CN210639486U - Battery-powered sensor device - Google Patents
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- CN210639486U CN210639486U CN201790001437.4U CN201790001437U CN210639486U CN 210639486 U CN210639486 U CN 210639486U CN 201790001437 U CN201790001437 U CN 201790001437U CN 210639486 U CN210639486 U CN 210639486U
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Abstract
A battery powered sensor device (10) is disclosed, comprising a sensor (11) for sensing an analyte of interest and coupled to a processor (13) adapted to periodically process a sensor signal acquired by the sensor, and an accelerometer (15) coupled to the processor for providing an accelerometer signal indicative of a degree of movement of the sensor device, wherein the processor is arranged to determine a processing frequency of the sensor signal in dependence on the degree of movement.
Description
Technical Field
The present disclosure relates to a battery-powered sensor device comprising a sensor coupled to a processor, the processor being adapted to periodically process sensor signals acquired with the sensor.
Background
Battery powered sensor devices are becoming increasingly popular, for example, as part of portable devices such as smartphones, smartwatches, etc., or as stand-alone sensor devices. There may be many reasons why one would like to use such a battery powered sensor device. One reason for this is to monitor air quality, for example, during exercise, such as running, cycling, etc. In developed countries, especially in urban areas, air pollution such as Particulate Matter (PM) pollution, e.g. pollution caused by vehicle emissions, is becoming an increasingly interesting health problem, and thus there is an increasing interest in obtaining and monitoring information about the pollution level.
One challenge associated with such battery-powered sensor devices is ensuring that the battery life is sufficient to provide the user with the desired sensing functionality throughout the period of time that the user wishes to obtain such sensed information, such as during exercise. This is particularly challenging for wearable battery-powered sensor devices, which need to have a small form factor so that the device can be worn comfortably. But this compromises the space available for the battery in such wearable devices.
US2016/0025628a1 discloses a mobile device for sensing particulate matter. The mobile device includes: a housing having an air flow path through which air flows when the mobile device is shaken; an inertial sensor for detecting an acceleration of the mobile device; a light scattering type sensor that irradiates the air flow path with light and detects particulate matter in air flowing through the air flow path; and a controller including a counter for counting the particulate matter detected by the light scattering type sensor and a flow calculator for detecting an air flow rate in the air flow path based on a detection signal of the inertial sensor. In this way, the airflow generating part can be omitted, so that the battery life can be improved. However, such mobile devices still suffer from limited battery life during use of the sensing function. It is therefore desirable to provide a battery powered sensor device in which the battery life, i.e. the operating time of a single charge of the device, can be extended.
US2015/0250385a1 discloses a device for continuous physiological monitoring. The device may include a wearable band that automatically and continuously determines the user's heart rate and provides continuous heart rate data. The sampling rate of the heart rate data may be a function of the activity level of the wearer of the wearable band as determined by the one or more accelerometers to obtain an accurate estimate of the heart rate.
SUMMERY OF THE UTILITY MODEL
The present disclosure is directed to a battery powered sensor device having such an extended battery life.
According to one aspect, there is provided a battery powered sensor device comprising a sensor for sensing an analyte of interest and coupled to a processor, the processor being adapted to periodically process a sensor signal acquired by the sensor, and an accelerometer coupled to the processor for providing an accelerometer signal indicative of a degree of movement of the sensor device, wherein the processor is arranged to determine a processing frequency of the sensor signal in dependence on the degree of movement.
The present disclosure is based on the following insight: there is often a correlation between the degree of movement of a battery powered sensor device and its user's desire for accurate sensing. For example, a user may be less interested in periodic updates of sensor data provided by the sensors when the user is asleep or at rest, while it may be desirable to update the sensor data more frequently when the user is active, e.g., exercising. Furthermore, in situations where the user is moving, the user is more likely to move between different environments with different concentrations of (airborne) analytes of interest (e.g., airborne contaminants), requiring measurements with higher sensor frequencies to accurately acquire such changes. Therefore, by including an accelerometer in the sensor device and setting the processing frequency of the sensor signal according to the movement information provided by the accelerometer, battery power can be maintained for a period of time in which the sensor device is moving relatively little. Since these periods are typically extended periods, battery life can be significantly extended. Additionally, as the frequency of measurements within the relevant measurement time periods increases, the accuracy of the determination of the analyte of interest within these time periods may be improved.
In one embodiment, the processor is arranged to vary the proportion of the processing frequency of the sensor signal in dependence on the degree of movement. Thus, the processing frequency, such as the sampling frequency, of the sensor signal may be configured as the movement increases.
In another embodiment, the battery powered sensor device further comprises a look-up table comprising table entries associated with ranges of movement of the sensor device, each range of movement being associated with a particular processing frequency, wherein the processor is adapted to identify the table entries in the look-up table associated with ranges of movement including degrees of movement; and setting a specific processing frequency associated with the identified movement range as the processing frequency of the sensor signal. For example, each table entry may be associated with a particular type of activity, e.g., rest, walk, jog, run, bike, etc., each activity being defined by a particular range of movement of the sensor device detected by the accelerometer.
The table entries of the lookup table may be configurable. This has the advantage that a user of the battery powered sensor device can update the look-up table, for example to improve the accuracy of the accelerometer for activity detection. Alternatively or additionally, the sensor device may be adapted to extract the range of movement based on a learning phase of the device, wherein the processor monitors data collected with the accelerometer and evaluates the collected data to extract typical movement patterns from the data that may be used to populate the table entries.
The battery powered sensor device may further comprise a further sensor coupled to the processor, the processor being adapted to periodically process further sensor signals acquired by the further sensor, wherein the processor is further adapted to set a processing frequency of the sensor signals in response to a processing result of at least one of the further sensor signals. For example, a change in an environmental condition, such as a CO2 level or a temperature level, determined with another sensor may indicate a change in the contaminant concentration monitored with the sensor, which may be of interest to a user of the battery-powered sensor device. For example, such a change may signal a change in location, e.g., a user moving from an indoor location to an outdoor location or from an outdoor location to an indoor location, which may be associated with a change in an analyte of interest (e.g., air pollution level). Such further sensor signals may therefore advantageously trigger a change in the processing frequency of the sensor signals to monitor possible concentration changes of the contaminant of interest.
The processor may be arranged to set a further processing frequency of the further sensor signal in response to the degree of movement, thereby further extending the battery life of the sensor device.
In an embodiment, the battery powered sensor device further comprises a user interface coupled to the processor, wherein the processor is further adapted to set the processing frequency of the sensor signal in response to a user input received from the user interface. For example, the user may indicate whether he or she is interested in acquiring data from the sensor, which indication may be used to overrule or augment accelerometer data used to control the processor, thereby providing extended operational flexibility for the battery-powered sensor device.
In a particularly advantageous embodiment, the sensor for sensing the analyte of interest is arranged to sense a contaminant and the processor is arranged to derive the contaminant level from a sensor signal provided by the sensor, the sensor device further comprising a communication module coupled to the processor, wherein the processor is arranged to determine whether the communication module has access to an external source for providing an external indication of the contaminant level and, if so, to obtain the external indication of the contaminant level and to deactivate the sensor. In this embodiment, the external indication of the level of the contaminant may be substituted for the level of the contaminant determined from the at least one sensor signal provided by the sensor, for example, if the external indication is sufficiently reliable. This has the advantage that the battery life of the sensor device can be further extended by temporarily deactivating the sensor device which can obtain such reliable external contaminant information.
Preferably, the communication module is arranged to be connected to an external source for providing the external indication over a data communication network. For example, the communication module may be adapted to access an internet service providing such contaminant information to assess whether the sensor may be temporarily deactivated.
The battery powered sensor device may further comprise a data storage device coupled to the processor, wherein the processor is arranged to store sensor data derived from the sensor signal in the data storage device to evaluate the sensor data. This facilitates evaluation of the sensor data at any suitable point in time, for example, after completion of an exercise regimen to assess the level of contaminants to which the user has been exposed during the exercise regimen.
The battery powered sensor device may be a wearable sensor device. Due to the limited and small battery capacity in wearable devices, wearable sensor devices particularly benefit from extended battery life and are particularly likely to be exposed to different ranges of movement depending on the type of activity in which the wearer is engaged.
The sensor for sensing the analyte of interest may be an air pollutant sensor, e.g. an aerosol sensor or a particulate matter sensor, such as a PM10 sensor, a PM5 sensor, a PM2.5 sensor, a UFP (ultra fine particle) sensor, etc. Such sensors are particularly attractive because they can monitor air pollution, and such pollution information may be of particular interest to users engaged in physical activities.
In one embodiment, the battery powered sensor device further comprises an air flow channel housing the sensor, wherein the processor is further adapted to calculate the air pollutant concentration from the sensor signal and the accelerometer signal. In particular, in such an arrangement, the accelerometer data may provide an accurate estimate of the air flow velocity through the air flow channel, and thus the volume of air displaced through the air flow channel during sensor signal acquisition and the associated concentration of air pollutants may be accurately estimated.
According to another aspect, there is provided a method of operating a battery powered sensor device, the sensor device comprising a sensor for sensing an analyte of interest and coupled to a processor, the processor being adapted to periodically process a sensor signal acquired by the sensor, and an accelerometer coupled to the processor, the method comprising receiving, with the processor, an accelerometer signal from the accelerometer, the accelerometer signal being indicative of a degree of movement of the sensor device; determining a degree of movement of the sensor device from the received accelerometer signal; and setting a processing frequency of the sensor signal according to the determined degree of movement. Since this method of operation of the battery powered sensor device enhances operation during intensive activities of the user, i.e. reduces operation during periods of inactivity, the battery life of the sensor device is extended, thereby reducing the risk of battery depletion during intensive activities of the user.
In one embodiment, the battery powered sensor device further comprises a communication module coupled to the processor, the method further comprising: checking with a processor whether an external source for providing an external indication of contaminant level is available; and, where available, receiving an external indication of the contaminant level from the communication module and deactivating the sensor. Such an external indication may be used to further improve the battery life of the battery powered sensor device, as it enables the device to operate in a low power mode, e.g. by deactivating the sensor, in case of a reliable external where the contaminant level of interest may be obtained.
Drawings
Embodiments of the present disclosure will now be described in more detail, by way of non-limiting examples, with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates a battery-powered sensor device according to one embodiment;
FIG. 2 schematically shows a battery-powered sensor device according to another embodiment;
FIG. 3 is a flow diagram of a method of operating a battery-powered sensor device according to one embodiment;
FIG. 4 schematically shows a battery-powered sensor device according to yet another embodiment;
FIG. 5 schematically shows a battery-powered sensor device according to yet another embodiment;
FIG. 6 is a flow chart of a method of operating a battery-powered sensor device according to another embodiment; and
FIG. 7 is a schematic illustration of operating a battery powered sensor device according to one embodiment of the present disclosure.
Detailed Description
It should be understood that the drawings 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.
Fig. 1 schematically illustrates a battery-powered sensor device 10 according to an embodiment of the present disclosure. The battery powered sensor device 10 comprises a sensor 11 communicatively coupled to a processor 13, the processor 13 being further coupled to an accelerometer, i.e. an inertial sensor 15. The battery powered sensor device 10 further comprises a battery 17, the battery 17 powering the various components of the battery powered sensor device 10. In a preferred embodiment, the battery powered sensor device 10 is a portable battery powered sensor device, such as a smart phone, tablet computer or other device comprising sensor functionality. Alternatively, such portable battery powered sensor devices are dedicated devices that may be worn in a user's pocket or the like, or alternatively may be secured to the user's exercise equipment (e.g., a bicycle or the like) using any suitable fastening means, such as clips, straps, or the like. In another preferred embodiment, the battery powered sensor device is a wearable device, such as a smart watch or the like or a dedicated sensor device. In the case of such a wearable battery powered sensor apparatus 10, the apparatus may comprise any suitable fastening means, e.g. a strap, belt or the like for securing the apparatus to the body of the wearer.
The sensor 11 for sensing the analyte of interest may be any suitable type of sensor, for example, a sensor for measuring air pollution. In an exemplary embodiment, the sensor 11 may be an aerosol sensor, for example, a particulate matter sensor, such as a PM2.5 sensor, UFP sensor, PM5 sensor, PM10 sensor, or the like, for detecting particulate matter particles of a particular size. Any suitable embodiment of such a specific substance sensor is contemplated. For example, the battery powered sensor device 10 may be configured as schematically shown in fig. 2, wherein the particulate matter sensor 11 is positioned in an airflow channel 19 through the housing of the battery powered sensor device 10. The housing may be made of any suitable material, such as a plastic material, a metal alloy, or a combination thereof. The airflow channel 19 extends between a first opening and a second opening, both of which are open to the surroundings. For example, in the case of the linear airflow channel 19, the first opening may be opposite to the second opening.
In one embodiment, the particulate matter sensor 11 is an optical particle sensor comprising an optical module (not shown) for emitting light through a sensing region of the airflow channel 19 and a detector (not shown) for detecting light scattered by particles in the air flowing through the sensing region. The optical module may be, for example, an LED module or a laser module, and the detector may be, for example, a photodetector, such as a photodiode or the like. Any suitable optical particle sensor design may be used. Since such optical particle sensors are well known per se, they will not be described in detail here for the sake of brevity.
With the sensor 11 placed in the airflow channel 19, such as has been disclosed in US2016/0025628a1, accelerometer data generated by the accelerometer 15 may be used by the processor 13 to calculate the airflow velocity through the airflow channel 19, using the known volume of the airflow channel 19. The processor 13 may take any suitable form. For example, a processor may be a single discrete device, such as an ASIC, a suitably programmed general purpose processor, a microcontroller, or the like, or may be a distributed apparatus comprising multiple processing devices, including multiple processing devices that cooperate to achieve the functionality of the processor. The processor 13 is generally adapted to process the signals acquired with the sensor 11 at a defined processing frequency. During each sensor signal processing event (e.g., measurement cycle), the processor 13 may activate the sensor 11, e.g., switch the sensor 11 from a sleep state to an active state, thereby generating a sensor signal with the sensor 11, after which the processor 13 may deactivate the sensor 11, e.g., switch the sensor 11 from the active state to the sleep state, to reduce the energy consumption of the sensor 11. For example, in the case of the optical particle sensor 11, during each sensor signal processing event, the processor 13 may activate the optical module and detector to trigger a sensing event. In this way, the obtained sensor readings, e.g., the determined concentration of an analyte of interest (e.g., an airborne contaminant such as a particulate of a particular size), may be stored by the processor 13 in the data storage device 14 for evaluation at a later stage. Any suitable data storage device 14 may be used for this purpose, for example, a memory device such as a flash or EEPROM, solid state disk, magnetic disk, or the like.
According to the present disclosure, the processor 13 is adapted to dynamically adjust the processing frequency of the sensor signals generated by the sensor 11 in response to accelerometer signals provided by the accelerometer 15 indicative of the degree of movement (e.g., speed of movement, etc.) of the battery-powered sensor device 10, such that the battery life of the battery 17 may be extended by reducing the processing frequency of the sensor signals from the sensor 11 during periods of reduced movement (i.e., user activity) of the battery-powered sensor device 10. This will be explained in more detail in connection with fig. 3, which fig. 3 shows a flow chart of a method 100 of operating the battery-powered sensor device 10. The method 100 starts at step 101, for example, by switching on the battery-powered sensor device 10 and optionally setting a default value for the processing frequency of the signal from the sensor 11, after which the method 100 proceeds to step 103, in which the processor 13 receives an accelerometer signal from the accelerometer 15. The accelerometer signal provides an indication of the extent of movement of the battery powered sensor device 10.
In the case of a wearable or portable battery-powered sensor device 10, this degree of movement is typically related to the activity level of its user. For example, when the user is at rest, this will be reflected by the limited movement of the battery powered sensor device 10 detected by the accelerometer 17, whereas an increase in the user's activity causes the accelerometer 17 to generate an accelerometer signal indicating an increase in the movement of the battery powered sensor device 10. Given that a user of the battery-powered sensor device 10 is generally more interested in sensor readings of the sensor 11 during periods of increased activity level, the degree of motion detected with the accelerometer 17 may be used by the processor 13 to adjust the processing frequency of the sensor signal (e.g., to be generated) produced by the sensor 11 accordingly. As described above, this may include activating and deactivating the sensor 11 to conserve energy.
To this end, in step 105, the processor 13 processes the accelerometer signals received from the accelerometer 17 to determine from the received accelerometer signals the degree of movement of the battery powered sensor apparatus 10, and in step 107, sets the processing frequency of the signals generated by the sensor 11 in accordance with the determined degree of movement. In one embodiment, the processor 13 may change the proportion of the processing frequency of the signal using the determined degree of movement, for example, according to a linear relationship (correlation) between the determined degree of movement and the set processing frequency. Alternatively, the processor 13 may access a look-up table (LUT), which may contain table entries associated with the range of movement of the sensor device 10, for example, which may be stored in the data storage device 14. Each movement range is associated with a specific processing frequency, so that the processor 13 may be adapted to identify a table entry in the look-up table associated with the movement range comprising the degree of movement and to set the specific processing frequency associated with the identified movement range as the processing frequency of the sensor signal of the sensor 11.
Each table entry of the LUT may be associated with a particular activity of the user (e.g., resting, walking, jogging, running, cycling, etc.), each activity being associated with a typical degree of movement of the battery-powered sensor device 10. The table entries of the LUT may be pre-populated and/or may be configurable in some embodiments. For example, the processor 13 may be adapted to implement a machine learning routine in which the processor 13 collects data from the accelerometer 15 over a defined period of time and stores the data in the data storage device 14 such that upon completion of the defined period of time, the collected data is evaluated to identify trends in the accelerometer data that may be converted into corresponding table entries in the LUT. In another embodiment, for example, the LUT may be programmed by a user to define corresponding table entries in the LUT, or to modify pre-populated table entries according to user experience, such that the table entries correspond exactly to the corresponding activity levels of a particular user. To this end, the battery powered sensor device 10 may include a user interface (not shown), such as a touch screen, one or more buttons or dials, or the like, to facilitate programming of the LUT by a user. Alternatively, the battery powered sensor device 10 may include a data communication module, such as a bluetooth module, to allow the LUT to be programmed using a user interface of another device (e.g., a smartphone, a tablet, etc.).
It is then checked in step 109 whether the method 100 can be terminated. If not, the method 100 returns to step 103, otherwise, the method terminates at step 111. As will be understood by those skilled in the art, the termination of the method 100 may signal the termination of the sensor data acquisition mode of the battery-powered sensor apparatus 10, which may be followed by a data evaluation mode in which the collected sensor data is evaluated, for example, to determine the air pollution level in case the sensor 11 detects air pollutants, as described hereinbefore. Such data evaluation may be performed by the processor 13, or alternatively may be performed on a remote device communicatively coupled to the battery-powered sensor device 10, e.g., by transmitting the acquired sensor data to a remote device, such as a smartphone, tablet, laptop, desktop computer, or the like. Since this is well known per se, it will not be described in detail here for the sake of brevity. It is fully contemplated that any suitable implementation of such data evaluation may be used in the present disclosure.
In some embodiments, in addition to the above-described detection of the degree of motion being achieved based on accelerometer signals provided by the accelerometer 15, the frequency of processing of sensor signals generated by the sensors 11 of the battery-powered sensor device 10 may be determined by the processor 13 by other mechanisms. For example, the battery powered sensor device 10 may include or may be responsive to a user interface, e.g., a user interface of a remote device communicatively coupled to the battery powered sensor device 10, through which a user may manually define a desired treatment frequency. In this manner, for example, when stationary within a space where an increase in the level of an analyte of interest (e.g., an air pollutant) is expected, e.g., a residential space where food is being prepared or a firewood is being ignited, the user may increase the processing frequency of the sensor signal, thereby allowing the user to trigger an increased (or decreased) sampling rate of the sensor signal from the sensor 11 by the processor 13 without an associated change in the degree of movement of the battery-powered sensor apparatus 10.
Fig. 4 schematically shows a battery-powered sensor device 10 according to another embodiment, which is a battery-powered sensor deviceThere is a further sensor 12 communicatively coupled to the processor 13. Such further sensor 12 may be any suitable type of sensor, such as a temperature sensor, CO2Sensors, etc. As with the sensor 11, the frequency of processing of the further sensor signal generated by the further sensor 12 by the processor 13 may be dynamically adjusted in dependence on the degree of motion derived from the accelerometer signal from the accelerometer 15, as described above. Such adjustment of the processing frequency of the further sensor signal may involve deactivating and activating the further sensor 12 as previously described. In case both the sensor 11 and the further sensor 12 are operated according to a dynamically adjustable processing frequency, the respective processing frequencies may be the same or may be different. In an embodiment, the processor 13 is adapted to set a processing frequency of the sensor signals of the sensor 11 in response to a result of a processing of at least one of the further sensor signals from the further sensor 12. For example, a sudden change in a characteristic, such as temperature or concentration, of a further analyte of interest monitored by the further sensor 12 may indicate a possible increase in a related change in the characteristic monitored by the sensor 11, e.g. a possible increase in a change, such as an air pollution level, e.g. a particle level, which may trigger the processor 13 to adjust (e.g. increase or decrease) the processing frequency of the sensor signal of the sensor 11 without a related change in the degree of movement of the battery-powered sensor device 10.
Fig. 5 schematically shows a battery-powered sensor device 10 according to another embodiment, wherein the battery-powered sensor device 10 further comprises a data communication module 16 coupled to the processor 13. In this embodiment, the sensor 11 is an air contaminant sensor, such as a particulate matter sensor. In a preferred embodiment, the data communication module 16 is a wireless data communication module 16, such as a radio, Wi-Fi module, Bluetooth module, NFC module, or the like. The processor 13 may be adapted to operate the data communication module 16 to access the external source of air pollution data by connecting directly to the external source via a data communication network such as the internet, or indirectly via such a data communication network, such as through a relay device such as a smartphone, tablet, router, or the like.
This method of operation will be described in more detail below in conjunction with fig. 6, which fig. 6 shows a flow chart of a method 100 according to the present embodiment. As previously described, the method 100 may begin at step 101 by enabling the battery-powered sensor device 10. Next, the method 100 proceeds to step 201, in which the processor 13 may check, e.g. by means of the data communication module 16, whether there is a reliable external source of air pollution data available within the communication range of the battery powered sensor device 10. For example, such an external source may be an internet service that provides air pollution information for a particular area, e.g., a cloud-based service, etc.
For example, the processor 13 may contain a list of recognized reliable external sources for providing contamination information for a particular area, and may check whether any of these sources are within communication range. Alternatively, the processor 13 may receive a user instruction to check whether a particular external source of such air pollution data is present within communication range, e.g. through a user interface of the battery powered sensor device 10 or a user interface of a remote device communicatively coupled to the battery powered sensor device 10. Furthermore, if such external sources are present within communication range, air pollution data may be provided for the space where the battery powered sensor device 10 is present.
At step 203, the processor 13 checks whether a reliable external indication of such air pollution level is available in the relevant space (e.g. a specific area such as a part of a town). If no such external indication is available, the method 100 may proceed to step 103, as previously described, where the processing frequency of the sensor signal of the sensor 11 is set according to the accelerometer signal received from the accelerometer 15. This is not repeated here for the sake of brevity only. On the other hand, if it is determined in step 203 that a reliable external indication of the level of airborne contaminants can be obtained, the method 100 proceeds to step 205, wherein a reliable external indication of the level of airborne contaminants is obtained from an external source (e.g. a network connection service for providing such external indication), which means that at least the sensor 11 can be placed in a sleep mode as long as such external indication is available, thereby reducing the energy consumption of the battery-powered sensor device 10 and extending the battery life. As will be immediately understood by those skilled in the art, other components of the battery-powered sensor apparatus 10, such as the accelerometer 15, may also be simultaneously placed in a sleep mode to further extend battery life. Furthermore, the accuracy of the sensor device 10 may be further improved in case the sensor device 10 may rely on such external indications, e.g. generated with professional measuring devices in the relative vicinity of the sensor device 10.
As previously described, it is checked in step 109 whether the method 100 can be terminated in step 111. If not, the method 100 returns to step 201, wherein it is checked again whether a reliable external source of air contaminant levels is available, so that the sleeping components of the battery-powered sensor device 10, such as the sensor 11 and the accelerometer 15, can be woken up in case such a reliable external source is no longer available.
Fig. 7 is a graph of the development of the concentration of particulate matter in the kitchen (Y-axis) over time (X-axis) as shown by the increase in particulate matter concentration when preparing and cooking a meal. Below the X-axis, the top row of equidistant arrows represents the static processing frequency of the sensor signal of a typical prior art battery-powered sensor device, each arrow indicating one sampling event. The lower row of arrows represents the dynamic processing frequency of the sensor signal achieved with the battery-powered sensor device 10 worn by the person preparing the meal according to an embodiment of the present disclosure.
As the wearer starts to prepare a meal, the accelerometer 15 detects an increase in the movement of the battery-powered sensor device 10, which is translated into an increase in the processing frequency of the sensor signals from the sensor 11, as indicated by the arrow with smaller row spacing. Thus, data points 1 of the sudden change in particulate matter concentration in the monitoring interval associated with an increase in activity of the person preparing the meal are all captured by the battery powered sensor device 10, but are missed by prior art battery powered sensor devices operating at a static processing frequency of the sensor signal, thus indicating that the battery powered sensor device 10 is capable of achieving an improvement in the coverage of the contamination event triggered by user activity while extending the battery life of the battery 17. In particular, it has been found that the battery life of the battery 17 of the battery powered sensor device 10 may be increased by up to 50% compared to prior art battery powered sensor devices operating according to a static processing frequency of the sensor signals of the sensor 11, and that a further increase of the battery life may be expected based on the activity level of the user of the battery powered sensor device 10.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of other elements or steps than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements. In the device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. 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.
Claims (14)
1. A battery powered sensor device, comprising a particle sensor (11) coupled to a processor (13) adapted to periodically process a sensor signal acquired with the sensor, and an accelerometer (15) coupled to the processor for providing an accelerometer signal indicative of a degree of movement of the sensor device, wherein the processor is arranged to set a processing frequency of the sensor signal in dependence of the degree of movement.
2. A battery-powered sensor device as claimed in claim 1, characterized in that the processor (13) is arranged to vary the proportion of the processing frequency of the sensor signal in dependence on the degree of movement.
3. A battery powered sensor device according to claim 1 or 2, further comprising a look-up table comprising table entries associated with ranges of movement of the sensor device, each range of movement being associated with a specific processing frequency, wherein the processor (13) is adapted to:
identifying a table entry in the lookup table associated with a range of movement that includes the degree of movement; and
setting the particular processing frequency associated with the identified range of movement as the processing frequency of the sensor signal.
4. The battery-powered sensor apparatus of claim 3, wherein the table entry is configurable.
5. Battery-operated sensor device according to any of claims 1, 2 and 4, characterized by further comprising a further sensor (12) coupled to the processor (13), the processor being adapted to periodically process further sensor signals acquired with the further sensor, wherein the processor is further adapted to set a processing frequency of the sensor signals in response to a processing result of at least one of the further sensor signals.
6. A battery powered sensor device as claimed in claim 5, characterized in that the processor (13) is arranged to set a further processing frequency of the further sensor signal in response to the degree of movement.
7. A battery powered sensor device according to any of claims 1, 2, 4 and 6, further comprising a user interface coupled to the processor, wherein the processor (13) is further adapted to set a processing frequency of the sensor signal in response to user input received from the user interface.
8. A battery powered sensor device according to any of claims 1, 2, 4 and 6, characterized in that the particle sensor (11) is arranged to sense a contaminant and the processor (13) is arranged to derive a contaminant level from a sensor signal provided by the sensor, the sensor device further comprising a communication module (16) coupled to the processor, wherein the processor is arranged to:
determining whether the communication module has access to an external source for providing an external indication of the contaminant level, and if so, obtaining the external indication of the contaminant level and deactivating the sensor.
9. A battery powered sensor device according to claim 8, characterized in that the communication module (16) is arranged to be connected to the external source for providing the external indication over a data communication network.
10. A battery powered sensor device according to any of claims 1, 2, 4, 6 and 9, further comprising a data storage device (14) coupled to the processor (13), wherein the processor is arranged to store sensor data derived from the sensor signals in the data storage device for evaluation of the sensor data.
11. The battery-powered sensor apparatus of any one of claims 1, 2, 4, 6, and 9, wherein the sensor apparatus is a wearable sensor apparatus.
12. A battery-powered sensor device according to any one of claims 1, 2, 4, 6 and 9, characterized in that the particle sensor (11) is an air contaminant sensor.
13. Battery-operated sensor device according to claim 12, further comprising an air flow channel (19) accommodating the particle sensor (11), wherein the processor (13) is further adapted to calculate an air contaminant concentration from the sensor signal and the accelerometer signal.
14. A battery-powered sensor device according to claim 12, characterized in that the particle sensor (11) is an aerosol sensor or a particle sensor.
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2016000646 | 2016-11-21 | ||
| CNPCT/CN2016/000646 | 2016-11-21 | ||
| EP17152804.5 | 2017-01-24 | ||
| EP17152804 | 2017-01-24 | ||
| PCT/EP2017/079646 WO2018091673A1 (en) | 2016-11-21 | 2017-11-17 | Battery-powered sensor device and operating method |
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| CN210639486U true CN210639486U (en) | 2020-05-29 |
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| US11185241B2 (en) | 2014-03-05 | 2021-11-30 | Whoop, Inc. | Continuous heart rate monitoring and interpretation |
| KR102163738B1 (en) | 2014-07-24 | 2020-10-08 | 삼성전자주식회사 | mobile device being capable of sensing particulate matter and particulate matter sensing method thereof |
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