outlet arranged restrict inflow of air so as to force inflowing air through a filter medium provided in the inlet/outlet for filtering air in advance of supply to the interior of the mask.

Examples according to a further aspect of the invention provide a method for configuring air flow into and/or out of an air mask, comprising:

acquiring one or more measurements of at least one atmospheric parameter of air inside the mask, and

iteratively adjusting a level of air flow into and/or out of the mask based on the measurements so as to adjust a value of the at least one atmospheric parameter toward a pre determined target value.

The adjustment may comprise a plurality of iterative adjustment steps, and wherein a measurement of the at least one atmospheric parameter is acquired before and/or after each iterative adjustment of the air flow level.

The iterative adjustment may comprise one or more discrete changes of the air flow level, each of an amount being determined based at least partly on a current air flow level. The discrete changes form the iterative adjustment steps. BRIEF DESCRIPTION OF THE DRAWINGS

Examples of the invention will now be described in detail with reference to the accompanying drawings, in which:

Fig. 1 schematically depicts a first example system according to an

embodiment;

Fig. 2 schematically depicts an example air mask incorporating a system according to an embodiment;

Fig. 3 schematically illustrates an example air flow adjustment procedure implemented by a system according to an embodiment; and

Fig. 4 schematically illustrates optimization of parameters in a parameter space by means of a system according to an embodiment.

DETAIFED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

The invention provides a system for configuring an air flow into and/or out of an air mask for configuring atmospheric conditions of air within the mask. The system includes a sensing means and an air flow control means. A controller receives readings from the sensing means and based on these controls the air flow level in order to move a level of the sensed atmospheric parameter towards a pre-set target level. The controller implements an iterative adjustment procedure, adjusting a level of the air flow in multiple steps, using feedback from the sensing means to guide each step.

Embodiments of the invention thus permit configuring air conditions within a mask so as to move them within a preferred‘comfort zone’ - that is, to adjust air flow until a set of one or more atmospheric parameters of air inside the mask are within a particular defined range or span or tolerance of defined target values. If there are multiple parameters, this can be understood as moving atmospheric conditions to within a particular region in an atmospheric parameter space. This region will be referred to in this disclosure as a‘comfort zone’.

The atmospheric parameters may include by way of example one or more of: temperature, humidity, air pressure, and carbon dioxide levels.

Embodiments of the invention are thus based on controlling an airflow supply to the mask in a-step-by-step manner to move to the comfort zone (with suitable temperature, humidity, air pressure). A solution is proposed for quickly optimizing the air flow to arrive at the predetermined target air conditions.

Fig. 1 schematically depicts components of an example system according to one or more embodiments of the invention. Fig. 2 shows the components of the system incorporated in situ within an example air mask 32.

The system 10 comprises an air flow control means in the form of a fan 16 for controlling air flow into the air mask 32. The fan may also control an air flow out of the mask, or an additional fan may be provided to control air flow out of the mask. The fan is configurable between multiple different air flow levels. The different air flow levels may correspond to different power levels of the fan (e.g. different speeds of the fan), and/or may correspond to different flow rates of air being provided into and/or out of the mask by the fan. The different air flow levels may correspond to different speeds or velocities of air being provided into and/or out of the mask.

The system further comprises a sensor 14 adapted in use to sense at least one atmospheric parameter, for example temperature, humidity, air pressure and/or carbon dioxide levels. In other examples, multiple sensors may be provided, each adapted in use to sense a different atmospheric parameter.

The sensor 14 and fan 16 are operatively coupled to a controller 20. The controller is adapted to control the fan so as to configure a level of the sensed atmospheric parameter of air within the air mask 32. In particular, the controller is adapted in use to acquire one or more measurements from the sensing means, and to iteratively adjust a level of the air flow based on the measurements so as to adjust a value of the at least one atmospheric parameter toward a pre-determined target value.

In cases where the sensor 14 is adapted to sense multiple atmospheric parameters, the controller 20 may be adapted to iteratively adjust the level of the air flow based on the measurements so as to adjust values of the multiple atmospheric parameters toward a set of pre-determined target values, or a pre-determined target region in parameter space.

As schematically depicted in Fig. 2, the sensor 14 is arranged to sense the one or more atmospheric parameters of air within an interior 36 of the mask 32, the interior meaning a space or cavity defined between an inner surface of the mask and the user’s face and/or mouth. The fan 16 is arranged proximal the user’s mouth for providing a stream of air to the mouth region. The fan is mounted within an inlet structure 34 arranged extending through a bounding outer wall of the mask 32. The outer wall for example functions as a filter and thus may be an in-line filter, in series with the fan.

The mask 32 further comprises a breathing inlet/outlet, typically comprising a check valve, the check valve permitting outflow of exhaled air, but restricting inflow of air during inhalation such as to force inhaled air through a filter medium provided in the breathing inlet/outlet. The filter media may by way of example comprise filter material for filtering particulate matter, Volatile Organic Compounds (VOC) and/or any other pollutant, e.g. gaseous pollutants. Optionally, the filter material may comprise carbon media to block the particulate matter, VOC or other pollutants.

The controller 20 is adapted in use to acquire readings or measurements of the one or more atmospheric parameters from the sensor 14, i.e. perform data sampling from the sensor. The sensor may be adapted to provide readings or sensor outputs which are directly representative of the atmospheric parameter in question. Alternatively, the outputs of the sensor may be only indirectly indicative of the atmospheric parameter. In the latter case, the controller may be adapted to perform processing of the sensor outputs to convert them into outputs directly representative of the parameter in question.

The controller 20 is adapted to adjust an air flow level in an iterative manner based on the acquired sensor readings. In practice, this may comprise adjusting a voltage supply or level of the fan 16. Adjusting the voltage upward will increase air flow; adjusting the voltage downward will decrease air flow.

An increase in air flow can be expected to decrease temperature, humidity and carbon dioxide levels, and to increase air pressure. A decrease in air flow can be expected to increase temperature, humidity and carbon dioxide levels, and to decrease air pressure.

Preferably, the system 10 further comprises a wireless communication module for wireless communicating with a remote computer, processor or terminal, for instance with a mobile device such as a smart phone or tablet. This may permit wireless control of the system or adjustment of certain settings of the system, and/or may permit output of sensor readings or a current status of the system.

Although a fan 16 is provided in the above example, in other examples, a different air flow control means may instead be used. All features and options described in this disclosure in relation to a fan in particular may also be applied to any other air flow control means which may instead be used.

As noted, the controller 20 is adapted to implement an adjustment procedure in which the air flow level of the fan 16 is iteratively adjusted based on measurements acquired from the sensing means 14 so as to adjust a value of the at least one atmospheric parameter toward a pre-determined target value.

A number of different approaches exist for implementing this iterative adjustment. These different approaches will now be outlined in more detail.

According to a most simple first approach, the controller 20 begins the fan (the air flow device) 16 at an initial air flow level being a minimum air flow level of the air flow control device. This will be referred to as AF min. The controller is then adapted to increase the air flow level in a series of one or more steps of equal size until a predetermined target value of at least one atmospheric parameter is reached. This is illustrated in Fig. 3 which schematically shows the increase in air flow level, AF (y axis, arbitrary units), as a function of time (x-axis, arbitrary units). As shown, AF is increased linearly in equal size steps. Arrow 42 indicates an air flow level (AF_i) at which the target value of the atmospheric parameter in question (e.g. temperature, humidity, pressure, carbon dioxide concentration) is reached.

As shown, the adjustment algorithm may continue the increase in air flow simply until the parameter is within a certain tolerance range (indicated by schematically by D) of the target value.

In advantageous examples, the air flow level is increased until a target value of a plurality of atmospheric parameters is reached. This can be understood by considering a set of two or more atmospheric parameters as forming a parameter space, wherein the controller is adapted to adjust the air flow level until a particular point, or more preferably a particular region, within the parameter space is reached. This point or region will be referred to as a ‘comfort zone’, CZ.

This concept is illustrated schematically in Fig. 4 which schematically depicts a two-dimensional parameter space 44 composed of humidity (depicted on the y-axis) and temperature (depicted on the x-axis). Start 46 and end 48 points within the parameter space are shown, each corresponding to a different temperature and humidity value. An arrow schematically represents the movement through the parameter space as the air flow level is adjusted by the controller 20. By way of example, the humidity and temperature are shown as decreasing. A number of zones 52 within the parameter space are also shown. The adjustment procedure may comprise adjusting the air flow iteratively until the measured atmospheric parameters (in this case temperature and humidity) reach a desired zone within the space. The particular shape, size and distribution of the zones shown in Fig. 4 is by way of example only, and these may differ in other examples.

An example algorithm for the above described most simple adjustment procedure is set out below.

[ALGORITHM 1]

Initial air flow level, AF_0, at time T=0, is set at AF min (minimum air flow level of the fan 16)

A measurement of the one or more atmospheric parameters is acquired from the sensing means 14.

If the value(s) are not within a defined tolerance range A of the target value(s) for the parameters, then the air flow is increased by a set amount: AF_i+l = AF_i + AAF

Steps 2 and 3 are repeated until the values of the atmospheric parameter(s) are within said defined tolerance range of the target value(s)

Hence, for this example, for each particular air flow level (AF_i), the system determines the values of the relevant atmospheric parameters (e.g. temperature (T) and relative humidity (RH) within the mask 32) and determines whether the target internal comfort zone has been reached (see discussion above).

In more complex examples, to be described below, a distance from a current point or region within the parameter space 14 (i.e. a current comfort zone CZ_i) and the target point or region (target comfort zone CZ target) can be determined and this used to guide the subsequent adjustments in the air flow level AF.

To take into account the possibility that the atmospheric parameters may be at too high a level, so that the air flow level needs to be reduced, an additional step can be introduced, wherein the controller determines whether the current value(s) of the atmospheric parameter(s) are higher that the target value(s) or lower. If the values are higher (a state referred to as Over Comfort Zone threshold), then the controller may be adapted to reduce the air flow level in a linear manner, i.e. at Time T_i+l : AF_i+l = AF_i - X * AAF, where X may be any integer, and may for instance be varied for different control modes or different system applications.

If the value(s) are lower than the target value(s), then the controller may implement the linear increase in air flow level described above, i.e. at Time T_i+l: AF_i+l = AF_i + X*AAF, where X may be any integer, and may for instance be varied for different control modes or different system applications.

In alternative examples, rather than linearly increasing the air flow level or linearly decreasing the air flow level, it may be exponentially increased or decreased until the target comfort zone is reached, i.e. until the values of the one or more atmospheric

parameters are sensed to be within a defined tolerance range of the target value(s).

The simple linear approach described above may be relatively slow for a number of reasons. In particular, starting adjustment of the air flow from AF min on each occasion may add a significant delay to reaching the target comfort zone, especially if a relatively high air flow level is eventually required. In addition, increasing the air flow level by the same, relatively small, amount each time also leads to a relatively slow adjustment time.

A second approach aims to reduce the time taken to reach the target values of the one or more atmospheric parameters of air inside the mask. A second example adjustment procedure implemented by the controller in accordance with one or more embodiments of the invention is thus set out below.

As noted above, it is assumed that the air flow control means 16 (the fan) can be adjusted between a minimum air flow level (AF min) and a maximum air flow level (AF max). CZ(AF) will be used to denote a particular point or region in atmospheric parameter space (a particular‘comfort zone’) corresponding to a particular air flow level, AF. A particular point or region in parameter space corresponds to a particular set of atmospheric parameter values, or ranges of values, e.g. (T, RH), for temperature and relative humidity. CZ TARGET will be used to denote the set of target values for the one or more atmospheric parameters (i.e. the target comfort zone).

A distance between two points in parameter space, corresponding to air flow levels AF1 and AF2, will be indicated by CZ(AFl) - CZ(AF2). This‘distance’ may for instance be understood as a vector, or simply as an ordered set of difference values. A positive distance value indicates that the target comfort zone (target set of one or more parameter values) has not been reached (e.g. parameter values are too low); a negative value indicates that the parameter values have overshot the target (e.g. parameter values are too high). A distance value of 0 indicates that the comfort zone has been exactly reached. In practice, a distance value indicated by A will be used to denote that the achieved value(s) of the one or more atmospheric parameters are within a defined acceptable tolerance range, A, of the target value(s).

The second approach is based on setting an initial level of the air flow part way between the minimum air flow level (AF min) and the maximum air flow level (AF max). This reduces the time taken to converge to the target parameter values (the target comfort zone), and so enhances the experience of the user.

In one example, an initial level of the air flow is set half way between AF min and AF max. An example algorithm corresponding to this approach is set out below.

[ALGORITHM 2] i=0

AF_i = (AF max - AF_min)/2

If (CZ TARGET - CZ(AF_i)| <= A Done

ELSE IF [CZ TARGET - CZ(AF_i)] > 0 THEN

AF_i+l = AF_i + X * AAF

ELSE IF [CZ TARGET - CZ(AF_i)] < 0 THEN

AF_i+l = AF_i - X * AAF

i=i+l

GOTO 3 where AAF is a set, discrete increase amount of the air flow level, and X is any integer, which may be set according for instance to a particular control mode or particular system application. X may simply be set to 1 in some examples.

Alternatively, AF may be adjusted in amounts configured such that growth is exponential. According to a more complex example, the adjustment amount added or subtracted to the air flow level AF each time may be varied according to the current level of the air flow. An example algorithm according to this approach is set out below.

[ALGORITHM 3] i=0

AF_i = (AF max - AF_min)/2

If (CZ TARGET - CZ(AF_i)| <= A Done

ELSE IF [CZ TARGET - CZ(AF_i)] > 0 THEN

AF_i+l = AF_i + [(AF_max - AF_i)/2]

ELSE IF [CZ TARGET - CZ(AF_i)] < 0 THEN

AF_i+l = AF_i - [(AF_i - AF_min)/2]

i=i+l

GOTO 3

According to the above example, the air flow level is increased on each iterative adjustment by an amount equal to half of the difference between the current air flow level and the maximum or minimum air flow level (depending upon whether the air flow level is required to increase or decrease in order to move closer to the target value(s) of the one or more atmospheric parameters). This example hence increases speed of convergence on the target parameter values in two ways: by setting the initial air flow level higher than AF min and by intelligently adjusting the amount of each increase in the air flow level.

It is noted that this approach assumes a monotonic relation between air flow and atmospheric parameters (i.e. increasing AF will reduce the distance to CZ TARGET if CZ TARGET - CZ(AF_i) > 0 and decreasing AF will reduce the distance to CZ TARGET if CZ TARGET - CZ(AF_i) > 0).

According to a further set of one or more examples, the initial air flow level AF_0 may be set according to a previously set air flow level, e.g. during a previous use of the system, before the system was subsequently switched off. In this set of examples, the controller is adapted, upon reaching the target value of the at least one atmospheric parameter, to locally or remotely store the level of the air flow (e.g. in a memory). The controller is furthermore adapted, in at least one control mode, to set an initial air flow level AF_0, in advance of any adjustment, being equal to a previously so stored air flow level. The system may hence comprise a local memory to facilitate storage and retrieval of previous air flow levels. Alternatively, the controller may be communicable with a remote data store or memory for storing and retrieving achieved air flow levels.

The controller is preferably adapted (when operated in the relevant control mode, if applicable), to retrieve the most recently so stored air flow level when switched on, and to set the initial air flow level equal to this previous air flow level. When the target value(s) of the atmospheric parameters are reached, the controller may override any previously stored air flow level, or may store the new current air flow level as a separate entry.

An example algorithm according to this approach is set out below. [ALGORITHM 4]

At time T=0, controller retrieves previously stored air flow level AF_previous

AF_0 set at AF_0 = AF_previous

If (CZ TARGET - CZ(AF_0)| <= A Done

ELSE IF [CZ TARGET - CZ(AF_0)] > 0 THEN

AF is increased, e.g. linearly or exponentially or according to any other scheme, until CZ TARGET is attained.

ELSE IF [CZ TARGET - CZ(AF_0)] < 0 THEN

AF is decreased, e.g. linearly or exponentially or according to any other scheme, until CZ TARGET is attained.

Once CZ TARGET is attained, controller stores the corresponding level of air flow AF new

By way of example, steps 3 to 5 of the above algorithm may be implemented by steps 3 to 6 of [ALGORITHM 2] or by steps 3 to 6 of [ALGORITHM 3] Variations and options described in relation to those algorithms may be applied equally to the above algorithm.

A further example algorithm according to this approach is set out below.

[ALGORITHM 5]

At time T=0, controller retrieves previously stored air flow level AF_previous

AF_0 set at AF_0 = AF_previous / 2

If (CZ TARGET - CZ(AF_0)| <= A Done ELSE IF [CZ TARGET - CZ(AF_0)] > 0 THEN

ELSE IF [CZ TARGET - CZ(AF_0)] < 0 THEN

This example differs only in that the initial air flow level AF_0 is set at half of the previously stored value (AF_previous / 2), rather than the previously stored value itself. This alternative provides more efficient attainment of the target atmospheric parameter values in the case that these values are liable to change between instances of use of the system. The system may in some examples be switchable between different control modes in which ALGORITHM 4 and ALGORITHM 5 respectively are used.

According to a further set of one or more embodiments, the adjustment procedure implemented by the controller may comprise setting an initial air flow level AF_0, in advance of any adjustment, being determined based on a detected activity status of a user, e.g. a detected activity level of the user. The activity status may include for instance one of a set of alternative options such as sitting, standing, walking, a speed of walking, e.g. slow walking, fast walking. Any other activity type may also be included in the set of options such as cycling, running, rowing.

An activity level may be a qualitative or quantitative parameter. It relates to exercise or movement related activity. Activity level may be categorized and/or measured in discrete activity level types such as sitting, standing, walking, and running. Alternatively, it may be measured and assessed quantitatively.

The system may comprise an activity detection means (activity monitor) arranged in use to detect the activity level of the user. This may for example comprise a heart rate monitor, a PPG sensor, a pulse rate sensor, and/or a movement sensor such as an accelerometer. Alternatively, a user may manually input their activity level of status for example.

The controller may be adapted to access a locally or remotely stored lookup table for instance which associates different activity levels or statuses with different appropriate initial air flow levels AF_0. Once the initial air flow level AF_0 has been set according the detected or determined activity level or status, the controller is adapted to continue the iterative adjustment procedure in order to arrive at the target values of the one or more atmospheric parameters. This may be done for example according to steps 2 to 6 of ALGORITHM 2 or steps 2-6 of ALGORITHM 3 or in accordance with any other approach or example set out in this disclosure, or defined in any claim of this application.

In some examples, contextual information may be combined with activity information. In particular, the controller may be adapted, upon attaining CZ TARGET, to store both the associated air flow level AF, and also the ongoing activity status or level.

Furthermore, the controller may be adapted, when adjusting the air flow level, to detect a current air flow level and to retrieve a previously stored air flow level corresponding to the detected activity level. In this way, the user’s previous setting for AF when at the given activity level is used.

In some examples, the controller may be adapted to detect any change in the activity level of the user, and to alter the initial level of the air flow, AF_0, in response to the detected change. The change may be detected by the activity monitor. The controller is adapted in this case to determine a new AF_0. This may for example be based contextual data, as discussed in the preceding paragraph, or based e.g. on entries in a database, for instance grouped according to personal characteristics of the user. This option will be described in detail below. This enables a quick response when the user changes their activity behavior.

According to a further set of embodiments, the distance between a current comfort zone level (a current value of the one or more atmospheric parameters) and the target comfort zone level (the target value(s) of the one or more parameters) is used in determining each amount by which the air flow level is adjusted.

By distance is meant a distance in parameter space (see the discussion above in relation to Fig. 4). Where only one atmospheric parameter is being targeted, the distance is simply a difference between the current value of the parameter and the target value.

In a particular set of examples for instance, a difference between the current air flow level and either the maximum or minimum air flow level is determined and is subsequently weighted in accordance with a determined distance between the current and target value(s) of the atmospheric parameter(s).

An example algorithm according to this approach is set out below. [ALGORITHM 6] i=0

AF_i = (AF max - AF_min)/2

IF (CZ TARGET - CZ(AF_i)| <= A Done

ELSE IF [CZ TARGET - CZ(AF_i)] > 0 THEN

AF_i+l = AF_i + [(AF max - AF_i) * [[CZ TARGET - CZ(AFJ)]/CZNORM]]

ELSE IF [CZ TARGET - CZ(AF_i)] < 0 THEN

AF_i+l = AF_i - [(AF_i - AF min) * [[CZ TARGET - CZ(AFJ)]/CZ_NORM]]

i=i+l

GOTO 3

CZ_NORM is used to normalize the distance from the current comfort zone (current physiological parameter values) to the target zone (target values).

In advantageous examples, CZNORM = CZ(AF_i). Alternatively, CZNORM may be set at any other appropriate value, e.g. at AF min, or AF max.

It is noted that this approach assumes a linear relation between air flow and atmospheric parameters (i.e. increasing AF will reduce the distance to CZ TARGET if CZ TARGET - CZ(AF_i) > 0 and decreasing AF will reduce the distance to CZ TARGET if CZ TARGET - CZ(AF_i) > 0). However, the approach still functions even in the case that the relationship is not exactly linear.

According to one or more examples, the controller may be adapted to switch between different of the adjustment approaches outlined above. This may be done

dynamically. For example, the controller may initially (e.g. for a new mask) perform the adjustment procedure according to ALGORITHM 1 or ALGORITHM 2. For repeated use, the controller may subsequently switch to one or more of the other approaches set out in the other algorithms. Thus a hybrid adjustment approach is implemented.

For example, the system may be adapted to implement a smart learning functionality, wherein different of the adjustment procedures outlined above are, for instance successively, trialed, and the speed at which each brings the atmospheric parameter(s) to within the target comfort zone level CZ TARGET recorded. Based on this, the fastest adjustment method (fastest to reach the target) is then determined, and adopted as at least the default adjustment approach for future adjustments. For different users, with different comfort zone preferences, certain of the adjustment approaches may prove to be quicker at reaching the target comfort zone level than others. This trial procedure permits the most efficient method for each given user to be determined and adopted.

Any combination of the various adjustment procedures outlined above may be trialed, for instance all may be trialed, one-by-one, or just a subset may be trialed one-by-one.

According to examples, the trial procedure may also be configured to determine and record an energy efficiency of each adjustment procedure which is trialed. A procedure is considered energy inefficient if the adjustment procedure leads to the air flow level being raised above the necessary level for the target comfort zone level (i.e. over shooting the necessary air flow level). This is energy inefficient because energy is wasted running the fan at an unnecessarily high level. A procedure is energy efficient if the target comfort zone level is reached without raising the air flow level above the necessary level for reaching the target comfort zone.

In examples, adjustment procedures which overshoot the required air flow level may be discounted from further use, and the fastest of the remaining adjustment methods selected as the default for future adjustments. Alternatively, the controller may be adapted to receive a user input indicating a preference between speed and efficiency, and the assessment of the different adjustment approaches made only on the selected preferred metric. Alternatively a mix of the two may be used, for instance an efficiency score and a speed score may be derived for each adjustment procedure and an average (e.g. mean) taken of the two scores. The method achieving the highest average score may then be selected as the default adjustment approach for future adjustments.

In accordance with an advantageous set of embodiments, the controller 20 is adapted to be communicable with a local or remote data store, storing personal information and/or preferences of a user. The controller in this case may be adapted to set an initial air flow level in advance of any adjustment being based on said personal information and/or preferences of the user.

The system may comprise a communication module for communicating with a remote terminal or computer or mobile device storing the remote data store. This may be a wireless communication module. This may for example comprise a transmitter and receiver including one or more antennas for transmitting and receiving. The module may use for example ZigBee, RF frequency, Wi-Fi, or any other wireless communication protocol for facilitating the communication.

The remote or local data store may for instance comprise a dataset storing optimum air flow levels for providing comfortable air environments within the mask for users having different personal characteristics. These characteristics may include for example age, height, weight, sex. This database may be used to look up an appropriate initial air flow value at which to begin the adjustment procedure, with the aim of choosing an initial value as close as possible to the final value which corresponds with the user’s chosen target level of the one or more atmospheric parameters.

An example set of steps in accordance with this approach will now be outlined.

First, the controller 20 receives as input one or more personal characteristics of the user. This may be received for example from a mobile computing device such as a smartphone, for instance mediated by an app, and received at the controller wirelessly. They may be based on a pre-stored profile of the user or input manually by the user. By way of non-limiting example, the input personal characteristics may include: age, weight, height, nationality, allergy symptoms.

Second, once the input characteristics are received, the controller 20 may be adapted to cluster or categorize the user according to the input characteristics into one of a set of defined clusters or groups or categories in the database. The clustering or categorizing may for example be performed via K-means clustering or via regression analysis.

Third, an initial air flow level AF_0 is then derived from the database based on the determined category or cluster in which the user falls. For example, the database may store a relationship (representable by a curve or line) between initial air flow level and personal information, e.g. with individual characteristics. Alternatively, the database may store a lookup table associating different groups or clusters of individuals with different particular AF_0 values. Alternatively again, the database may store one or more decision trees permitting derivation of an appropriate air flow level AF_0 based on the various personal characteristics.

Additionally or alternatively, in accordance with examples, the controller is adapted to populate or update the database according to information derived through use of the mask by a given user. For example, the database may use as inputs for charactering the user one or more of the following: age, height, weight, nationality, allergies. Other characteristics might also be used such as a manner in which the air mask is worn, or a duration (e.g. average duration) for which the air mask is worn.

These characteristics may be used to cluster or categorize the user and/or to re cluster or re-categorize entries of the database. Acquired information concerning air flow levels used by the clustered or categorized user may then be used to update or populate the database. In particular, the controller may acquire, e.g. store, air flow levels associated with preferred atmospheric parameter settings of a user. These can be used to update the database, improving results when using the database to determine initial air flow settings for future users, as described above.

The data in the database may be used in other ways to further enhance adjustment procedures implemented by the controller.

For example, as noted above, ALGORITHM 6 above assumes a linear relationship between air flow level and the one or more atmospheric parameters (the comfort zone). By collecting data from a population of connected users (as outlined above), an actual relationship between these parameters can be determined and stored (possibly also further stratified or categorized based on user characteristics). Derived relationship curves thus collected from a population of connected mask users can be used for example in configuring the adjustment algorithms implemented in new non-connected masks, and in existing masks (as the relationship between parameters and air flow is better known).

As discussed above, embodiments make use of a controller. The controller can be

implemented in numerous ways, with software and/or hardware, to perform the various functions required. A processor is one example of a controller which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. A controller may however be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.

Examples of controller components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

In various implementations, a processor or controller may be associated with one or more storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform the required functions. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller.

Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

Claims

CLAIMS:

1. A system (10) for configuring air flow into and/or out of an air mask (32), comprising:

sensing means (14) for sensing at least one atmospheric parameter of air inside the mask;

air flow control means (16) for controlling air flow into and/or out of the air mask; and

a controller (20) operatively coupled with the air flow control means and the sensing means, and adapted to

acquire one or more measurements from the sensing means, and iteratively adjust a level of the air flow based on the measurements so as to adjust a value of the at least one atmospheric parameter toward a pre-determined target value, the adjustment comprising a plurality of iterative adjustment steps, and wherein the controller is adapted to acquire a sensor measurement from the sensing means before and/or after each iterative adjustment of the air flow level,

wherein the iterative adjustment comprises one or more discrete changes of the air flow level, each of an amount being determined based at least partly on a current air flow level.

2. A system (10) as claimed in claim 1, wherein the controller (20) is adapted to continue the iterative adjustment until a value of the at least one atmospheric parameter is within a defined tolerance range of the target value.

3. A system (10) as claimed in claim 1 or 2, wherein the controller is

communicable with a reference dataset comprising reference air flow settings, and wherein upon reaching the target value of the at least one atmospheric parameter, the controller (20) is adapted to store the level of the air flow in the reference dataset, and

wherein the controller is adapted in accordance with at least one control mode to set an initial air flow level, in advance of any adjustment, being equal to an air flow level in the reference dataset.

4. A system (10) as claimed in any preceding claim, wherein the at least one atmospheric parameter includes one or more of: air pressure, air temperature, and air humidity.

5. A system (10) as claimed in any preceding claim, wherein the air flow control means (16) has a maximum air flow level and minimum air flow level, and wherein the controller (20) is adapted to set an initial air flow level, in advance of any adjustment, being part way between the minimum air flow level and the maximum air flow level, and

optionally wherein the initial air flow level is half way between the minimum air flow level and maximum air flow level.

6. A system (10) as claimed in any of claims 1-4, wherein the system further comprises an activity detection means for detecting an activity level of a user, and wherein the controller (20) is adapted to set an initial air flow level, in advance of any adjustment, the level being determined based on a detected activity level of the user, and optionally wherein the controller is adapted to detect any change in the activity level of the user, and to alter said initial level of the air flow in response to the detected change.

7. A system (10) as claimed in any preceding claim, wherein the controller (20) is adapted to be communicable with a local or remote data store, storing personal information and/or preferences of a user, and wherein the controller is adapted to set an initial air flow level in advance of any adjustment being based on said personal information and/or preferences of the user.

8. A system (10) as claimed in any preceding claim, wherein the amount of each discrete change is a positive amount equal to a set portion of the difference between the current air flow level and a maximum air flow level of the air flow control means, or a negative amount equal to a set portion of the difference between the current air flow level and a minimum air flow level of the air flow control means.

9. A system (10) as claimed in any of claims 1-8, wherein the iterative adjustment comprises one or more discrete changes of the air flow level, each of an amount being determined based at least partly on a difference between the target value of the at least one atmospheric parameter and a current value of the at least one parameter.

10. A system (10) as claimed in claim 9, wherein the amount of each discrete change is equal to f*Dr, where Dr = a set portion of the difference between the current air flow level and either a maximum or minimum air flow level of the air flow control means, and f = a weighting based on a difference between the target value of the at least one atmospheric parameter and a current value of the at least one atmospheric parameter.

11. An air mask (32) comprising a system ( 10) as claimed in any preceding claim, arranged in use for configuring air flow into and/or out of the air mask.

12. An air mask (32) as claimed in claim 11 , wherein the air mask is for filtering or purifying air being inhaled by a user.

13. An air mask (32) as claimed in claim 11 or 12, wherein the mask comprises a breathing inlet/outlet arranged restrict inflow of air so as to force inflowing air through a filter medium provided in the inlet/outlet for filtering air in advance of supply to the interior of the mask.

14. A method for configuring air flow into and/or out of an air mask (32), comprising:

iteratively adjusting a level of air flow into and/or out of the mask based on the measurements so as to adjust a value of the at least one atmospheric parameter toward a pre determined target value, the adjustment comprising a plurality of iterative adjustment steps, and wherein a measurement of the at least one atmospheric parameter is acquired before and/or after each iterative adjustment of the air flow level,