Indoor space characterisation and calibration

Abstract

A computer-implemented method and sensing system for characterising an indoor space uses a computing device sensor data from an environment sensor (101 in figure 1) within an indoor space such as a bu

Description

TECHNICAL FIELD

[0001] The present invention relates to a method and a sensing system for characterising an indoor space based on data from an environment sensor. The present invention further relates to a method and a sensing system for calibrating the characterisation of an indoor space. BACKGROUND

[0002] Environment sensors, for instance temperature sensors, pressure sensors, humidity sensors or gas sensors (such as those for sensing CO2 or CO) are well known. Environment sensors, particularly temperature sensors, are commonly used in buildings including commercial, industrial and office buildings, and domestic premises (that is, houses and apartments, referred to in this document as homes). Environment sensors may be used to gather data for use in building control systems, such as Heating, Ventilation and Air Conditioning (HVAC) systems. In some cases, environment sensors may directly control a building system, for instance, where a temperature sensor forms a thermostat for controlling a building heating system.

[0003] It is known to use sensors, for instance cameras and motion sensors, to detect events within a building. An example of an event that may be detected using a camera or a motion sensor is a person entering or exiting an indoor space (for instance, a building room). A camera or motion sensor may also be used to detect a room state, for instance occupancy levels within a room. However, cameras and motion sensors are relatively expensive, particularly if used extensively throughout a building. Related to this the computing power required to process camera and motion sensor data to determine states and events may further add to the cost. In some cases, particularly cameras may not be used due to privacy concerns, especially in public buildings such as hospitals or hotels. Furthermore, a range of sensor types may be required to detect various indoor space states (for instance, occupancy levels) and events within the indoor space (for instance, people or animals entering or exiting a room, doors or windows being opened or heating or cooling systems being activated or deactivated).

[0004] It is an aim of certain examples of the present invention to solve, mitigate or obviate, at least partly, at least one of the problems and / or disadvantages associated with the prior art. Certain examples aim to provide at least one of the advantages described below. BRIEF SUMMARY OF THE INVENTION

[0005] According to a first aspect of the present invention there is provided a computer-implemented method of characterising an indoor space, the method comprising: receiving sensor data from an environment sensor within an indoor space; identifying within the received sensor data at least one: stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold; trend region indicative of a period of time within which the received data trends upwards ordownwards with a sensor data gradient less than a gradient threshold; or rapid change region indicative of a period of time within which the received data trends upwards ordownwards with a sensor data gradient greater than or equal to the gradient threshold; and inferring at least one of an indoor space state or an event occurring within the indoor space based on the identification.

[0006] An advantage of certain embodiments of the present invention is that a relatively low-cost environment sensor, for instance a single temperature sensor, may be used in an indoor space to infer an indoor space state or an event within the indoor space. For instance, a temperature sensor may be located within an indoor space, for instance a room in a domestic premises, and room state information such as the number of people present, and which heat sources are activated or deactivated may be inferred. Alternatively or additionally, events such as people entering or exiting the room or doors or windows being opened or closed may be inferred. In some embodiments of the present invention this inference of indoor space state or events is based on a prior calibration process for the environment sensor in a particular location within the indoor space.

[0007] Prior to the step of identification, the method may further comprise filtering the sensor data. The filtering may comprise noise reduction and removal of fluctuations not indicative of change within the indoor space. The filtering may comprise passing the sensor data through a weighted window processing filter. Prior to the step of identification, the method may further comprise curve fitting the sensor data or filtered sensor data to a polynomial curve. The sensor data or filtered sensor data may be fitted to a polynomial curve with an order of two or more. Filtering and / or curve fitting may reduce noise from the sensor and eliminate small fluctuations that have limited information value for identifying indoor space states or events.

[0008] Identifying a stable region, trend region or rapid change region may comprise processing the sensor data, filtered sensor data or curve fitted sensor data using a region weighting mathematical expression.

[0009] For at least one identified stable region inferring an indoor space state may comprise: identifying a sensor data magnitude or an average sensor data magnitude within the identified stable region; identifying a sensor data magnitude band containing the sensor data magnitude or average magnitude; and inferring an indoor space state based on the identified sensor data magnitude band; wherein the inference of an indoor space state is based on a prior calibration process of measuring sensor data magnitudes for a plurality of known indoor space states and determining sensor data magnitude bands for those known indoor space states.

[0010] For at least one trend region the method may comprise: inferring that no event has occurred within the indoor space state or that the indoor space is remains within a previously identified indoor space state based on a prior calibration process of measuring sensor data magnitudes for a plurality of known indoor space states and determining expected data trends or drifts in sensor data for those known indoor space states over time.

[0011] For at least one identified rapid change region inferring an event occurring within the indoor space may comprise: identifying a sensor data gradient or an average sensor data gradient within the identified rapid change region; identifying a sensor data gradient band containing the sensor data gradient or average gradient; and inferring that an event that has occurred within the indoor space based on the identified sensor data gradient band; wherein the inference of an event that has occurred within the indoor space is based on a prior calibration process of measuring sensor data gradients for a plurality of known events within the indoor space and determining sensor data gradient bands for those known events within the indoor space.

[0012] In some examples, inferring an event that has occurred within the indoor space is further based upon an identified sequence of two or more different rapid change regions.

[0012] In some examples, inferring an event that has occurred within the indoor space is further based upon an identified sequence of two or more different rapid change regions having opposite polarity sensor data gradients. In some examples, inferring an event that has occurred within the indoor space is further based upon an identified sequence of one or more stable regions or trend regions and one or more rapid change regions. That is, it may be a compound series of multiple identified regions that allows inferences to be drawn about what is happening in a room.

[0013] The environment sensor may comprise: a temperature sensor; a pressure sensor; a humidity sensor; or a gas sensor. Where the environment sensor is a temperature sensor located within the indoor space, an inferred indoor space state may comprise a state in which the number of heat sources and heat sinks within the indoor state remains static. Where the environment sensor is a temperature sensor located within the indoor space, an inferred event that has occurred within the indoor space may comprise one of: a heat source entering or being activated in the indoor space; or a loss of heat being triggered in the room. A heat source entering or being activated in the indoor space comprises: an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space. A loss of heat being triggered in the room may comprise: an artificial cooling system being activated in the indoor space; an artificial heating system being deactivated within the indoor space; an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; or a person or animal leaving the indoor space. While the present invention is not limited in its broadest scope to temperature sensors, the information that may be inferred from temperature data is intuitively easier to understand.

[0014] The indoor space may comprise a room in a building.

[0015] According to a second aspect of the present invention there is provided a computer-readable storage medium having computer-readable program code stored therein that, in response to execution by a processor, cause the processor to perform the above characterisation method.

[0016] According to a third aspect of the present invention there is provided an apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to perform the above characterisation method.

[0017] According to a fourth aspect of the present invention there is provided a sensing system for characterising an indoor space, the system comprising: an environment sensor configured to generate sensor data indicative of an environment parameter within an indoor space; and a computing device configured to: receive sensor data from the environment sensor; identify within the received sensor data at least one: stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold; trend region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient less than a threshold gradient; or rapid change region indicative of a period of time within which the received data trends upwards or downwards within a sensor data gradient greater than or equal to a threshold gradient; and infer one of an indoor space state or an event occurring within the indoor space based on the identification.

[0018] According to a fifth aspect of the present invention there is provided a computer-implemented method for calibrating the characterisation of an indoor space, the method comprising: receiving sensor data from an environment sensor within an indoor space; observing indoor space states or events within the indoor space; and correlating sensor data to observed indoor space states or events within the indoor space.

[0019] Correlating sensor data to observed indoor space states or events within the indoor space may comprise: measuring sensor data magnitudes for a plurality of observed indoor space states and determining sensor data magnitude bands for those observed indoor space states; or measuring sensor data gradients for a plurality of observed events within the indoor space and determining sensor data gradient bands forthose observed events within the indoor space.

[0020] Where the environment sensor is a temperature sensor located within the indoor space, a known indoor space state may comprise a state in which the number of heat sources and heat sinks within the indoor state remains static. Where the environment sensor is a temperature sensor located within the indoor space, a known event that has occurred within the indoor space may comprise one of: a heat source entering or being activated in the indoor space; or a loss of heat being triggered in the room. A heat source entering or being activated in the indoor space may comprise: an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space. A loss of heat being triggered in the room may comprise: an artificial cooling system being activated in the indoor space; an artificial heating system being deactivated within the indoor space; an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; or a person or animal leaving the indoor space.

[0021] The calibration may be specific to a particular indoor space and to a particular sensor location within that indoor space.

[0022] According to a sixth aspect of the present invention there is provided a computer-readable storage medium having computer-readable program code stored therein that, in response to execution by a processor, cause the processor to perform the above calibration method.

[0023] According to a seventh aspect of the present invention there is provided an apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to perform the above calibration method.

[0024] According to an eighth aspect of the present invention there is provided a sensing system for calibrating the characterisation of an indoor space, the system comprising: an environment sensor configured to generate sensor data indicative of an environment parameter within an indoor space; and a computing device configured to: receive sensor data from the environment sensor; observe indoor space states or events within the indoor space; and correlate sensor data to observed indoor space states or events within the indoor space. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Examples of the invention are further described hereinafter with reference to the accompanying drawings, in which: Figure 1 schematically illustrates a sensing system for characterising an indoor space state or for calibrating the characterisation according to an embodiment of the present invention; Figure 2 is a perspective view of a hybrid home; Figure 3 is a plan view of the hybrid home of figure 2 in which the sensing system of figure 1 is deployed in a room; Figure 4 is a graph showing raw data received from a temperature sensor forming part of the sensing system of figure 1; Figure 5 is a graph showing the raw data for figure 4 and the raw data after filtering; Figure 6 is a graph showing the filtered data of figure 5 and a curved fitted to the filtered data; Figure 7 is a graph showing the curved fitted data of figure 6 after regions of interest have been identified; Figure 8 is a flowchart illustrating a method for characterising an indoor space state according to an embodiment of the present invention; Figure 9 is a flowchart illustrating a method for characterising an indoor space state according to a further embodiment of the present invention; Figure 10 is a flowchart illustrating a method for calibrating a system for characterising an indoor space state according to an embodiment of the present invention; Figure 11 is a flowchart illustrating a method for calibrating a system for characterising an indoor space state according to a further embodiment of the present invention; Figure 12 schematically illustrates a sensor forming part of the sensing system of figure 1; and Figure 13 schematically illustrates a computing device forming part of the sensing system of figure 1. DETAILED DESCRIPTION

[0026] The present invention concerns a system and method for characterising an indoor space. In the following description the term room is used, particularly a room in a building such as a domestic premises, however embodiments of the present invention are applicable to the characterisation of any indoor space. The characterisation of the indoor space uses data from an environment sensor, for instance a temperature sensor. Raw sensor data is processed and used to infer a room state or an event that has taken place within the room.

[0027] Certain embodiments of the present invention are particularly suited to the detection and identification of human presence and related movements within a living space in a home using a temperature sensor. Certain embodiments assess the thermal data from a temperature sensor for both magnitude and gradient indicating a thermal gradient within the room with high precision. Certain embodiments are the present invention also permit the presence or absence of heat sources to be inferred or for thermal variations caused heat gain / loss appliances, by drafts and leaks or the opening or closing of a window or door to be inferred.

[0028] Embodiments of the present invention allow sensing of thermal changes due to the presence or absence of a thermal source such as a human body or similar (or more than one body) or slow changes ordrifts in temperature within the living space and which can be due to slow heat gain or loss phenomena and any changes from a steady state caused by these. These outlined phenomena can be caused by: presence or absence (use and non-use) of heat sources and building cooling systems or the appearance of heat loss phenomena (heat leakage / loss and absence or switch off of heaters, warm air etc.), drafts caused either by heat loss (bad insulation / thermal performance), local actions such as door or window opening / closure, the presence of a person in the living space and increase or decrease in the number of persons, detection of subsequent actions such as door opening / closure, window opening / closure, person or persons accessing the living space.

[0029] Heat gain sources include a person or persons, a heater or more, a draft or heat loss occurrences. The gains outline an increase with respect to heat when the sources are exhibiting a warmer output than the current temperature of the living space and they outline a decrease when for example a cooling appliance is used, or a heat loss device or object is present or activated.

[0030] As will be described below in connection with figures 4 to 7 data from an environment sensor is signal processed to remove noise and irrelevant data fluctuations, the filtered data is fitted to a curve and a mathematical identification process is applied. The identification process identifies within the received sensor data at least one: stable region indicative of a period of time within which the received data is relatively stable within a first sensor data magnitude band; trend region indicative of a period of time within which the received data trends upwards or downwards within a first sensor data gradient band; or rapid change region indicative of a period of time within which the received data trends upwards or downwards within a second sensor data gradient band. From the identified stable region, trend region or rapid change region an indoor space state or event occurring within the indoor space can be inferred. In certain embodiments this inference is based on a prior calibration process, as is described below in connection with figures 10 and 11.

[0031] While the signal processing, inference and calibration methods described below are broadly applicable to any form of environment sensor, the present inventor has identified that they are applicable to temperature sensors. Furthermore, the present inventor has identified that identified stable regions, trend regions or rapid change regions derived from temperature sensor data are particularly informative in inferring room events of particular interest within a room such as in a domestic premises, for instance the number of people within the room and people entering and leaving the room. Accordingly, embodiments of the present invention described below focus on the use of a single thermal sensor which is used to detect small changes in the thermal envelope associated with moving or static persons, animals, or objects as well as assessment of the overall space thermal background and any associated changes within a defined space (as shown above). It will be understood however that in its broadest form the present invention is not limited only to temperature sensors.

[0032] Referring now to figure 1, a sensing system suitable for characterising an indoor space (and for calibrating an indoor space characterisation method) is illustrated. The sensing system comprises a single sensor 101, which in certain embodiments of the present invention may be a temperature sensor. The sensor 101 may be any suitable commercially sourced sensor available to the skilled person or a proprietary sensor providing the necessary precision described above. For instance, for the case of a temperature sensor, a sensor with a precision of ±0.01 °C (or better) may suffice to characterise an indoor space. Atypical measurement frequency for sensor readings may be 1 Hz to 20 Hz. In some cases, the accuracy of the temperature measurement may be less significant than the precision: so long as a temperature change indicative of a room event is detectable, the magnitude of the temperature measurement may not be critical. That is, the value of the temperature reading within a temperature scale is not as important as the ability to detect changes in temperature (or indeed the absence of change in a stable region of the data). Suitable temperature sensors are widely commercially available, for instance from ST-Microelectronic Ltd, Analog Devices Inc, and Texas Instruments Inc.

[0033] The sensor data sensed by the sensor 101 is transmitted to a computing device 102, or other suitable device for processing sensor data. The sensor 101 and the computing device 102 may be coupled together using any available wired or wireless network. The processing of the sensor data is described below starting with figure 4.

[0034] Referring now to figures 2 and 3, as noted previously, while embodiments of the present invention are applicable to buildings in general, they are particularly to suited to domestic premises such as houses and apartments (referred to generically in this document as homes). Partly this is because the daily routines of people within a home and the range of activities that take place within a home are significantly more complex than for industrial, retail or commercial premises, with the result that it may be harder to infer indoor space states or events within the indoor space using conventional techniques. Figure 2 is a perspective view of one particular form of home, and figure 3 illustrates a cross section plan view of such a home incorporating the sensing system according to figure 1.

[0035] Figures 2 and 3, show a home 200 which is constructed as a hybrid building 200 (which may also be referred to as a modular building). It should be appreciated that the specific example of a hybrid home is not limiting on the types of buildings or indoor spaces to which the present application can be applied. The hybrid building 200 comprises a first building section 201 and a second building section 202. The first building section 201 can take the form of a dock. The term “dock” is used to describe a building section to which another building section may be docked, installed, connected, or attached. The first building section 201 is an on-site construction at a final location of the building 200, which may be a fixed, on-site location. That is, the building 200 has a final, fixed, on-site location. In this exemplary embodiment, said final, fixed, on-site location is determined by construction plans and fixed by virtue of building foundations. The term “on-site location” is used to refer to the building site, which will be understood to refer to the immediate proximity of the building 200 and the entire building site, including housing estate, on which the building 200 is to be built. It will be understood that the site may be a large building / construction site comprising a plurality of plots, the final location for the building 100 being provided by one of said plots.

[0036] The second building section 202 can take the form of a module. The term “module” is used to describe a building section which can be docked, or is installable, connectable, or attachable to another building section, particularly a first building section in the form of a dock. The second building section 202 is transportable to the final location in a substantially assembled form.

[0037] During construction of the first building section 201 at the on-site location, the first building section 201 is preconfigured to receive the second building section 202. That is, the first building section 202 is constructed with the knowledge and design that a second building section is to be subsequently connected, and the first building section 201 is thus preconfigured for connection and receipt of the second building section. The second building section 202 may be removable. This could relate to the shaping of the first building section 201, through to sealing and connection features. The second building section 202 is connected to the first building section 201 at the final location of the building 200, thereby to provide said building 200 at the final, fixed, on-site location.

[0038] The first building section 201 further includes a services hub 203, which may include part or the whole of certain building systems. For instance, for a ventilation system, the services hub 203 may include ventilation system components such as the pumping and heat exchange elements of a Mechanical Ventilation with Heat Recovery (MVHR) system.

[0039] Figure 3 shows the sensor 101 located within dock 201, though it will be understood that this location is merely exemplary, and the sensor 101 may be in any room within any part of the home where it is desired to monitor room states and events. Similarly, the computing device 102 is shown located within dock 201, though it may be located at any convenient location so long as it can communicate with sensor 101.

[0040] Within the room or other indoor space to be characterised the sensor 101 can be positioned at any convenient location, for instance near the entrance, in the periphery, or in the middle of the room. For the selected room or other indoor space that it is desired to monitor or characterise, the selected position within the room for the sensor may affect the ability to infer room states and events and may affect the calibration process described below. In some examples a central location within a room may be preferable so that the sensor readings are not unduly affected by events around the periphery which may be less significant. As will become clear from the discussion of calibration in the following description (particularly in relation to figures 10 and 11), during calibration the relevant heat profiles from a given heat or cold source for example, can be obtained at certain distances from the sensor and this outlines the heat or cool signature of such a source to ascertain the state of the room space (that is what heat or cooling sources are present or activated). In some cases, the calibration may be performed iteratively for multiple distance between heating or cooling sources (including humans) and the sensor which can assist with ascertaining the heat or cooling source and its distance from the sensor within the relevant indoor space. It may be that certain locations within a room are particularly sensitive to detecting room states or events, as may be established through a calibration process (which may include changing the location of the sensor within a room until an optimal location is determined). For instance, for detecting when a door is opened using a temperature sensor, it may be that a location for the sensor close to the door gives the clearest indication of a door opening event in the sensor data. Alternatively, for sensing a number of persons present within a room through a temperature sensor, a location within the central region of a room may be preferred. For the case of a temperature sensor, positioning is flexible because it is the overall background temperature that is sensed. In the case sensing the presence and movement of persons is important, and then the sensor can be strategically positioned in an area closer to where people congregate or pass by. In some examples a location close to areas or movement or congregation for the building occupants may be preferable. In some examples a location close to or remote from artificial heating or cooling sources may be preferred, dependent on whether the intention is to capture information about their use or whether other events are more important to detect.

[0041] Turning now to figure 4, this graph illustrates raw temperature data 401 (dotted line) generated by a sensor 101 and received by a computing device 102. The X axis indicates data samples, numbered sequentially from 1 to 1140. Data samples may be received, for instance, 10 times per second. The Y axis shows temperature ranging from 22.05°C to 23°C. It can be seen that the raw temperature sensor data 401 within this temperature range is quite noisy and includes fluctuations that may not be indicative of room states or events. However, broadly, generally steady stable regions and rapid change regions can be observed.

[0042] In certain embodiments of the present invention regions of interest within the temperature sensor may be identified within the raw temperature data. However, in accordance with other embodiments of the present invention, the raw temperature is first pre-processed.

[0043] Turning now to figure 5, this graph shows both the raw temperature data 401 (dotted line) and filtered temperature data 501 (solid line) after a filtering process to remove any noise or irrelevant fluctuations has been applied. The filter allows for the removal of noise fluctuations and yields a trend to the data so that deduction of the heat and temperature parameters are made easier for access.

[0044] The filtering of the raw temperature data may use any conventional technique known to the skilled person that serves to remove noise and irrelevant fluctuations (but retain trend regions and rapid change regions indicative of room states and events, as well as stable regions). The present inventor has identified that for filtering temperature data a weighted window processing filter is suitable, though other filter types such as dedicated multipole filters may be used. A further filter option would be to use a recursive filter. In other embodiments of the invention a machine learning clustering technique for clustering data points based on their similarity may be used, such as K-means clustering. A weighted window processing filter is a type of digital signal processing filter used to modify or enhance a signal by applying a weighted average to a set of samples within a sliding window. The filter operates by first defining a window of a certain size, which may be applied to each sample in turn (with the window looking back over a preceding set of temperature samples within the window). Each sample within the window is then multiplied by a corresponding weight value, which can be predetermined or dynamically calculated based on the characteristics of the input signal. The weighted average of the samples within the window is then computed, with the resulting value replacing the original sample. The temperature data for each sample is thus replaced by a weighted average of that original sample using a predetermined number of the preceding samples resulting in a filtered output signal that is a smoothed or modified version of the original signal. Any shifts in the data can be corrected by shifting the new data by an amount equal to the width of the weighting window.

[0045] The filtering is performed by the computing device 102 and may form part of a programmed data acquisition loop whereby the temperature data is collected and a portion is kept in memory.

[0046] The raw data stream from the sensor has m samples, as represented by equation (1) presented below, where i is an index: To, Ti, T2, ... T|, Ti+1, Ti+2, ... Tm-2, Tm-1, Tm (1)

[0047] A weighted data stream may be established where A is a window coefficient, according to equation (2) presented below: Ao.To, A1.T1, A2.T2, ... Ai.Ti, Ai+1 .Ti+1, Ai+2.T+2, ... Am-2.Tm-2, Am-i.Tm-i, Am.Tm (2)

[0048] The initial index is at a random temperature sample i which is the start of the averaging window. There are n samples or poles in the averaging window, with 0<=n<=m (the window must be smaller than the data stream). As an example: having a window size n=200 poles, then the sum calculated for the filtered sample Bi+199 starting at index i+199 would be according to equation (3) presented below (the index varies from the start to the end of a 200 sample window): Bi+199 = A|T| + A1+1T1+1 + ... + Aj+iggTj+199 (3) Bi+200 = Ai+iTi+i + A1+2T1+2 +...+ Ai+200 T i+200 and so on...

[0049] The matrix for the filtered samples would be in accordance with equation (4) presented below: [BJ with n-1 <= j <=m (4)

[0050] It will be understood that there is a shift over n-1 values, along the abscissa, and this can be corrected to remove the phase delay caused. The discrepancies caused by missing out on a single sample have no significant effect.

[0051] Practically, each output from the filter is sequentially obtained from earlier data by 200 points. This may represent 10 seconds of data (200 samples when sampling at 20 Hz). This kind of time is a good representation for events that occur in real time within the living space. Such an action window may extend a few seconds to an arbitrary number (below the value m).

[0052] The raw temperature data of figure 4 illustrates a scenario in which a person walks into a small, previously empty room at around index 800 along the X axis. That person is joined by another person a little later, with the door being opened and closed rapidly (from around index 5000) leading to a rise in temperature. A door is opened at around index 8000 one person leaves leading to a large drop in temperature, returning to approximately the temperature of just one person in the room at around index 8500. In comparison to the filtered temperature data line 501 in figure 5, it can be seen that there are inflection points visible around these time points, following filtering using a 200 pole weighted window processing filter. While a 200 pole weighted window processing filter has been applied to the data in figure 5, in other embodiments of the present invention a filter with 2 or more poles may be used.

[0053] Referring now to figure 6, following filtering the filtered temperature data in some embodiments is further processed according to a curve fitting function. In one example, the filtered temperature data is passed to a data fitting polynomial having one or more elements. Figure 6 shows the raw temperature data 401 (dotted line), the filtered temperature data 501 (thin solid line) and curve fitted temperature data 601 (thick solid line). In this step the data is outlined using a fitting curve which follows the filtered data and produces a fit which reduces the fluctuations further and highlights regions of interests. Suitable curve fitting functions will be well known to the skilled person, for instance the Chebyshev method. This curve fitting has an order equal to 2 or higher and outputs a curve fit which removes not only further residual noise but also helps show the trends in the data more consistently.

[0054] Following curve fitting there follows a third step of identifying regions of interest within the smoothed temperature data (although the skilled person will appreciate that in principle this identification step may be applied directly to the raw or filtered temperature data, though potentially with a reduction in accuracy). In this step the data output by the polynomial curve fitting function is passed through a region weighting mathematical expression which identifies the regions of remaining fluctuations and outline areas of thermal stability or regions where temperature is on varying trend. The identification of regions of interest outlines one or more of a stable region indicative of a period of time within which the received data is relatively stable within a first sensor data magnitude band; a trend region indicative of a period of time within which the received data trends upwards or downwards within a first sensor data gradient band; or a rapid change region indicative of a period of time within which the received data trends upwards ordownwards within a second sensor data gradient band. The shape of this curve shows multiple “Trapezoidal” sections (that is, flat sections corresponding to stable regions and ramped sections corresponding to trend or rapid change regions).

[0055] Each stable region comprises a quasi-horizontal portion of the curve fitted temperature data 601 (or the raw or filtered temperature data) against time. That is, in a stable region the temperature remains fairly constant. By consideration of the magnitude of the temperature within the stable region (or the identification of a temperature band within which the stable region is located) an inference may be made of what heat sources are located within that room based on a prior calibration process (as is described below).

[0056] Each trend region comprises a time period including a small upward or downward trend in temperature. This can be used to infer a drift in the living space thermal envelope, but that no major event has occurred within the room. That is, it may be inferred that the room may remain within a state indicated by a preceding stable region.

[0057] Each rapid change region showing a fast change between two temperature levels and is a transient region that may be used to infer the sudden appearance of a heat gain or heat loss, based on a prior calibration process.

[0058] In some embodiments of the present invention potential heat and loss sources in a room are initially identified as part of the calibration process. As is described in greater detail below, these heat and loss sources are initially weighted so that each of the stable regions can be attributed to the relevant source, and rapid changes between those sources identified.

[0059] Referring now to figure 7, an explanation of the identification of different regions and the inference of events based on a prior calibration process is presented for the specific example of a room initially containing a single person (that is, only a single heat source), a door opening and a second person entering, and the door closing.

[0060] For a given room initially at a temperature To, the entry of a single person may yield an increase in temperature ATi. Assuming for the moment that the heat output from two people is similar within a broad tolerance, if N people enter the room then the change in temperature is N.ATi. It will be appreciated that different people may generate different amounts of heat, however as an approximation it remains the case that the total heat output is directly proportional to the number of people.

[0061] Similarly, a specific heat source H1 like a heater will cause a change in temperature of ATHi (assuming for the moment that the heater is self-regulated).

[0062] Stated generally, each heat source of loss of heat that occurs within a room may be referred to as H2 and will cause a change of temperature of-ATH2-

[0063] The thermal profile for a given room, would therefore be subject to a series of changes outlined initially by a base temperature which can then vary according to the presence, introduction or removal or heat generating or heat removing elements, which cause a temperature change of ±AThx.

[0064] As an example, as illustrated in figure 7, the room may initially be stable at To = 16°C while empty. This first region 701 may be identified as an initial stable region or period of time on the basis that the temperature data does not vary by more than a threshold temperature magnitude throughout this region. The size of the threshold temperature magnitude may be specific to a particular sensor and a particular deployment location within a room, but for instance the threshold might be ~0.2°C. Subsequently there follows first and second rapid change regions 702, 703. A region may be identified as a rapid change region on the basis that the temperature varies by more than the threshold temperature magnitude and the temperature increases or decreases by greater than or equal to a gradient threshold through that period of time. If the gradient in a region is less than the gradient threshold then that region is identified as a trend region. The first rapid change may be identified as a period of time between a door being opened and closed, during which time warm air escapes the room. While the door is open a second person enters the room. The entry of a person may raise the temperature by circa 1,5°C relative to the initial stable temperature in stable region 701. It may be expected that a temperature sensor will indicate an increase in temperature that stabilises after 1,5°C has been added to the baseline of 16°C. During rapid change region 703 the room is heating up as a result of the person (that is, a heat source) entering the room. Therefore, the temperature differential of 1.5°C can then be identified and attributed to the added presence of a person into the space. Subsequently, a second stable region 704 may be identified where the temperature is 17.5°C and the margin that may be considered acceptable to infer that a single person has entered a room may be ±0.2°C (that is, the threshold temperature magnitude is exceeded). That is, the temperature in the second stable region 704 may be determined according to equation (5) presented below: To + Tpi=17.5±0.2°C (5) Where To is the initial room temperature in region 701 and Tpi is the heat output from a single person.

[0065] It will be appreciated that the temperature change yielded by a single person entering a room will be dependent on the particular volume of the room, amongst other parameters, hence the necessity of a calibration process. This may vary between for instance 0.8 to 1,5°C, and this temperature increase is indicated by the arrow 705 in figure 7. Furthermore, the area underneath the graph in stable region 704, relative to the baseline temperature in stable region 701, may be calculated and is indicative of the energy released into the room as a result of a single person entering (this is indicated by the shaded area under region 704 indicated by numeral 706.

[0066] When the temperature is stable or within an acceptable noise margin, then a small droop (that is, a trend region) not exceeding, for instance 0.2°C may be identified within the temperature data as a trend region. As noted previously, a trend region is one in which the temperature magnitude variation exceeds the magnitude threshold but the gradient of that variation does not exceed the gradient threshold. In a trend region it may be inferred that there is no change in the number of people present. In some examples after a person enters a room the initial temperature rise may be almost exponential, and detected as a rapid change region, but after a short time the temperature stabilises (identified as a new stable region) and may rise afterwards marginally (identified as a further trend region). Any change that exceeds a temperature magnitude shift above a predetermined value, or a significant temperature gradient indicative of a rapid change region may be used to infer a further room event, that is a thermal gain or loss indicative of a new event (for instance people entering or leaving the room, a door being opened or closed, a window being opened or closed etc.).

[0067] For the raw, filtered or curve fitted temperature data as set out above according to the preceding signal processing steps, for the third step of identifying stable, trend and rapid change regions the analytical method employed for windowing the values for temperature within bands covering each stable region may be set as follows.

[0068] Mathematically an arrays of values (Tn, timen), (Tn-i ,timen-i),..... (To, time0) should outline values T according to equation (6) presented below: V Ti e [ To , Tn] we have Ti - AT <= Ti <= Ti + AT

[0069] AT may be 0.2°C for the example above. Equation (6) defines the interval for temperature bands for stable temperature regions. For all the values for T (indexed as i) belonging to the inclusive interval of temperatures from To to n, the value of the i indexed temperature Ti will be within the between the values T- AT and T+AT. i takes any values from 0 to n.

[0070] AT is the height of the bar that defines the stable and horizontal region or interest.

[0071] A transient region (that is, a trend region or a rapid change region) is a region which stands between two stable regions or outlines a continuous significant increase or decrease in temperature. This is outlined by events which may include opening or closing of the door, window, or the introduction of a heating or cooling source.

[0072] With respect to heating or cooling sources, these have a self-converging thermal output and may eventually lead to a stable region with respect to the heat envelope of the space considered.

[0073] Referring now to figure 8, is a flowchart illustrating a method for characterising an indoor space state according to an embodiment of the present invention. At step 801 a computing device 102 receives sensor data from sensor 101.

[0074] At step 802 regions (specifically, stable, trend and rapid change regions) are identified within the sensor data.

[0075] As noted above in connection with figure 7, the identification of regions may comprise analysing changes of sensor data magnitude and sensor data gradients in different time portions. Specifically, a stable region may be indicative of a period of time within which the received data is relatively stable. It may be identified by determining a time period during which the sensor data does not vary by more than a data magnitude threshold. In some cases the threshold is uniform irrespective of the absolute magnitude of the sensor data. In other cases different thresholds may apply in different portions of a sensing range of a sensor.

[0076] A trend region may be indicative of a period of time within which the received data trends upwards or downwards gradually. It may intuitively be understood as a no change region, but with some sensor drift. For the example of a temperature sensor the sensor drift may equate to gradual heat loss or warming within the room as a result of no-optimal insulation. A trend region may be identified based upon a period of time during which the sensor data varies by more than the magnitude threshold but the gradient of sensor data change is less than a gradient threshold.

[0077] A rapid change region may be indicative of a period of time within there is a significant change in the sensor data indicative of an event which has occurred within the room. A rapid change region may be identified based upon a period of time during which the sensor data varies by more than the magnitude threshold and for which the gradient of the sensor data gradient is greater than or equal to the gradient threshold. It will be appreciated that like the threshold temperature magnitude, the threshold temperature gradient may be context specific. However, in some examples the gradient threshold which if exceeded indicates a rapid change region may be ~0.05°C / s (degrees Celsius per second). A trend region may have a markedly lower gradient, for instance ~1 °C / s.

[0078] At step 803 an inference is drawn regarding what each identified region (or groups of regions) relate to. This inference is based on a prior calibration process forthat sensor, that room and typical room states and events that may occur in that room.

[0079] For an identified stable region a room state may identified by looking at the average sensor data during the stable region and using a look up table showing sensor data magnitude bands and corresponding room states for each band. The room states may comprise, for instance, the number of occupants and whether artificial heating or cooling appliances are operational. The number of room states and their corresponding sensor data bands will be specific to the particular installation. In some cases the duration of a stable region may also be used as part of the inference of a room state.

[0080] For an identified rapid change region, the average gradient during that region may be compared to a look up table showing sensor data gradient bands and corresponding room events for each band. The events may include, for instance, a person entering a room, a person leaving a room or the operation or discontinuance of an artificial heating or cooling system. In some cases the duration of a rapid change region may also be used as part of the inference of a room event.

[0081] Figure 9 is a flowchart illustrating a method for characterising an indoor space state according to a further embodiment of the present invention. Steps 801,802 and 803 are as described above for figure 8. Step 901 of filtering the sensor data corresponds to the filtering process described above in connection with figure 5 for reducing noise and fluctuations not indicative of room states or events. Step 902 of curve fitting the sensor data corresponds to the curve fitting process described above in connection with figure 6 for further smoothing the data before regions are identified.

[0082] The above described method for identifying data regions and inferring room states and events has been proven by the present inventor to be particularly appropriate and robust. Nevertheless the skilled person will be aware that alternative data processing techniques may be applied, for instance the use of a trained machine learning algorithm to process sensor data and infer room states and events (again based on a prior calibration process to form a suitable training data set).

[0083] In some embodiments of the present invention, the inference of a room event may take account of two or more concatenated regions. The concatenated regions may comprise at least one stable or trend region and at least one rapid change region. Or they may comprise two or more concatenated rapid change regions. As one example, a sharp drop in temperature sensor data followed by a rapid rise may indicate that a door has been opened, a person has entered, and the door has been closed behind them. Alternatively, an increase in temperature following a stable region may indicate that an artificial heating system has been turned on.

[0084] While the particular example of a temperature sensor has been given above, the sensor may be any suitable type of environmental sensor, for instance, temperature, pressure, humidity or gas. The present inventor has however identified that temperature sensors yield particularly useful information for inferring room states and events such as doors opening and people entering and leaving. Advantageously, this information, obtained from a low cost, may have clear applications for controlling building systems such as HVAC and security systems, as will be apparent to the skilled person. Nevertheless, a calibration process as will be described below may allow similar determinations to be made of room states and events for different sensor types.

[0085] Where a temperature sensor is use, an inferred room state may comprise a state in which the number of heat sources and heat sinks within the indoor state remains static. Similarly, an inferred event that has occurred within the room may comprise one of: a heat source entering or being activated in the indoor space; or a loss of heat being triggered in the room. A heat source entering or being activated in the indoor space may comprise: an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space. A loss of heat being triggered in the room may comprises: an artificial cooling system being activated in the indoor space; an artificial heating system being deactivated within the indoor space; an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; or a person or animal leaving the indoor space.

[0086] When a room state or event is inferred then this information may be output or used in any suitable form. For instance, it may be displayed on a screen or communicated to any required recipient or device. In some cases the information may directly feedback to a building system that is being controlled.

[0087] Turning now to figure 10, this is a flowchart illustrating a method for calibrating a system for characterising an indoor space state according to an embodiment of the present invention. Essentially the calibration process comprises the receipt of sensor data 1001 while also observing 1002 indoor space states and events that occur in the room and correlating 1003 these to the sensor data. In particular, the calibration may for instance comprise the collection of sensor data during periods of time where the number of occupants of a room remains static. Also, for the case of a temperature drop sensed when a door opens, a door may be repeatedly opened and closed and a body of sensor data during open periods be recorded.

[0088] Referring to figure 11, this expands upon the correlation step 1003. For a body of sensor data relating to known time periods when a room is in a static state, sensor data magnitudes may be measured at step 1100 and at step 1101 a series of data magnitude bands for observed room states may be established. Thus, the sensor data may later be compared to the bands to infer a current room state. The calibration process relies on taking initial measurement of given cooling or heat sources and recording such values at typical distances from the sensor, including one distance or more. The values recorded will allow for not only asserting the source of heating or cooling but potentially the distance from such a source. As an example: a fixed heater appliance will provide a temperature profile that is repeatable because it is fixed. Should it be moved then that the changed temperature profile may be indicative of the distance between the sensor and the heater.

[0089] For room events sensor data gradients may be measured at step 1102 during each observed event. At step 1103 a series of gradient bands may established corresponding to the observed events. Thus, the sensor data may later be compared to the bands to infer a current room event.

[0090] It will be appreciated that the number of room states and room events, and thus the number of bands and their divisions will be context dependent.

[0091] Figure 12 illustrates a sensor 101 configured to communicate sensor data to a computing device according to an embodiment of the present invention. For instance, the sensor 101 may form part of the sensing system of figure 1, communicating with the computing device 102. At a minimum the sensor 101 comprises a sensor 1201 capable of generating sensor data indicative of at least one sensed parameter and some form of input / output device 1202 configured to communicate that sensor data to other a computing device across a network. The network may be a wired or wireless network within a home. The sensed parameter may be an environmental parameter, for instance temperature, pressure, humidity, gas (for instance, CO2). It will be appreciated that in other embodiments the sensor device 101 may include further components, for instance memory configured to buffer sensor data for periodic transmission and a processor to control the other parts.

[0092] Figure 13 illustrates a computing device 102 configured to receive and process sensor data to a computing device according to an embodiment of the present invention. The computing device 102 comprises a processor 1301, memory 1302 and some form of input / output device 1303 configured to communicate with the sensor 101 across a network. The memory 1302 stores executable instructions that, in response to execution by the processor 1301, cause the computing device 102 to perform the indoor space characterisation method of figures 8 and 9 or the calibration method of figures 10 and 11.

[0093] It will be appreciated that examples of the present invention can be realized in the form of hardware, software or a combination of hardware and software. Any such software may be stored in the form of volatile or non-volatile storage, for example a storage device like a ROM, whether erasable or rewritable or not, or in the form of memory, for example RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium, for example a CD, DVD, magnetic disk or magnetic tape or the like. It will be appreciated that the storage devices and storage media are examples of machine-readable storage that are suitable for storing a program or programs comprising instructions that, when executed, implement examples of the present invention.

[0094] Accordingly, examples provide a program comprising code for implementing apparatus or a method as claimed in any one of the claims of this specification and a machine-readable storage storing such a program. Still further, such programs may be conveyed electronically via any medium, for example a communication signal carried over a wired or wireless connection and examples suitably encompass the same.

[0095] Throughout this specification, the words “comprise” and “contain” and variations of them mean “including but not limited to”, and they are not intended to (and do not) exclude other components, integers or steps. Throughout this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise. Throughout this specification, the term “about” is used to provide flexibility to a range endpoint by providing that a given value may be “a little above” or “a little below” the endpoint. The degree of flexibility of this term can be dictated by the particular variable and can be determined based on experience and the associated description herein.

[0096] Features, integers or characteristics described in conjunction with a particular aspect or example of the invention are to be understood to be applicable to any other aspect or example described herein unless incompatible therewith. All of the features disclosed in this specification, and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The invention is not restricted to the details of any foregoing examples. The invention extends to any novel feature or combination of features disclosed in this specification. It will be also be appreciated that, throughout this specification, language in the general form of “X for Y” (where Y is some action, activity or step and X is some means for carrying out that action, activity or step) encompasses means X adapted or arranged specifically, but not exclusively, to do Y. 5

[0097] Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

[0098] The reader's attention is directed to all papers and documents which are filed 10 concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference. 13 07 23

Claims

1. A method of inferring events within an indoor space, the method comprising: receiving sensor data from an environment sensor within the indoor space;5 identifying regions within the received sensor data, each region comprising:a stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold;a trend region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient less than a gradient10 threshold; ora rapid change region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient greater than or equal to the gradient threshold; andinferring an event occurring within the indoor space based on an identified15 sequence of two or more identified regions.

2. A method according to claim 1, wherein the inference is based on a calibration method comprising:receiving sensor data from the environment sensor within the indoor space during a 20 plurality of known events within the indoor space;identifying regions within the received sensor data, each region comprising:a stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold;a trend region indicative of a period of time within which the received data25 trends upwards or downwards with a sensor data gradient less than a gradientthreshold; ora rapid change region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient greater than or equal to the gradient threshold; and30 correlating an identified sequence of regions within the sensor data to those knownevents within the indoor space.

3. A method according to claim 1 or claim 2, wherein prior to the step of identification the method further comprises filtering the sensor data.

354. A method according to claim 3, wherein the filtering comprising noise reduction and removal of fluctuations not indicative of change within the indoor space.13 07 235. A method according to claim 3 or claim 4, wherein the filtering comprises passing the sensor data through a weighted window processing filter.5 6. A method according to anyone of the preceding claims, wherein prior to the stepof identification the method further comprises curve fitting the sensor data or filtered sensor data to a polynomial curve.

7. A method according to claim 7, wherein the sensor data or filtered sensor data is 10 fitted to a polynomial curve with an order of two or more.

8. A method according to any one of the preceding claims, wherein identifying a stable region, trend region or rapid change region comprises processing the sensor data, filtered sensor data or curve fitted sensor data using a region weighting mathematical15 expression.

9. A method according to any one of the preceding claims, wherein for at least one identified stable region the method further comprises:identifying a sensor data magnitude or an average sensor data magnitude within 20 the identified stable region;identifying a sensor data magnitude band containing the sensor data magnitude or average magnitude; andinferring an indoor space state based on the identified sensor data magnitude band;25 wherein the inference of an indoor space state is based on a calibration methodcomprising measuring sensor data magnitudes for a plurality of known indoor space states and determining sensor data magnitude bands forthose known indoor space states.

10. A method according to claim 9, wherein for at least one trend region the method 30 further comprises:Inferring that no event has occurred within the indoor space state or that the indoor space remains within a previously identified indoor space state based on a calibration method comprising measuring sensor data magnitudes for a plurality of known indoor space states and determining expected data trends ordrifts in sensor data forthose 35 known indoor space states over time.

11. A method according to any one of the preceding claims, wherein inferring an event occurring within the indoor space further comprises:13 07 23for at least one identified rapid change region:identifying a sensor data gradient or an average sensor data gradient within the identified rapid change region; andidentifying a sensor data gradient band containing the sensor data gradient or average gradient; andwherein the inference of an event that has occurred within the indoor space is further based on a calibration method of measuring sensor data gradients for a plurality of known events within the indoor space and determining sensor data gradient bands for those known events within the indoor space.

12. A method according to claim 11, wherein inferring an event that has occurred within the indoor space is further based upon an identified sequence of two or more different rapid change regions.

13. A method according to claim 12, wherein inferring an event that has occurred within the indoor space is further based upon an identified sequence of two or more different rapid change regions having opposite polarity sensor data gradients.

14. A method according to any one of the preceding claims, wherein the environment sensor comprises:a temperature sensor;a pressure sensor;a humidity sensor; ora gas sensor.

15. A method according to claims 9 and 14, wherein where the environment sensor is a temperature sensor located within the indoor space, an inferred indoor space state comprises a state in which the number of heat sources and heat sinks within the indoor state remains static.

16. A method according to claim 14 or claim 15, wherein where the environment sensor is a temperature sensor located within the indoor space, an inferred event that has occurred within the indoor space comprises one of:a heat source entering or being activated in the indoor space; ora loss of heat being triggered in the room.13 07 2317. A method according to claim 16, wherein a heat source entering or being activated in the indoor space comprises:an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space.

518. A method according to claim 16 or claim 17, wherein a loss of heat being triggered in the room comprises:an artificial cooling system being activated in the indoor space;an artificial heating system being deactivated within the indoor space;10 an aperture, for instance a door or window, being opened allowing warm air tovent from the indoor space; ora person or animal leaving the indoor space.

19. A method according to any one of the preceding claims, wherein the indoor space 15 comprises a room in a building.

20. A computer-readable storage medium having computer-readable program code stored therein that, in response to execution by a processor, cause the processor to perform the method of any one of the preceding claims.2021. An apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to perform the method of any one of claims 1 to 19.25 22. A sensing system for inferring events within an indoor space, the systemcomprising:an environment sensor configured to generate sensor data indicative of an environment parameter within the indoor space; anda computing device configured to:30 receive sensor data from the environment sensor;identify regions within the received sensor data, each region comprising:a stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold;a trend region indicative of a period of time within which the received data35 trends upwards or downwards with a sensor data gradient less than a thresholdgradient; or13 07 23a rapid change region indicative of a period of time within which the received data trends upwards or downwards within a sensor data gradient greater than or equal to a threshold gradient; andinfer an event occurring within the indoor space based on an identified sequence of 5 two or more identified regions.

23. A method for calibrating a method of inferring events within an indoor space, the method comprising:receiving sensor data from an environment sensor within the indoor space during a 10 plurality of known events within the indoor space;identifying regions within the received sensor data, each region comprising:a stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold;a trend region indicative of a period of time within which the received data15 trends upwards or downwards with a sensor data gradient less than a gradientthreshold; ora rapid change region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient greater than or equal to the gradient threshold;20 andcorrelating an identified sequence of regions within the sensor data to known events within the indoor space.

24. A method according to claim 23, wherein the method further comprises:25 measuring sensor data magnitudes for a plurality of known indoor space states anddetermining sensor data magnitude bands forthose known indoor space states; ormeasuring sensor data gradients for a plurality of known events within the indoor space and determining sensor data gradient bands forthose known events within the indoor space.3025. A method according to claim 24, wherein where the environment sensor is atemperature sensor located within the indoor space, a known indoor space state comprises a state in which the number of heat sources and heat sinks within the indoor state remains static.13 07 2326. A method according to any one of claims 23 to 25, wherein where the environment sensor is a temperature sensor located within the indoor space, a known event that has occurred within the indoor space comprises one of:a heat source entering or being activated in the indoor space; or5 a loss of heat being triggered in the room.

27. A method according to claim 26, wherein a heat source entering or being activated in the indoor space comprises:an artificial heating system being activated within the indoor space; or10 a person or animal entering the indoor space.

28. A method according to claim 26 or claim 27, wherein a loss of heat being triggered in the room comprises:an artificial cooling system being activated in the indoor space;15 an artificial heating system being deactivated within the indoor space;an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; ora person or animal leaving the indoor space.20 29. A method according to any one of claims 23 to 28, wherein the calibration isspecific to a particular indoor space and to a particular sensor location within that indoor space.

30. A computer-readable storage medium having computer-readable program code 25 stored therein that, in response to execution by a processor, cause the processor toperform the method of any one of claims 23 to 29.

31. An apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to 30 perform the method of any one of claims 23 to 29.

32. A sensing system for calibrating a method of inferring events within an indoor space, the system comprising:an environment sensor configured to generate sensor data indicative of an35 environment parameter within the indoor space; anda computing device configured to:receive sensor data from the environment sensor during a plurality of known events within the indoor space;identify regions within the received sensor data, each region comprising:a stable region indicative of a period of time within which the received data5 does not vary by more than a magnitude threshold;a trend region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient less than a gradient threshold; ora rapid change region indicative of a period of time within which the10 received data trends upwards or downwards with a sensor data gradient greaterthan or equal to the gradient threshold; andcorrelate an identified sequence of regions within the sensor data to known events within the indoor space.COCM

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