System and method for determining location of asset

Abstract

A computer-implemented method (200) for use in determining a location of an asset in a building of a medical environment comprises a first step of i) tracking (202) the location of the asset in the building over time using a location obtained using a first asset location method. In a second step, the method comprises ii) if the tracked position according to the first asset positioning method indicates that the asset is moving between the first floor of the building and one or more other floors of the building at a rate higher than the first threshold rate in a second region of the first floor of the building, determining that the asset is moving between the first floor of the building and the one or more other floors of the building. If so, it is determined (204) to position the asset using a second asset positioning method in a first area of a first floor of the building.

Description

Technical Field

[0001] Embodiments herein relate to locating assets. In particular but not exclusively, embodiments herein relate to determining the location of assets in a building having a medical environment. Background Art

[0002] This disclosure lies in the field of hospital resource management; and more generally relates to managing resources in a medical and / or care environment.

[0003] Hospitals may use Wi-Fi-based Real-Time Location Systems (RTLS) to locate mobile (e.g., movable) assets such as equipment and people. Wi-Fi positioning is discussed in the paper by Liao et al. (2011) titled “Wifi Positioning: A survey”, International Journal of Communication Networks and Distributed Systems 7(3-4):229-248. See also the paper by Z. Hao, J. Dang, W. Cai and Y. Duan titled “A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion,” IEEE Access, vol. 8, pp. 223765-223781, 2020, doi:10.1109 / ACCESS.2020.3039394.

[0004] While cost-effective, Wi-Fi-based positioning may be inaccurate, sometimes resulting in the reporting of an asset on a different floor of the building than its true location. Incorrect location information may impede the workflow of care providers in a hospital, e.g., a nurse cannot afford the wasted time of looking for a device he / she needs on the wrong floor. Summary of the Invention

[0005] The location of an asset often depends on the data transmitted by tags associated with the asset. The data transmission itself is determined by a relay system installed in the hospital, which can be in the form of Wi-Fi, an infrared system, or an exciter (e.g., a low-frequency beacon). The infrared system has a small transmission radius, which results in a large number of installations, which can quickly become very expensive. Exciter technology is highly reliable because exciters are typically placed at choke points (possibly at entrances and exits on each floor in a hospital), and they transmit addresses at a very low frequency (VLF), typically 125 - 150 KHz per second. The transponder in the form of an asset tag placed on each asset will periodically wake up, listen, and receive the transmitted VLF choke point number. Once this transmission is received, the tag is designed to transmit on ultra-high frequency (UHF) (usually 433 MHz) to a separate receiver infrastructure spread throughout the facility. The UHF receivers, in turn, will transmit back via wires to a central computer, which will determine the location of the asset tag. However, placing exciters is very expensive, and it can be estimated to reach 50,000 euros per hospital.

[0006] Wi-Fi systems are already readily available in most hospitals, but due to the uncertainties associated with location, the data is not highly reliable because Wi-Fi uses radio-based frequencies, which can be interrupted by wireless interference.

[0007] Thus, although hospitals and other healthcare facilities can use Wi-Fi-based real-time location systems (RTLS) to locate mobile assets, Wi-Fi-based location may not be accurate. Location errors can occur not only in the X-Y plane but also on the Z-axis. For example, if a tagged entity is on a particular floor (or level) of a building, the Wi-Fi-based location method may report the tagged entity as being on another floor of the building. If an employee ends up wasting time looking for an asset on the wrong floor of the building, this incorrect location information can impede the workflow of healthcare providers in the hospital.

[0008] As mentioned herein, "floor hopping" is a phenomenon in which an asset location method (e.g., a Wi-Fi RTLS, for example) reports the location of an asset on a different floor of a building than its true location.

[0009] To address floor hopping, a common technique is to place exciters at the entrance and exit points on all floors. The exciter is only activated when a tagged entity passes by the exciter (the mechanism is similar to the device placed at the entrance of a store that detects and sounds an alarm when a person leaves the store carrying unpaid items).

[0010] Thus, even if the WiFi positioning method reports incorrect floor information, this can be overridden by the readings from the beacons. However, as mentioned above, beacons are relatively expensive. Additionally, it is known that the accuracy of WiFi-based RTLS can vary significantly between locations. For example, the location accuracy can be good in some parts of a building while it can be poor in other areas. Therefore, in a medical environment, placing beacons at the entry and exit points of each floor of a building may be over-engineered and unnecessarily expensive.

[0011] The aim of the embodiments of this document is to improve this situation and determine an improved placement of positioning devices (such as beacons in a building of a medical institution).

[0012] Accordingly, in a first aspect, there is provided a computer-implemented method for use in determining the location of an asset in a building of a medical environment. The method includes: i) tracking the location of the asset in the building over time using a location obtained by a first asset positioning method; and ii) determining to use a second asset positioning method to locate the asset in a first region of a first floor of the building if the location tracked according to the first asset positioning method indicates that the asset is moving between the first floor of the building and one or more other floors of the building at a rate higher than a first threshold rate in a second region of the first floor of the building.

[0013] According to a second aspect, there is provided an apparatus for determining the location of an asset in a building of a medical environment. The apparatus includes: a memory including instruction data representing a set of instructions; and a processor configured to communicate with the memory and execute the set of instructions. The set of instructions, when executed by the processor, causes the processor to: i) track the location of the asset in the building over time using a location obtained by a first asset positioning method; and ii) determine to use a second asset positioning method to locate the asset in a first region of a first floor of the building if the tracked location according to the first asset positioning method indicates that the asset is moving between the first floor of the building and one or more other floors of the building at a rate higher than a first threshold rate in a second region of the first floor of the building.

[0014] According to a third aspect, there is provided a system including the apparatus of the second aspect. The system further includes a first device configured to obtain the location of the asset using a first asset positioning method. The first device is configured to: obtain the location of the asset and send the location to the apparatus for use by the apparatus when performing steps i) and ii).

[0015] According to a fourth aspect, there is provided a computer program product comprising a computer-readable medium having computer-readable code embodied therein, the computer-readable code being configured to, when executed by a suitable computer or processor, cause the computer or processor to perform the method of the first aspect.

[0016] Accordingly, a system and method for determining locations in a healthcare facility are provided, wherein a first asset location method can be better enhanced by using a second asset location method to provide cost-effective asset tracking in the healthcare facility. This improves asset tracking and asset utilization in the healthcare environment, thus helping to ensure that clinical staff do not waste time searching for assets registered on incorrect floors by the first asset location method. The floor-hopping analysis process herein analyzes data from a first asset location method, such as a WiFi method, and employs a data-driven approach to identify areas of a building that suffer from floor-hopping. This information can then be used to deploy a second asset location method, such as an actuator-based system, which is needed to address excessive floor-hopping without incurring the costs associated with placing actuators at every entrance and exit point on every floor of a building housing a healthcare facility.

[0017] It is noted that these benefits lie not only in improving the location of assets, but also in directly benefiting the healthcare environment due to the avoidance of unnecessary hardware (e.g., actuators) associated with the second location method. In the actuator example, each actuator is battery-powered, so reducing / optimizing the placement of actuators also means that the hospital can save resources by not having to replace / maintain as many batteries.

[0018] EP3917183 A1 discloses a positioning system comprising a plurality of RFID tags, a plurality of location receivers, and a central server.

[0019] ZHOU HOUPAN et al.: "indoor positionging research based on wireless sensor network topology optimization" discloses a sensor deployment method based on the optimization of the wireless sensor network topology.

[0020] US2021 / 392513A1 discloses a method and system for assigning wireless beacons to locations within a building, wherein the wireless beacons are included in an indoor positioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] To better understand and more clearly illustrate how the embodiments herein are implemented, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0022] Figure 1 illustrates an example apparatus in accordance with some embodiments herein;

[0023] Figure 2 illustrates an example method in accordance with some embodiments herein;

[0024] Figure 3 illustrates an example diagram of the movement of assets between floors of a building in accordance with some embodiments herein;

[0025] Figure 4 illustrates an example flowchart in accordance with some embodiments herein;

[0026] Figure 5 illustrates an example diagram of per - device per - floor jumps for different floors of a building;

[0027] Figure 6 illustrates an example spatial distribution of devices having floor jumps above a first threshold on a particular floor of a building;

[0028] Figure 7 illustrates an example output of a clustering method in accordance with some embodiments herein; and

[0029] Figure 8 illustrates from Figure 6 an example centroid of a cluster output from a clustering method of the assets shown.

[0030] Specific implementation

[0031] Turning now to Figure 1 , in some embodiments, there is a device 100 for displaying a three - dimensional volume of an image on a two - dimensional display. The device may form part of a dedicated device (such as a dedicated medical device), or, the device may form part of a computer device (e.g., a laptop computer, a desktop computer, or other device). In other examples, the device 100 may form part of a cloud / distributed computing arrangement.

[0032] The device includes a memory 104 and a processor 102, the memory 104 includes instruction data representing an instruction set, and the processor 102 is configured to communicate with the memory and execute the instruction set. Generally, the instruction set, when executed by the processor, may cause the processor to execute any embodiment of the method 200 described below. In some implementations, the instruction set may include multiple software and / or hardware modules each configured to perform or for performing individual or multiple steps of the methods described herein.

[0033] More specifically, when executed by a processor, the set of instructions causes the processor to i) track the location of an asset in a building over time using a location obtained by a first asset location method. The processor is then further caused to: ii) if the tracked location according to the first asset location method indicates that the asset is moving between the first floor and one or more other floors of the building in a second region of the first floor of the building at a rate higher than a first threshold rate, determine to use a second asset location method to locate the asset in a first region of the first floor of the building.

[0034] Processor 102 may include one or more processors, processing units, multi-core processors, or modules configured or programmed to control device 100 in the manner described herein. In a particular implementation, processor 102 may include multiple software and / or hardware modules each configured to perform or for performing individual or multiple steps of the methods described herein. In some implementations, for example, processor 102 may include multiple (e.g., interoperable) processors, processing units, multi-core processors, and / or modules configured for distributed processing. Those skilled in the art will understand that such processors, processing units, multi-core processors, and / or modules may be located at different locations and may perform different steps and / or different portions of individual steps of the methods described herein.

[0035] Memory 104 is configured to store program code that can be executed by processor 102 to perform the methods described herein. Alternatively or additionally, one or more memories 104 may be external to device 100 (i.e., separate from or remote from device 100). For example, one or more memories 104 may be part of another device. Memory 104 may be used to store the location of an asset and / or any other data received, calculated, or determined by processor 102 of device 100 or from any interface, memory, or device external to device 100. Processor 102 may be configured to control memory 104 to store the location of an asset and / or any other data received, calculated, or determined by processor 102.

[0036] In some embodiments, memory 104 may include multiple sub-memories, each sub-memory capable of storing one instruction data. For example, at least one sub-memory may store instruction data representing at least one instruction in the set of instructions, while at least one other sub-memory may store instruction data representing at least one other instruction in the set of instructions.

[0037] It should be understood that Figure 1Only the components necessary to illustrate this aspect of the disclosure are shown, and in an actual implementation, device 100 may include additional components to those shown. For example, device 100 may include a battery or other power source for powering device 100 or a device for connecting device 100 to a main power supply. As another example, the device may include a display, such as a screen on a computer, mobile phone or tablet, a screen forming part of a medical device or medical diagnostic tool, or any other screen for displaying / presenting the location or other information processed by the processor described herein. As another example, the device may also include a user input, such as a keyboard, mouse or other input device, which enables a user to interact with the device, for example, to provide initial input parameters to be used in the methods described herein.

[0038] Device 100 may be configured to perform Figure 2 the method 200 shown. Method 200 may be computer-implemented. Method 200 may be used to determine the location of an asset in a building in a medical environment. Briefly, in a first step 202, method 200 includes i) tracking the location of an asset in the building over time using a location obtained using a first asset location method. In a second step 204, method 200 includes ii) determining to use a second asset location method to locate the asset in a first region of a first floor of the building if the tracked location according to the first asset location method indicates that the asset is moving between the first floor of the building and one or more other floors of the building at a rate higher than a first threshold rate in a second region of the first floor of the building.

[0039] As used herein, a medical environment may be a hospital, clinic, doctor's surgery, inpatient facility, outpatient facility, emergency facility, nursing home (e.g., a nursing home for the elderly or disabled) or any other environment providing care and / or medical procedures or treatments.

[0040] The medical environment may be housed in a building. The building may have more than one floor (e.g., storey or level).

[0041] As used herein, an asset may be a device used in a medical facility, for example, to provide care and / or medical procedures and / or medical treatments. Examples of assets include, but are not limited to, beds, wheelchairs, chairs, monitors (such as bedside monitors, heart rate monitors, SpO2 monitors, etc.), drips and / or any other medical devices in a medical facility.

[0042] In other examples, an asset may be a human asset, for example, the devices, systems and methods herein may be deployed to locate medical personnel, such as doctors, nurses, dentists, physiotherapists and / or any other care or medical staff in a medical institution.

[0043] In other examples, the asset can be a fixed asset, e.g., an asset known not to move floors. In an embodiment where the first asset localization method is a Wi-Fi RTLS method, the asset can be a Wi-Fi transmitter or receiver, e.g., in the form of a Wi-Fi "tag", for the purpose of determining the location where Wi-Fi floor hop artifacts occur (e.g., a fixed tag used in a way to control the tag). In such an example, whenever movement between a first floor and one or more other floors is registered, this will be referred to as a signal artifact (e.g., a hop event).

[0044] In step i), the first asset localization method can be any asset localization method, e.g., any process for localizing an asset or determining an estimate of where the asset is located. As an example, in some embodiments, the first asset localization method is a Wi-Fi method, also known as a Wi-Fi Real-Time Location System (RTLS). Those skilled in the art will be familiar with using Wi-Fi localization methods, which use the characteristics of nearby Wi-Fi hotspots and other wireless access points to determine where a device is located. As an example, a Wi-Fi localization method can use the strength of received signals to determine the distance from one or more Wi-Fi access points to the asset, in order to triangulate or otherwise determine the location of the asset. Thus, one or more assets can be attached with transmitters and / or receivers suitable for sending and / or receiving wireless signals associated with the Wi-Fi localization method. A summary of various Wi-Fi localization methods is given in the paper by Liao et al. (2011) cited in the Background section.

[0045] However, Wi-Fi is merely an example, and it should be understood that the first asset localization method can be any method suitable for determining / tracking the location of an asset over time.

[0046] In step i), method 200 includes tracking the location of one or more assets in a building over time using the location obtained using the first asset localization method. The method can include obtaining a time series of location estimates from equipment associated with the first asset localization method. The location can be received in response to one or more requests from device 100.

[0047] In some examples, the first asset localization method can provide an estimate of the floor on which the asset is located. In other examples, the first asset localization method can provide x-y-z data that can be mapped to a specific floor of a building. In other words, method 200 can include converting the tracked location into layer data that includes an indication of the floor on which the asset is located.

[0048] Figure 3An example graph 302 showing the temporal movement of an asset tag displaying an asset measured using Wi-Fi RTLS over time is shown. Graph 302 shows the actual movement of the asset from the fourth floor at 306 to the sixth floor at 308. The graph also shows a floor jump artifact 310 where Wi-Fi records a transient "movement" of the artifact across floors even though the asset was not actually moving at that time.

[0049] In step 204, method 200 includes: if the tracked position according to the first asset positioning method indicates that the asset is moving between the first floor of a building and one or more other floors of the building at a rate higher than a first threshold rate in a second area of the first floor of the building, then determining to use a second asset positioning method to locate the asset in a first area of the first floor of the building.

[0050] Generally, the second asset positioning method can be used in the same area of the building where the first asset positioning method indicates that the asset is moving between the first floor of the building and one or more other floors of the building at a rate higher than the first threshold. In other words, the first area can overlap or partially overlap with the second area. However, it is also contemplated that the second asset positioning method can be used in a different area of the first floor of the building. For example, if a floor jump higher than the first threshold is detected anywhere on the first floor of the building, the exciter can be placed at the entrance / exit point of the floor. In such an example, the second area of the building can be any area on the first floor of the building (e.g., where a high floor jump rate is detected), and the first area can be the entrance / exit point of the first floor of the building.

[0051] Thus, in some embodiments, the first area includes the entrance point or the exit point of the first floor of the building (e.g., is the entrance point or the exit point of the first floor of the building or in the area of the entrance point or the exit point of the first floor of the building). Thus, method 200 can include determining to locate a position beacon at the entrance point or the exit point. In other words, step ii can include: if it is determined that one or more assets are moving between the first floor of a building and one or more other floors of the building at a rate higher than a first threshold in a second area of the first floor of the building, then determining to use a tag- and beacon-based asset positioning method at the entrance point or the exit point of a specific floor.

[0052] In some embodiments, method 200 includes determining the rate of movement of an asset between different floors of a building. This can be determined by evaluating the time frames in which the asset is located on different floors and calculating the rate of movement within the same time frame. In another example, it can be determined spatially. For example, the rate of movement of different areas of different floors of a building can be calculated by combining (e.g., averaging) the rates of movement of all assets present in each area of a particular floor of the building within the time frame.

[0053] In some embodiments, step i) can be repeated for multiple assets. In other words, method 200 can include repeating step i) for multiple assets. In step ii), the method can then include: determining to use a second asset localization method in a first area of the first floor of the building if the tracked location according to the first asset localization method indicates that multiple assets are moving between floors of the building at an average rate higher than a first threshold rate in a second area of the first floor of the building. Thus, the average rate of movement between floors can be determined for multiple assets.

[0054] In step ii), if the position of an asset according to the first asset localization method indicates that the asset is moving between floors, this may be due to the actual movement of the equipment (e.g., correct signal) or may be due to signal artifacts (e.g., such as floor hopping). Thus, in step ii), a first threshold is used to distinguish these scenarios. The first threshold can be the rate of movement between floors. The first threshold can be the rate of movement between floors, and compared to the case where the rate of movement is lower than the first threshold, movement higher than this rate is more likely to be due to signal artifacts rather than the actual movement of the asset.

[0055] In some examples, a "fingerprint" method can be used to set the first threshold. In other words, control samples of stationary assets or asset tags can be used to set the first threshold. For example, one or more stationary assets or asset tags can be placed in different areas of the first floor of the building, and the recorded movement according to the first asset localization method (between the first floor of the building and one or more other floors of the building) can be monitored. Since the corresponding asset or asset tag is known to be stationary, by definition, any recorded movement is an artifact, such as a floor hopping event. Thus, the recorded "movement" of the stationary asset or the rate of floor hopping can be used to set the first threshold.

[0056] In some examples, the first threshold can be set according to a data-driven method. As an example, Wi-Fi coordinates (X, Y) and floor numbers can be used to understand the location of an asset at the current time point. This allows for a per-floor and per-asset analysis of the location of the asset based on how long the asset has been on a certain floor. It also allows for the visualization of the floor-hopping metric (hops / hour / device). This can give a baseline understanding of the hopping scenarios in a hospital.

[0057] Figure 5 Examples are shown in Plot 500 of the per-asset per-hour floor hops across all floors of a building during a 1-month period measured using a Wi-Fi asset location method.

[0058] After an initial analysis, a threshold is selected for hopping. As an example, the first threshold can be set to 4 hops / hour / device. Thus, if on a particular floor, the average hops / hour / device is greater than 4 hops / hour / device, a second asset location method can be used on that particular floor.

[0059] In another example, the first threshold can be set based on, for example, design or budget requirements. For example, in an embodiment where the second asset location method is an exciter-based method, the first threshold can be set so that a healthcare environment can save x% of its budget on exciters (e.g., instead of placing exciters at each entry point of a floor, only y exciters can be used at the top y floor-hopping locations). As another example, the first threshold can be set such that no more than a predetermined number of exciters are recommended.

[0060] This is shown in Figure 6 which shows Figure 6 a floor plan 600 of an example first floor of a building according to an embodiment herein. The floor plan shows rooms 604 and entry and exit points 606. Point 602 represents the location of an asset with a floor hop (e.g., during a specific time period). Figure 6 Enabling the location of hops on each floor of a building. As Figure 6 shown, the initial exploration allows for the location of areas where frequency hopping occurs on a particular floor. However, it is difficult to obtain the approximate location responsible for the hop from the variance shown in the plotted data.

[0061] One way to better understand the most common locations of floor hops is by clustering the Figure 6 points in

[0062] Thus, generally, method 200 may include determining a location where an asset has moved between a first floor of a building and one or more other floors of the building according to a first asset positioning method. The determined locations may then be clustered into clusters, and a first area of the building in step ii) may be determined according to the locations of the clusters. In other words, the method may include: clustering a plurality of assets that have floor jumps or move between floors of the building at an average rate higher than a first threshold rate during a specific time period into clusters according to the positions of the corresponding floor-jumping assets relative to the floors of the building. The method may then include determining, from the locations of the clusters, a first area of the building in step ii) (wherein a second asset positioning method is used).

[0063] Clustering can be used to find the centroid of the clusters and select the main clusters on a specific floor. Any clustering method can be used to cluster the points and determine the centroid of each cluster. As an example, the Elbow method can be used. Those skilled in the art will be familiar with the Elbow method, which is described in the paper titled: “Determining The Appropriate Cluster Number Using Elbow Method for K-Means Algorithm.” by Humaira, Hestry & Rasyidah, Rasyidah. (2020). Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA) 2018, 24 - 25 January 2018, Padang, Indonesia. The Elbow method clusters the data based on the K-means clustering algorithm and calculates the WCSS (within-cluster sum of squares) error to find the optimal number of clusters from the data.

[0064] Figure 7 is shown in Figure 6 the data shown using the Elbow clustering method. The Y-axis represents the WCSS (within-cluster sum of squares) when the data is clustered into different numbers of clusters, as shown on the X-axis. When the WCSS is plotted with the value of K, the curve looks like an elbow. As the number of clusters increases, the WCSS value decreases (because the points within each cluster are closer together). At Figure 7In it, when K = 1 (at 702), the WCSS value is the largest. When analyzing the graph, it can be seen that the graph changes rapidly at specific points, resulting in an elbow shape. From this point, increasing the number of clusters no longer has a significant impact on WCSS, and the graph starts to move almost parallel to the X-axis. This is because further dividing already tight clusters no longer further reduces the WCSS metric. Therefore, the K value corresponding to the Elbow is the optimal K value or the optimal number of clusters.

[0065] The centroid of the clustering can be found based on WCSS, and these centroids can be plotted as shown in Figure 8 to approximate the position of the jump. Then, we calculate the jump per cluster and remove the top k clusters to reach the desired threshold for the jump.

[0066] In other words, the method can also include identifying the clusters with the highest jump metric, or the clusters with an average movement between the floors of the building at a rate higher than the first threshold rate. The top k clusters can be iteratively removed to reach the desired threshold for the jump. Figure 8 is shown Figure 6 the centroid 802 of the clusters of the assets shown. Then this analysis can be performed across all floors, effectively optimizing the actuator placement and reducing the operating cost.

[0067] Therefore, method 200 can include clustering the locations where (suspected) floor jumps have occurred, and determining the first area of the building in step ii) from the locations of the clusters (e.g., where the second location method is deployed). For example, step ii) can include selecting the first cluster, where the movement rate of each asset in the first cluster between the first floor of the building and one or more other floors of the building is higher than the first threshold rate. The method can then include determining the first area of the building from the location of the first cluster (e.g., from the centroid of the first cluster). In some embodiments, the centroid of the cluster can correspond to a second area (or a point therein) of the first floor of the building.

[0068] Turning to step ii) of method 200, the method can then include determining to use the second asset positioning method in the first area on the first floor. The first area can overlap with the center of the cluster. Alternatively, as a result of finding the clustering on the first floor y (e.g., anywhere), the second asset positioning method can be used in its first area. In such an embodiment, the first area can be, for example, the entrance / exit point of the first floor of the building, a "checkpoint" at the central position on the first floor, or any other location where the asset tag can be expected to pass if the asset is indeed on the first floor.

[0069] In some embodiments, the second asset localization method can be an exciter-based method. For example, the second asset localization method can use a low-frequency location beacon that interacts with a tag placed on the asset to determine whether the asset is within the range of the beacon.

[0070] The beacon, referred to herein as an "exciter", is a device that emits a low-frequency (LF) signal (about 125 Hz). In this asset localization method, the asset is equipped with a tag. Different exciters have different identifiers that are sent along with the LF signal. If the tag moves within the range of the exciter (the range is configurable and can be, for example, 4, 8, or 16 feet depending on the configuration), it picks up the LF signal and sends a report (e.g., to a device such as device 100) indicating the identifier associated with the exciter obtained from the LF signal of the exciter. Thus, the position of the tag near the exciter can be determined. The exciter can be configured to emit its LF signal every 250, 500, or 1000 milliseconds.

[0071] As described above, the exciter can assist WiFi localization in scenarios where there is uncertainty about the floor on which the tag is present. In fact, the floor can be determined based on the position of the exciter of the most recently reported tag.

[0072] In some embodiments, the second asset localization method has a higher accuracy than the first asset localization method. For example, the first asset localization method can be a Wi-Fi localization method, and the second asset localization method can be an exciter-based (e.g., beacon and tag) localization method or an IR-based localization method as described above. In this way, method 200 can be used to determine where to use the second more accurate asset localization method.

[0073] In some examples, the second asset localization method may be more expensive, e.g., computationally or monetarily, to install, use, or maintain. In such examples, method 200 can be used to determine the locations where such an asset localization method can be most advantageously used, and correspondingly, in a medical facility, the first (cheaper) asset localization method is sufficient. In this way, the optimal combination of asset localization methods can be determined for a particular medical facility.

[0074] Thus, in embodiments where the first asset localization mechanism is Wi-Fi RTLS and the second asset localization mechanism is tag and beacon-based asset localization, method 200 can facilitate the identification of areas of a building that suffer from the floor hopping phenomenon associated with the WiFi-based asset localization method. This information can then be used by an enterprise to deploy exciters only where they need to cost-effectively address excessive floor hopping (e.g., without the need to deploy exciters at every entry point and / or exit point on every floor of the building).

[0075] Some of the above steps are summarized in Figure 4 . In this embodiment, the first asset localization method is the Wi-Fi method, and the second asset localization method is the actuator-based method using beacons and tags as described above. However, it should be understood that Figure 4 the method in

[0076] is equally applicable to other asset localization methods.

[0077] In step 402, Wi-Fi data is obtained from a medical environment. In this example, the medical environment is a hospital. Then the hop analysis module 404 analyzes the data in the following manner: Figure 5 In block 406, the data is filtered to obtain a specific date range of interest. Then, according to the asset tags 408, assets 410, and / or the data is further split into individual hospitals 418. Then, the asset distribution map of each floor and each hospital 420 can be drawn in a manner similar to that shown in

[0078] . As another example, if the data is split according to tags or assets (in step 408 or 410), then the hop profile of each tag can be considered individually (in step 412), or the average hop frequency of a group of assets can be considered (as in step 414). Either of these can be plotted in 416.

[0079] Now returning to Figure 2 and method 200, after steps i) and ii), the output of the method can be displayed. For example, method 200 may further include instructing a display to display the time and / or spatial distribution of one or more assets moving between the floors of a building at a rate higher than a first threshold rate. This can indicate to the user areas where it may be more beneficial to use the second asset localization method.

[0080] In some embodiments, method 200 may further include one or more of the following options: initiating the installation of hardware in a building to implement a second asset localization method in a first area of the building; initiating the activation of the second asset localization method in the first area of the building; using the second asset localization method to track one or more other assets in a first area of the first floor of the building; and using a second location estimate of a first asset obtained using the second asset localization method in the first area of the building to correct a first location estimate of the first asset obtained using the first asset localization method.

[0081] As briefly described above, apparatus 100 may also be part of a system. For example, in some embodiments, there is a system that includes apparatus 100 and a first piece of equipment configured to obtain the location of an asset using a first asset localization method. In such an embodiment, the first piece of equipment is configured to obtain the location of the asset and send the location to the apparatus for use by the apparatus when performing steps i) and ii) of method 200.

[0082] In embodiments where the first asset localization method is a Wi-Fi localization method, examples of the first piece of equipment include, but are not limited to, Wi-Fi routers, Wi-Fi repeaters, and any other equipment capable of providing the location of an asset using the Wi-Fi localization method. In an example where the first method uses an exciter (such as a beacon), the first device can be any equipment that can be used to implement the method, such as an exciter.

[0083] In another embodiment, there is provided a computer program product including a computer-readable medium having computer-readable code embodied therein, the computer-readable code being configured to, when executed by a suitable computer or processor, cause the computer or processor to execute one or more of the methods described herein.

[0084] Therefore, it should be understood that the present disclosure also applies to computer programs, particularly computer programs on or in a carrier, which are adapted to implement the embodiments. The program may be in the form of source code, object code, intermediate source and object code, such as in a partially compiled form, or in any other form suitable for use in the implementation of the method according to the embodiments described herein.

[0085] It should also be understood that such a program can have many different architectural designs. For example, the program code implementing the functionality of the method or system can be subdivided into one or more subroutines. Many different ways of distributing the functions among these subroutines will be obvious to those skilled in the art. The subroutines can be stored together in an executable file to form a self - contained program. Such an executable file can include computer - executable instructions, for example, processor instructions and / or interpreter instructions (such as Java interpreter instructions). Alternatively, one or more or all of the subroutines can be stored in at least one external library file and linked, for example, statically or dynamically with the main program at runtime. The main program contains at least one call to at least one of the subroutines. The subroutines can also include function calls to each other.

[0086] The carrier of a computer program can be any entity or device capable of carrying the program. For example, the carrier can include a data memory, such as a ROM, such as a CD ROM or a semiconductor ROM, or a magnetic recording medium, such as a hard disk. In addition, the carrier can be a transmissible carrier, such as an electrical or optical signal, which can be transmitted via an electrical or optical cable or by radio or other means. When the program is embodied in such a signal, the carrier can consist of such a cable or other device or unit. Alternatively, the carrier can be an integrated circuit in which the program is embedded, the integrated circuit being adapted to execute the relevant method or being used in the execution of the relevant method.

[0087] Based on the study of the drawings, the present disclosure, and the appended claims, those skilled in the art can understand and implement variations of the disclosed embodiments. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit can implement the functions of several items recited in the claims. 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 advantageously. A computer program can be stored / distributed on a suitable medium, such as an optical storage medium or a solid - state medium supplied together with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. A computer-implemented method for use in determining the location of an asset in a building of a healthcare environment, the method comprising: i) Tracking the location of the asset in the building over time using a location obtained by a first asset location method; And ii) If the tracked location according to the first asset location method indicates that the asset is moving between the first floor of the building and one or more other floors of the building in a second area of the first floor of the building at a rate higher than a first threshold rate, determining to use a second asset location method to locate the asset in a first area of the first floor of the building, Wherein the second asset location method has a higher accuracy than the first asset location method, and Wherein the method further comprises one or more of the following options: - Initiating the installation of hardware in the building to implement the second asset location method in the first area of the building; - Initiating the activation of the second asset location method in the first area of the building; - Using the second asset location method to track one or more other assets in the first area of the first floor of the building; and - Using a second location estimate of the first asset obtained by the second asset location method to correct a first location estimate of the first asset obtained by the first asset location method in the first area of the building.

2. The method according to claim 1, wherein, The first asset location method is a WiFi Real Time Location System (RTLS).

3. The method according to claim 1 or 2, wherein The second asset location method uses low-frequency location beacons that interact with tags placed on the asset to determine whether the asset is within the range of the beacons.

4. The method according to claim 3, wherein The first area includes an entry point or an exit point of the first floor of the building, and wherein the method comprises determining to locate a location beacon at the entry point or the exit point.

5. The method according to any one of the preceding claims, comprising repeating step i) for a plurality of assets; and Wherein in step ii), the method includes: If the tracked location according to the first asset location method indicates that the plurality of assets are moving between the first floor of the building and one or more other floors of the building in the second area of the first floor of the building at an average rate higher than the first threshold rate, determining to use the second asset location method in the first area of the first floor of the building.

6. The method according to claim 5, wherein in step ii), the method comprises: Determining, according to the first asset location method, the location where the asset has moved between the first floor of the building and one or more other floors of the building; Clustering the determined locations into clusters; And Determining the first area of the building in step ii) from the locations of the clusters.

7. The method according to claim 6, wherein step ii) comprises: Selecting a first cluster, each asset of the first cluster having a movement rate higher than the first threshold rate between the first floor of the building and one or more other floors of the building; And Determine the first area of the building from the location of the first cluster.

8. The method according to claim 7, comprising: Determine the first area of the building from the location of the centroid of the first cluster.

9. The method according to claim 6, 7 or 8, wherein Perform the clustering using the Elbow clustering method.

10. An apparatus for use in determining the location of assets in a building of a healthcare environment, the apparatus comprising: a memory including instruction data representing a set of instructions; and a processor configured to communicate with the memory and execute the set of instructions, wherein the set of instructions, when executed by the processor, causes the processor to perform the following operations: i) Track the location of the asset in the building over time using the location obtained by the first asset location method; and ii) If the tracked location according to the first asset location method indicates that the asset is moving between the first floor and one or more other floors of the building in a second area of the first floor of the building at a rate higher than a first threshold rate, determine to use a second asset location method to locate the asset in a first area of the first floor of the building, wherein the second asset location method has a higher accuracy than the first asset location method, and wherein the set of instructions further causes the processor to perform one or more of the following options: - Initiate the installation of hardware in the building to implement the second asset location method in the first area of the building; - Initiate the activation of the second asset location method in the first area of the building; - Use the second asset location method to track one or more other assets in the first area of the first floor of the building; and - Use the second location estimate of the first asset obtained by the second asset location method to correct the first location estimate of the first asset obtained by the first asset location method in the first area of the building.

11. The apparatus according to claim 10, wherein, The second asset location method has a higher accuracy than the first asset location method.

12. A system, comprising the apparatus according to claim 10 and a first device configured to obtain the location of an asset using a first asset location method; wherein, The first equipment is configured to: - Obtain the location of the asset; and - Send the location to the apparatus for use by the apparatus when performing steps i) and ii).

13. A computer program product including a computer-readable medium having computer-readable code embodied therein, the computer-readable code being configured to cause, when executed by the apparatus according to claim 10 or the processor of the apparatus according to claim 10, the apparatus or the processor to perform the method according to any one of claims 1 to 9.

Images