Systems and methods for managing motion detection of electronic devices, and related electronic devices

Abstract

To provide embodiments for managing operations of sensors in an electronic device.SOLUTION: According to certain aspects, an electronic device may detect a change in motion from an initial set of sensor data generated by a sensor(s). A memory cache may store the initial set of sensor data or additional sensor data generated by the sensor(s). The electronic device may initiate a supplemental algorithm that analyzes cached data. On the basis of, the analysis of the cached data and whether the change in motion has been confirmed or whether additional motion has been detected, the electronic device may manage an operation of the supplemental algorithm.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Patent Application No. 17 / 145,007, filed on January 8, 2021, and U.S. Patent Application No. 17 / 145,134, filed on January 8, 2021, which are hereby incorporated herein by reference in their entirety.

[0002] Technical Field The present disclosure relates to managing the operation of sensors on an electronic device, and more particularly, to managing the operation of various sensors and algorithms for processing corresponding sensor data.

Background Art

[0003] Background of the Invention The description of the background of the invention provided herein is for the purpose of presenting the overall context of the present disclosure. The achievements of the inventors within the scope described in this background section of the invention, as well as aspects of the description that are not eligible as prior art at the time of filing, are not recognized as prior art to the present disclosure, either explicitly or implicitly.

[0004] Electronic devices such as smartphones and other devices are constantly being technically improved. Generally, electronic devices incorporate more sensors and / or improved sensors to facilitate various functionalities, modes, and applications related to the electronic device. Along with the improvement of device capabilities, in the situation where sensors are added and / or improved, the resource management of the device becomes more difficult. Specifically, the use of additional sensors consumes more power, among other increased resource usages, and utilizes a larger bandwidth of the central processing unit (CPU).

Summary of the Invention

Problems to be Solved by the Invention

[0005] Electronic devices are generally designed with sufficient CPU and memory capabilities to accommodate these improved capabilities and CPU demands. However, this can increase the hardware costs associated with manufacturing the electronic devices. In addition, when sensors operate actively in the best signal-to-noise ratio (SNR) mode, noise increases in the environment of other devices. Furthermore, since devices are usually designed with sufficient heat dissipation to compensate for worst-case operating conditions, the devices often end up being larger than necessary, which can become a constraint on physical design, raise user expectations, and potentially shrink the market. [Means for solving the problem]

[0006] overview Depending on the implementation, an electronic device may manage multiple algorithms that process data from one or more sensors, such as radar sensors and / or ultrasonic sensors. The sensor(s) may continuously generate a set of sensor data, which an initial motion recognition algorithm can analyze to detect changes in the target's motion in the electronic device's vicinity. Once a change in motion is detected, the electronic device may cache the set of sensor data in memory and initiate a supplemental motion recognition algorithm to process the data from the sensor. The electronic device may also facilitate "clutter removal." In this process, a portion of the sensor data cached in memory that does not show motion can be removed.

[0007] An auxiliary motion recognition algorithm can analyze a set of sensor data cached in memory and, based on this analysis, identify changes in the motion initially detected by the initial motion recognition algorithm. If the auxiliary motion recognition algorithm does not identify a change in motion, the change in motion initially detected by the initial motion recognition algorithm may be considered a false positive.

[0008] In situations where a false positive is detected, the electronic device can stop its auxiliary motion recognition algorithm. Therefore, the electronic device only needs to revert to running the initial motion recognition algorithm, saving computational, memory, power, and / or thermal resources.

[0009] In situations where a change in motion is detected, an auxiliary motion recognition algorithm can process additional sensor data generated by the sensor to enhance various functionalities. For example, the auxiliary motion recognition algorithm could be a gesture recognition algorithm that can detect user gestures performed in the vicinity of an electronic device.

[0010] In other implementations, the sensor may operate in a first mode to generate a corresponding first set of sensor data in the first mode. An early algorithm may analyze this first set of sensor data in the first mode to detect changes in the movement of a target in the vicinity of the electronic device. In response, the sensor may further operate in a second mode to generate a corresponding set of sensor data in the second mode, which can be cached in memory.

[0011] An electronic device may initiate an auxiliary algorithm to retrieve and analyze cached sensor data. Based on any movement detected in the cached sensor data, the electronic device may continue or terminate the operation of the auxiliary algorithm.

[0012] In additional implementations, the sensor may operate in a low-sensitivity mode to generate a corresponding set of low-sensitivity sensor data. Initial algorithms could analyze this set of low-sensitivity sensor data to detect changes in the movement of a target in the vicinity of an electronic device.

[0013] The electronic device may initiate a timeout window during which the same or a different sensor may operate in high-sensitivity mode to generate a corresponding set of high-sensitivity sensor data. The electronic device may further initiate an auxiliary algorithm to process the set of high-sensitivity sensor data to confirm any changes in motion that were originally detected.

[0014] In addition, the initial algorithm may analyze an additional set of low-sensitivity data generated during the timeout window to determine that no further movement was detected during that timeout window. As a result, the electronic device may determine that the change in motion initially detected by the set of high-sensitivity sensor data was actually a false positive.

[0015] In some embodiments of the disclosed subject matter, a method is provided that is performed on a computer for managing motion detection functionality on an electronic device. The method performed on a computer includes the steps of: by a processor, obtaining a set of sensor data from the sensor of the electronic device; by the processor, detecting a change in target motion relative to the electronic device based on an analysis of the set of sensor data; by caching the set of sensor data in the memory of the electronic device; and by the processor, starting an auxiliary motion recognition algorithm based on the detection of the change in motion; and by using the sensor data cached in memory. The process includes the steps of analyzing a set of data using an auxiliary motion recognition algorithm initiated by the processor, and confirming changes in motion using the auxiliary motion recognition algorithm based on an analysis of the set of sensor data cached in memory.

[0016] In some embodiments, the method performed by the computer further includes the step of removing at least a portion of the set of sensor data cached in memory that does not show motion.

[0017] In some embodiments, the step of analyzing a set of sensor data cached in memory includes, at a minimum, the step of analyzing a set of sensor data from which non-moving portions have been removed, using an auxiliary motion recognition algorithm initiated by the processor.

[0018] In some embodiments, the method performed on a computer further includes the step of obtaining an additional set of sensor data from the sensor of an electronic device by a processor, and analyzing the additional set of sensor data by an auxiliary motion recognition algorithm.

[0019] In some embodiments, the method performed on a computer further includes the steps of attempting to detect further changes in motion based on an analysis of an additional set of sensor data by an auxiliary motion recognition algorithm, if no detection is made, and terminating the auxiliary motion recognition algorithm.

[0020] In some embodiments, the method performed on a computer further includes the step of obtaining an additional set of sensor data from an additional sensor of an electronic device by a processor, and analyzing the additional set of sensor data by an auxiliary motion recognition algorithm.

[0021] In some embodiments, the step of detecting a change in motion includes detecting a change in the motion of a target relative to an electronic device based on an analysis of a set of sensor data by an initial motion recognition algorithm performed by a processor.

[0022] In some embodiments of the disclosed subject matter, an electronic device is provided. The electronic device includes a sensor, a memory, and a processor interfaced with the sensor and the memory. The processor obtains a set of sensor data from the sensor, detects a change in the movement of a target with respect to the electronic device based on an analysis of the set of sensor data, caches the set of sensor data by the memory, initiates an auxiliary motion recognition algorithm based on detecting the change in movement, obtains the cached set of sensor data from the memory, analyzes the cached set of sensor data in the memory by the auxiliary motion recognition algorithm, and is configured to confirm the change in movement by the auxiliary motion recognition algorithm based on an analysis of the cached set of sensor data in the memory.

[0023] In some embodiments, the processor is further configured to remove at least a portion of the set of sensor data cached in the memory, and at least a portion of the set of sensor data does not indicate movement.

[0024] In some embodiments, the processor analyzes the set of sensor data with at least a portion that does not indicate movement removed by the auxiliary motion recognition algorithm.

[0025] In some embodiments, the processor is further configured to obtain an additional set of sensor data from the sensor and analyze the additional set of sensor data by the auxiliary motion recognition algorithm.

[0026] In some embodiments, the processor is further configured to fail to detect a further change in movement based on an analysis of the additional set of sensor data by the auxiliary motion recognition algorithm and terminate the auxiliary motion recognition algorithm.

[0027] In some embodiments, the electronic device further includes an additional sensor, and the processor is further configured to obtain an additional set of sensor data from the additional sensor and analyze the additional set of sensor data by an auxiliary motion recognition algorithm.

[0028] In some embodiments, the processor analyzes a set of sensor data by an initial motion recognition algorithm to detect a change in a target motion with respect to the electronic device.

[0029] In some embodiments of the disclosed subject matter, a method executed by a computer for managing a motion detection function on an electronic device is provided. The method executed by the computer includes obtaining, by a processor, a first set of sensor data in a first mode from sensors of an electronic device operating in a first sensitivity mode; detecting, by an initial motion recognition algorithm, a change in a target motion with respect to the electronic device based on an analysis of the first set of sensor data in the first mode; obtaining, by the processor, a set of sensor data in a second mode from sensors operating in a second sensitivity mode based on detecting the change in the motion; caching the set of sensor data in the second mode in a memory of the electronic device; obtaining, by the processor, a second set of sensor data in the first mode from sensors operating in the first sensitivity mode; starting, by the processor, an auxiliary motion recognition algorithm; and analyzing, by the auxiliary motion recognition algorithm started by the processor, the set of sensor data in the second mode cached in the memory.

[0030] In some embodiments, the method executed by the computer further includes analyzing, by the initial motion recognition algorithm, a second set of sensor data in the first mode.

[0031] In some embodiments, the method performed by the computer further includes the steps of: obtaining an additional set of sensor data in the second mode from a sensor operating in the second sensitivity mode using a processor; and analyzing the additional set of sensor data in the second mode using an auxiliary motion recognition algorithm.

[0032] In some embodiments, the method performed by the computer further includes the step of removing at least a portion of the set of second-mode sensor data cached in memory, the set of second-mode sensor data that does not show motion, and the set of second-mode sensor data cached in memory is analyzed by an auxiliary motion recognition algorithm initiated by the processor, which removes at least a portion of the second-mode sensor data.

[0033] In some embodiments, the method performed on a computer further includes the step of having the processor terminate an auxiliary motion recognition algorithm.

[0034] In some embodiments, the step of terminating the auxiliary motion recognition algorithm includes the step of attempting to detect a change in motion relative to an electronic device based on an analysis by the auxiliary motion recognition algorithm of a set of second-mode sensor data cached in memory, but finding no detection, and then terminating the auxiliary motion recognition algorithm.

[0035] In some embodiments of the disclosed subject matter, sensor activity in an electronic device is managed. A method is provided that is performed on a computer. The method is performed on a computer and includes the steps of: obtaining a set of low-sensitivity sensor data from at least one sensor of an electronic device operating in low-sensitivity mode using a processor; detecting a change in the movement of a target relative to the electronic device based on an analysis of the set of low-sensitivity sensor data by the processor; obtaining a set of high-sensitivity sensor data from at least one sensor operating in high-sensitivity mode using a processor during a set time based on the detection of the change in movement; obtaining an additional set of low-sensitivity sensor data from at least one sensor operating in low-sensitivity mode using a processor; analyzing the set of high-sensitivity sensor data and the additional set of low-sensitivity sensor data using a processor; confirming a change in movement relative to the electronic device based on an analysis of the set of high-sensitivity sensor data; and, after a set time has elapsed, determining, based on an analysis of the additional set of low-sensitivity sensor data, that no further changes in movement relative to the electronic device have been detected, and considering the confirmation of change in movement based on the analysis of the set of high-sensitivity sensor data as false positive.

[0036] In some embodiments, the step of acquiring a set of low-sensitivity sensor data includes the step of the processor acquiring a set of low-sensitivity sensor data from a first sensor among at least one sensor operating in low-sensitivity mode; the step of acquiring a set of high-sensitivity sensor data includes the step of the processor acquiring a set of high-sensitivity sensor data from a second sensor among at least one sensor operating in high-sensitivity mode; and the step of acquiring an additional set of low-sensitivity sensor data includes the step of the processor acquiring an additional set of low-sensitivity sensor data from a first sensor operating in low-sensitivity mode.

[0037] In some embodiments, the step of checking for changes in motion relative to an electronic device includes detecting subsequent changes in motion relative to the electronic device based on an analysis of a set of highly sensitive sensor data.

[0038] In some embodiments, the method performed by the computer further includes the step of starting an algorithm by the processor to consume a set of high-sensitivity sensor data acquired from at least one sensor operating in high-sensitivity mode, based on the detection of a change in motion, over a set period of time. In some embodiments, the algorithm for consuming the set of high-sensitivity sensor data is inactive until it is started based on the time period. In some embodiments, the method performed by the computer further includes the step of terminating the algorithm for consuming the set of high-sensitivity sensor data and requesting at least one sensor to cease generating the set of high-sensitivity sensor data.

[0039] In some embodiments, the step of analyzing a set of high-sensitivity sensor data and an additional set of low-sensitivity sensor data includes, by the processor, the step of analyzing the additional set of low-sensitivity sensor data using an initial algorithm, and by the processor, the step of analyzing the set of high-sensitivity sensor data using a subsequent algorithm.

[0040] In some embodiments, the step of obtaining a set of high-sensitivity sensor data includes the steps of requesting the processor to at least one sensor to generate a set of high-sensitivity sensor data, and the step of the processor receiving the set of high-sensitivity sensor data from at least one sensor.

[0041] In some embodiments of the disclosed subject matter, an electronic device is provided. The electronic device includes a first sensor configured to operate in a low-sensitivity mode, a second sensor configured to operate in a high-sensitivity mode, and an interface to the first and second sensors. The system comprises a processor which acquires a set of low-sensitivity sensor data from a first sensor, detects a change in the target's movement relative to an electronic device based on an analysis of the set of low-sensitivity sensor data, initiates a timeout window based on the detection of the change in movement, acquires a set of high-sensitivity sensor data from a second sensor while the timeout window is active, acquires an additional set of low-sensitivity sensor data from the first sensor, analyzes the set of high-sensitivity sensor data and the additional set of low-sensitivity sensor data, detects further changes in the movement relative to the electronic device based on an analysis of the set of high-sensitivity sensor data, and after the timeout window has elapsed, determines, based on an analysis of the additional set of low-sensitivity sensor data, that no movement relative to the electronic device was detected, and considers the detection of further changes in movement based on the analysis of the set of high-sensitivity sensor data as a false positive.

[0042] In some embodiments, the first sensor is a radar sensor and the second sensor is an ultrasonic sensor.

[0043] In some embodiments, the processor is further configured to initiate an algorithm that consumes a set of highly sensitive sensor data acquired from a second sensor. In some embodiments, the algorithm that the processor uses to consume the set of highly sensitive sensor data is inactive until it is initiated based on a time zone. In some embodiments, the processor is further configured to terminate the algorithm that consumes the set of highly sensitive sensor data and request the second sensor to cease generating sets of highly sensitive sensor data.

[0044] In some embodiments, the processor is further configured to analyze the set of high-sensitivity sensor data and an additional set of low-sensitivity sensor data using an initial algorithm, and then analyze the set of high-sensitivity sensor data using a subsequent algorithm, in order to analyze the set of high-sensitivity sensor data and an additional set of low-sensitivity sensor data.

[0045] In some embodiments, the processor is further configured to obtain a set of high-sensitivity sensor data from a second sensor by requesting the second sensor to generate a set of high-sensitivity sensor data in order to obtain a set of high-sensitivity sensor data from the second sensor.

[0046] In some embodiments of the disclosed subject matter, non-temporary computer-readable memory storing instructions is provided, and one or more processors of an electronic device, by executing these instructions, obtain a set of low-sensitivity sensor data from at least one sensor of the electronic device operating in low-sensitivity mode, analyze the set of low-sensitivity sensor data to detect a change in the motion of a target relative to the electronic device, and, based on the detection of the change in motion, obtain a set of high-sensitivity sensor data from at least one sensor operating in high-sensitivity mode, obtain an additional set of low-sensitivity sensor data from at least one sensor operating in low-sensitivity mode, analyze the set of high-sensitivity sensor data and the additional set of low-sensitivity sensor data to detect further changes in motion relative to the electronic device based on the analysis of the set of high-sensitivity sensor data, and, after the set time has elapsed, determine, based on the analysis of the additional set of low-sensitivity sensor data, that the additional set of low-sensitivity sensor data does not indicate further changes in motion relative to the electronic device, and consider the detection of further changes in motion based on the analysis of the set of high-sensitivity sensor data to be false positive.

[0047] In some embodiments, in order to obtain a set of low-sensitivity sensor data, one or more processors, by instruction, obtain a set of low-sensitivity sensor data from a first sensor among at least one sensor operating in low-sensitivity mode, and high-sensitivity sensor data To obtain a set of high-sensitivity sensor data, one or more processors, by instruction, obtain a set of high-sensitivity sensor data from a second sensor among at least one sensor operating in high-sensitivity mode, and to obtain an additional set of low-sensitivity sensor data, one or more processors, by instruction, obtain an additional set of low-sensitivity sensor data from a first sensor operating in low-sensitivity mode.

[0048] In some embodiments, based on detecting a change in motion, one or more processors, by instruction, further initiate an algorithm that consumes a set of sensitive sensor data acquired from at least one sensor operating in sensitive mode over a set period of time.

[0049] In some embodiments, one or more processors may, by instruction, further terminate the algorithm that consumes the set of high-sensitivity sensor data and request at least one sensor to cease generating the set of high-sensitivity sensor data.

[0050] In some embodiments, in order to analyze a set of high-sensitivity sensor data and an additional set of low-sensitivity sensor data, one or more processors, by instruction, analyze the additional set of low-sensitivity sensor data using an initial algorithm and then analyze the set of high-sensitivity sensor data using a subsequent algorithm.

[0051] In the attached figures, similar reference figures, through individual figures, refer to elements that are identical or functionally similar, and the attached figures, together with the following detailed description, are incorporated into this specification and form part thereof, and also serve to illustrate conceptual embodiments, including the claimed embodiments, in order to further illustrate the various principles and benefits of those embodiments. [Brief explanation of the drawing]

[0052] [Figure 1] This figure shows exemplary electronic devices that may facilitate the described functions according to several embodiments. [Figure 2] This is an illustrative diagram relating to techniques for managing sensor operation using a memory cache, according to several embodiments. [Figure 3] This is an illustrative diagram relating to another technique for managing sensor operation using a memory cache, according to several embodiments. [Figure 4] This is an illustrative diagram relating to the management of the operation of one or more sensors according to several embodiments. [Figure 5A] This figure shows illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on those interactions, according to several embodiments. [Figure 5B] This figure shows illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on those interactions, according to several embodiments. [Figure 5C] This figure shows illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on those interactions, according to several embodiments. [Figure 5D] This figure shows illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on those interactions, according to several embodiments. [Figure 5E] This figure shows illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on those interactions, according to several embodiments. [Figure 5F] This figure shows illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on those interactions, according to several embodiments. [Figure 6] This is a flowchart illustrating a method for an electronic device to manage motion detection functionality, according to several embodiments. [Figure 7] This is a flowchart of another method for an electronic device to manage motion detection functionality, according to several embodiments. [Figure 8]This is a flowchart illustrating a method for an electronic device to manage sensor activity, according to several embodiments. [Figure 9] This is a block diagram of an electronic device according to several embodiments. [Modes for carrying out the invention]

[0053] Detailed explanation Systems and methods for managing motion detection functions in electronic devices are described. In certain embodiments, the electronic device may consist of one or more sensors, such as radar sensors, ultrasonic sensors, and / or other sensors. The sensors may operate in various modes depending on various situations and commands. Similarly, the electronic device may execute various algorithms and applications for processing sensor data from sensors operating in various modes. Generally, various sensor modes and various algorithms consume varying amounts of device resources. Therefore, the embodiments described are configured to manage the operation of the sensors and the device algorithms to reduce / improve the efficiency of resource consumption.

[0054] Generally, an electronic device may execute an initial algorithm to detect motion or other events in the vicinity of the electronic device based on sensor data generated by a given sensor. Based on the detected motion, the electronic device may initiate a subsequent algorithm to process the original sensor data and / or additional sensor data. Alternatively, or in addition to the above, the electronic device may activate additional sensors to process the resulting data generated by those additional sensors. The electronic device may utilize a memory cache to more efficiently and effectively determine whether to continue executing the subsequent algorithm. If the electronic device determines that the subsequent algorithm is no longer needed, it may terminate the subsequent algorithm.

[0055] By managing sensor operation, sensor modes, and algorithms for processing sensor data, the resource consumption of electronic devices can be managed more efficiently and effectively. Therefore, more affordable hardware components can be used in electronic devices, and fewer hardware components may be required, thus lowering the manufacturing cost of such devices. In addition, the battery life and overall lifespan of electronic devices can be extended. Furthermore, sensor noise in the environment of such electronic devices can be reduced. It should be understood that other benefits are also conceivable.

[0056] In the exemplary electronic device 100 shown in Figure 1, the described embodiments may be incorporated and / or facilitated or implemented. Electronic device 100 may be any type of electronic device, such as a mobile device (e.g., a smartphone), a display assist device, a desktop computer, a notebook computer, a tablet, a phablet, a GPS (Global Positioning System) or GPS-enabled device, a wristwatch, eyeglasses, a bracelet, other wearable electronic devices, a PDA (Personal Digital Information Processing Terminal), a pager, and / or the like.

[0057] The electronic device 100 may include a user interface 101, which may be embodied as a touchscreen or other type of display capable of displaying or presenting visual content. In embodiments where the user interface 101 is a touchscreen, the user interface 101 may incorporate a set of touch-sensitive input panels that enable user selection or interface with the electronic device 100. The electronic device 100 may further include a housing 102 that incorporates or may contain various components (including the user interface 101). In addition, the electronic device 100 may be powered by alternating current (AC) configured to supply power to the electronic device 100 and its components. The system may include a power component 116, the AC power component 116 which may supply power or receive power from a power source. It should be understood that additional or alternative power sources, such as one or more batteries or other power sources, are conceivable.

[0058] According to the embodiment, the electronic device 100 may include various sensors, user interface components, and / or similar. In particular, as shown in Figure 1, the electronic device 100 may include a pair of infrared image sensors 103, 109 and an image sensor 104. In addition, the electronic device 100 may include a speaker 106 that can be configured to output sound. Furthermore, the electronic device 100 may include a dot projector 108 and a flood illuminator 110. According to the embodiment, the pair of infrared image sensors 103, 109, the dot projector 108, and the flood illuminator 110 may be used by the electronic device 100 to unlock various functions of the electronic device 100, in particular (for example, unlocking the user interface 101), among several functionalities. The electronic device 100 may also include an ambient light sensor (ALS) 105 that can support various proximity detection functions. It should be understood that alternative and additional sensors are conceived. For example, one of the sensors could be an ultrasonic sensor configured to emit ultrasonic waves. Furthermore, it should be understood that sensors and components can be arranged in various configurations and combinations.

[0059] The electronic device 100 may further include a radar chip 107. Generally, the radar chip 107 can detect and measure the characteristics of objects based on the interaction of radio waves with distant and / or nearby objects. The radar chip 107 may include a set of transmitter components that emit radio waves, which are scattered or redirected by objects in their path, and some portion of the energy is reflected back or blocked by a set of receiver components. The radar chip 107 may be bidirectional and configured to emit signals in two or more directions (e.g., in front of and behind the electronic device 100), or it may be directional and configured to emit signals in one direction (e.g., in front of the electronic device 100). The processor 115 or controller may inspect or analyze the received waveforms to detect the presence of objects and may also estimate some of the characteristics of these objects, in particular distance and size.

[0060] Conventional radar designs rely on fine spatial resolution relative to target size to decompose various objects and identify their spatial structure. Such spatial resolution generally requires a wide transmission bandwidth, a narrow antenna beamwidth, and a large antenna array. According to the embodiment, the radar chip 107 can be integrated into the top of the electronic device 100 (or another location or part of the electronic device 100) by employing a sensing paradigm that may be based on motion rather than spatial structure. Thus, the electronic device 100 may support algorithms, applications, etc. (generally "algorithms") that do not need to form a clearly defined image of the spatial structure of a target, as opposed to, for example, an optical imaging sensor. Consequently, the algorithm does not need to, and does not need to, generate an identifiable image of the target for certain purposes such as presence detection and / or gesture detection.

[0061] The processor 115 can perform various signal processing functions and functionalities to process temporal changes in the received radar signal, such as detecting and resolving minute movements. In operation, the radar chip 107 transmits a frequency-modulated signal at a specific frequency or frequency range (e.g., 50-70 GHz, or other frequencies or frequency ranges) to receive a superposition of reflections from nearby objects or people (generally "targets"). Between one transmission and the next, the processor 115 can induce a sub-millimeter change in the target's position that may cause an identifiable timing shift in the received signal. Metric-scale displacements can be detected. These shifts may manifest as Doppler frequencies proportional to the target's velocity across multiple transmission windows. By decomposing the various Doppler frequencies, the signal processing function can identify various targets moving in various motion patterns. According to the embodiment, the signal processing function may include a combination of custom filters and coherent integrating means that can increase the underlying signal-to-noise ratio (SNR), attenuate undesirable interference, and distinguish reflections from the target from noise and clutter. These signal processing functions enable the radar chip 107 to operate at low power within the constraints of the electronic device 100.

[0062] The electronic device 100 may support various modes or algorithms for processing data from the radar chip 107 and / or other sensors as described herein. Generally, one mode may process and analyze signals having a first frequency (or frequency range), and another mode may process and analyze signals having a second frequency (or frequency range) different from the first frequency. For example, one of the modes may be a “presence mode” that can be configured to detect the presence of a nearby target (e.g., a person) of the electronic device 100. In this example, the presence of the target may be detected when the target enters the space in which the electronic device 100 is located. Another mode may be a “gesture mode” that can be configured to detect and recognize gestures performed by a person nearby the electronic device 100 (e.g., in front of the user interface 101). In this example, the gesture may be a “macro” gesture (e.g., a hand movement to switch active applications) or a “micro” gesture (e.g., a simulation of turning a dial using the index finger and thumb). Generally, the processor 115 may execute various algorithms using various machine learning techniques.

[0063] Each mode can operate independently and therefore may have different levels of precision, such as varying degrees of resource consumption, including computation, memory, power, and / or heat. For example, the gesture mode can process short-range radio waves emitted from the radar chip 107, while the presence mode can process long-range radio waves emitted from the radar chip 107. In addition, short-range radio waves may have a higher SNR and be more accurate, while long-range radio waves may have a lower SNR and be less accurate. However, the gesture mode, which processes short-range radio waves, may consume more resources, while the presence mode, which processes long-range radio waves, may consume fewer resources.

[0064] The radar chip 107 may operate in multiple modes simultaneously (i.e., radiate both short-range and long-range radio waves over a period of time), and similarly, the processor 115 of the electronic device 100 may simultaneously execute algorithms for processing the received waveforms resulting from the radiated short-range and long-range radio waves. According to one embodiment, long-range radar sensing may be achieved by a frequency-modulated sustained wave (FMCW) radar using a slow frequency sweep from a first frequency to a second frequency. In contrast, the short-range radar mode may use a faster frequency sweep from the first frequency to the second frequency. To conserve resource usage, it may be advantageous to limit the amount and type of algorithms that the processor 115 executes. For example, if a person is not actually performing a gesture in the vicinity of the electronic device 100, it may be advantageous to deactivate the processor 115 or not execute a gesture mode to conserve various resources.

[0065] According to the embodiment, different sensor modes and device algorithms may operate based on different circumstances. In detail, the sensor mode and / or algorithm may be disabled or enabled based on the time of day, day of the week / month / day, month / day, time since the last motion detection, and / or other circumstances. For example, the gesture mode may be disabled between 1 AM and 5 AM. These modes can be activated. As another example, the sleep detection mode can be deactivated between 9 AM and 9 PM. As an additional example, the high-sensitivity sensor mode can be disabled if more than 10 minutes have passed since the last motion detection. Thus, resources can be saved by selectively disabling these modes and algorithms. It should be understood that these situations may be static, or may be adjusted according to the usage of the electronic device 100, or may be explicitly activated or deactivated by the user, depending on several factors or conditions.

[0066] Furthermore, according to the embodiment, detection by a particular sensor or sensor may trigger the activation or initiation of other particular sensor modes and device algorithms. Generally, a low-sensitivity mode with low resource usage for a given sensor may detect neighboring motion of the electronic device 100 and initiate a high-sensitivity mode with high resource usage, which may operate on that given sensor or another sensor. The electronic device 100 may also initiate an algorithm to process the high-sensitivity sensor data. Furthermore, the sensor data generated as a result of the high-sensitivity mode may be used to confirm (or not confirm) the initially detected motion. If further motion is indicated in the high-sensitivity data, the electronic device 100 may continue the operation of the initiated algorithm and, accordingly, enhance its operation and functionality.

[0067] The electronic device 100 may include a memory 120 to which a processor 115 may interface. The memory 120 may include a main memory portion and a cache portion for temporarily storing certain data that the processor 115 may access. According to one embodiment, the processor 115 may store certain sensor data (e.g., sensor data from a radar chip 107) in the cache portion so that it can verify certain detected actions or movements. In addition, or instead, the processor 115 may initiate a certain algorithm that accesses and analyzes the data stored in the cache portion to enable more accurate detection or recognition of movements or actions. Since machine learning algorithms require a certain amount of time before accurately detecting a target and its movements, the cache portion of the memory 120 allows the algorithm to access the necessary data before the target arrives in a zone and / or before the target performs a certain movement or gesture in that zone.

[0068] Figure 2 is a signal diagram 200 relating to various embodiments of the system and method. The signal diagram 200 includes a memory 220 (such as the memory 120 described with reference to Figure 1), a processor 215 (such as the processor 115 described with reference to Figure 1), and one or more sensors 225 (such as one or more of the various sensors discussed with reference to Figure 1). In embodiments, the sensor 225 may include a single sensor (e.g., a single radar sensor) or multiple sensors (e.g., a radar sensor and an ultrasonic sensor). Furthermore, the memory 220, the processor 215, and the sensor 225 may be embodied in a single electronic device or integrated into a single electronic device. In addition, the memory 220 may be a hardware cache accessible by the processor 215, configured to cache data that the processor 215 accesses at high speed. It should be understood that the various functionalities shown in the signal diagram 200 may appear at various times or in various orders in relation to other functionalities.

[0069] Signal diagram 200 may begin with sensor 225(or more) generating sensor data (230). For example, if sensor 225 is a radar sensor, the radar sensor may emit radio waves and detect the resulting waveform. According to the embodiment, sensor 225(or more) may operate in a first operating mode when generating sensor data. For example, if sensor 225 is a radar sensor, the presence of a target in the vicinity of the electronic device It may emit radio waves with long-range frequencies for purposes such as detection. The processor 215 can acquire generated sensor data from the sensor 225 (multiple sensors are possible) (232). It should be understood that the sensor 225 (multiple sensors are possible) continuously generates sensor data, and the processor 215 can continuously acquire this data.

[0070] The processor 215 may analyze the acquired sensor data to determine whether motion has been detected (234). More specifically, the processor 215 may analyze the sensor data (e.g., the received waveform) to determine whether the sensor data indicates motion. When analyzing the sensor data, the processor 215 may execute an initial algorithm, such as a motion detection algorithm configured to process sensor data from the sensor 225 (or more) operating in a first operating mode. If the processor 215 does not detect motion ("NO"), processing may return to (230 / 232), terminate, or proceed to other functionalities. Otherwise, if the processor 215 detects motion ("YES"), it may supply the sensor data to the memory 220 (236). After receiving the sensor data, the memory 220 may store (cache) the sensor data for later access by the processor 215 (240). Please understand that when sensor 225(or more) generates additional sensor data, memory 220 can cache the sensor data in a rolling manner, and processor 215 can retrieve the additional sensor data.

[0071] After detecting motion, the processor 215 may also initiate an auxiliary motion recognition algorithm (238). According to the embodiment, the auxiliary motion recognition algorithm may differ from the initial algorithm that the processor 215 executes in (234) when it detects motion. For example, the processor 215 may detect motion in (234) while operating in presence mode, and the auxiliary motion recognition algorithm may be associated with gesture mode. In general, the auxiliary motion recognition algorithm may consume more resources (e.g., computation, memory, electricity, and / or heat) than the initial algorithm.

[0072] The processor 215 may also acquire sensor data cached in memory 220 (242). In certain embodiments, the processor 215 may perform “clutter removal” or background subtraction technique on the sensor data acquired from memory 220 (244). When performing clutter removal, the processor 215 may analyze the acquired sensor data to remove portions of data that do not indicate motion. The remaining data may then include data that indicates (or may indicate) motion.

[0073] When executing the auxiliary motion recognition algorithm, the processor 215 may analyze the cached sensor data with or without removing background data (i.e., data in which no motion is detected) (246). In relation to the analysis of the cached sensor data by the auxiliary motion recognition algorithm, the processor 215 may determine whether the motion detected in (234) has been confirmed. By analyzing the cached sensor data, the auxiliary motion recognition algorithm may determine whether the motion detection by the initial algorithm in (234) was false positive. If the auxiliary motion recognition algorithm does not confirm the detected motion ("NO", i.e., the motion detection by the initial algorithm was false positive), the process may return to (230 / 232), terminate, or proceed to other functionalities.

[0074] If the auxiliary motion recognition algorithm confirms the detected motion ("YES," i.e., if the motion detection by the initial algorithm was not false positive), the processor may continue the operation of the auxiliary motion recognition algorithm. In detail, the processor 215 may request the sensor 225(or more) to generate and supply additional sensor data (249). After receiving the request, the sensor 225(or more) will generate additional sensor data (250) may generate additional sensor data. According to the embodiment, the sensor 225(or more) may generate additional sensor data while operating in a mode different from the operating mode in which the sensor data was generated in (230). For example, the initial sensor data generated in (230) may come from the sensor 225(or more) operating in presence mode, which generates long-range radio waves, and the additional sensor data generated in (250) may come from the sensor 225(or more) operating in gesture mode, which generates short-range radio waves. The sensor 225(or more) may supply additional sensor data to the processor 215 (251).

[0075] It should be understood that an additional sensor other than the one that generated the sensor data in (230) may generate additional sensor data. Therefore, in this implementation, the processor 215 may request the additional sensor to generate additional sensor data (249), and the additional sensor may generate (250) additional sensor data and supply it to the processor 215 (251).

[0076] An auxiliary motion recognition algorithm, executed by processor 215, may analyze additional sensor data after it has been received (252). The auxiliary motion recognition algorithm may enhance various functionalities when analyzing the additional sensor data. For example, if associated with a gesture mode, the auxiliary motion recognition algorithm may detect a specific gesture (e.g., a gesture of turning a dial clockwise) and enhance a specific action based on that gesture (e.g., increasing the volume in a music playback application).

[0077] If the auxiliary motion recognition algorithm attempts to detect further changes in motion from additional sensor data and no detection is made, the processor 215 may terminate the auxiliary motion recognition algorithm. Thus, the processor 215 may revert to executing the initial algorithm, for example, to conserve various resources of the electronic device. It should be understood that the processor 215 may continue to execute the initial algorithm throughout the signal diagram 200, or may terminate the initial algorithm in response to the auxiliary motion recognition algorithm confirming motion detection in (248).

[0078] Figure 3 is a signal diagram 300 relating to various embodiments of the system and method. The signal diagram 300 includes a memory 320 (such as the memory 120 described with reference to Figure 1), a processor 315 (such as the processor 115 described with reference to Figure 1), and one or more sensors 325 (such as one or more of the various sensors discussed with reference to Figure 1). In embodiments, the sensor 325 may include a single sensor (e.g., a single radar sensor) or multiple sensors (e.g., a radar sensor and an ultrasonic sensor). Furthermore, the memory 320, the processor 315, and the sensor 325 may be embodied in a single electronic device or integrated into a single electronic device. In addition, the memory 320 may be a hardware cache accessible by the processor 315, configured to cache data that the processor 315 accesses at high speed. It should be understood that the various functionalities shown in the signal diagram 300 may appear at various times or in various orders in relation to other functionalities.

[0079] Signal diagram 300 may begin with sensor 325(or more) generating sensor data in a first mode (330). For example, if sensor 325 is a radar sensor, the radar sensor may emit radio waves and detect the resulting waveform. According to the embodiment, when sensor 325(or more) generates sensor data in a first mode, it may operate in a first operating mode having a first sensitivity. For example, if sensor 325 is a radar sensor, it may emit radio waves with long-range frequencies, such as to detect the presence of a target in the vicinity of an electronic device. Processor 315 receives from sensor 325(or more) Sensor data in the first mode can be acquired (332). It should be understood that the sensor 325 (or more) continuously generates sensor data in the first mode, and the processor 315 can continuously acquire this data.

[0080] The processor 315 may analyze the acquired sensor data in the first mode to determine whether motion has been detected (334). More specifically, the processor 315 may analyze the sensor data in the first mode (e.g., the received waveform) to determine whether the sensor data in the first mode indicates motion. When analyzing the sensor data in the first mode, the processor 315 may execute an initial motion recognition algorithm configured to process the sensor data in the first mode originating from the sensor 325(or more) operating in the first operating mode. If the processor 315 does not detect motion ("NO"), processing may return to (330 / 332), terminate, or proceed to other functionalities.

[0081] Otherwise, if the processor 315 detects motion ("YES"), it may request the sensor 325(or more) to generate and supply sensor data in a second mode (336). The sensor 325(or more) may generate sensor data in a second mode after receiving the request (338). According to the embodiment, the sensor 325(or more) may generate sensor data in a second mode while operating in a second mode different from the first operating mode in which it generated sensor data in a first mode in (330). For example, the sensor data in a first mode generated in (330) may come from a sensor 325(or more) operating in presence mode, which generates long-range radio waves, and the sensor data in a second mode generated in (338) may come from a sensor 325(or more) operating in gesture mode, which generates short-range radio waves. The sensor 325(or more) may supply sensor data in a second mode to the processor 315 (340).

[0082] In some scenarios, the determination in (334) is based on the processor 315 determining whether the sensor data in the first mode includes movement exceeding a certain speed, which may represent the minimum speed of movement that may include a gesture. If the processor 315 determines that the sensor data in the first mode includes movement exceeding a certain speed ("YES"), processing may continue (336). If the processor 315 determines that the sensor data in the first mode does not include movement exceeding a certain speed ("NO"), processing may return to (330 / 332), terminate, or proceed to other functionalities.

[0083] It should be understood that an additional sensor, separate from the sensor that generated the sensor data in (330), may generate sensor data in a second mode. Therefore, in this implementation, the processor 315 may request the additional sensor to generate sensor data in a second mode (336), and the additional sensor may generate (338) sensor data in a second mode and supply it to the processor 315 (340).

[0084] In addition, the processor 315 may supply sensor data in a second mode to the memory 320 (342). After receiving the sensor data in a second mode, the memory 320 may store (cache) the sensor data in a second mode for later access by the processor 315 (348). When the sensor 325(or more) generates sensor data in a second mode, the memory 320 may cache the sensor data in a second mode in a rolling manner, and the processor 315 may retrieve the sensor data in a second mode.

[0085] The processor 315 may acquire additional first mode sensor data generated by the sensor 325 (or more) operating in the first operating mode (344). In addition, the processor 315 may initiate an auxiliary motion recognition algorithm (346). According to the embodiment, the auxiliary motion recognition algorithm is initiated when the processor 315 detects motion (334) This may differ from the initial motion detection algorithm that runs in (334). For example, processor 315 may detect motion in (334) while operating in presence mode, and the auxiliary motion detection algorithm may be associated with gesture mode. In general, the auxiliary motion detection algorithm may consume more resources (e.g., computation, memory, electricity, and / or heat) than the initial motion detection algorithm.

[0086] The processor 315 may also acquire sensor data of a second mode cached in memory 320 (350). In certain embodiments, the processor 315 may perform “clutter removal” or background subtraction technique on the sensor data of a second mode acquired from memory 320 (352). When performing clutter removal, the processor 315 may analyze the acquired sensor data of a second mode to remove portions of data that do not indicate motion from the acquired sensor data of a second mode. Thus, the remaining data may include data that indicates (or may indicate) motion.

[0087] The processor 315, which executes the auxiliary motion recognition algorithm, may analyze cached second-mode sensor data with or without background data (i.e., data in which no motion is detected) (354). In embodiments, this step may improve the motion detection and recognition capabilities of the auxiliary motion recognition algorithm by allowing it to access initially detected motion data before the target enters a zone adapted to the auxiliary motion recognition algorithm. In addition, the processor 315 may analyze additional first-mode sensor data when executing the initial motion recognition algorithm (356). Thus, the processor 315 may execute the initial motion recognition algorithm and the auxiliary motion recognition algorithm simultaneously.

[0088] The processor 315 may acquire additional second-mode sensor data generated by the sensor 325(or more) operating in the second mode (358). The processor 315 may also execute an auxiliary motion recognition algorithm to analyze the additional second-mode sensor data and, based on the analysis, determine whether motion has been detected (360). According to one embodiment, if the processor 315 detects motion based on the additional second-mode sensor data, the target may be in a zone adapted to the auxiliary motion recognition algorithm. For example, the auxiliary motion recognition algorithm may be associated with a gesture mode, and the detected motion may be associated with a gesture performed by the target. By analyzing both the initial (cached) second-mode sensor data and the additional second-mode sensor data, the accuracy of motion detection and recognition by the auxiliary motion recognition algorithm may be improved. In some scenarios, for example, the first motion algorithm may start the second algorithm when it detects motion exceeding a certain speed that may represent the lowest speed motion that may include a gesture. In addition, once activated, the second algorithm can search / analyze cached data to determine whether the cached data contains gestures.

[0089] If the processor 315 detects motion ("YES"), it may continue to acquire and analyze additional second-mode sensor data. If the processor 315 does not detect motion ("NO"), it may terminate the auxiliary motion recognition algorithm (362). Thus, resource conservation of the electronic device is improved by not running the auxiliary motion recognition algorithm when the second-mode sensor data does not indicate motion that the auxiliary motion recognition algorithm would normally process. Consequently, the processor 315 may revert to simply running the initial motion recognition algorithm, for example, to conserve various resources in the electronic device.

[0090] Figure 4 is a signal diagram 400 related to various embodiments of the system and method. 400 includes a processor 415 (such as the processor 115 described with reference to Figure 1) and one or more sensors 425 (such as one or more of the various sensors discussed with reference to Figure 1). In embodiments, the processor 415 and the sensor 425(or more) may be embodied in a single electronic device or incorporated into a single electronic device. It should be understood that the various functionalities shown in signal diagram 400 may appear at various times or in various sequences in relation to other functionalities.

[0091] In the first implementation, sensor 425 may consist of two separate sensors. For example, sensor 425 may include a radar sensor configured to detect overall motion and an ultrasonic sensor configured to detect respiratory rate. In this implementation, the first sensor of sensor 425 may be a low-sensitivity sensor and, accordingly, may generate low-sensitivity sensor data with a low false-positive rate, while the second sensor of sensor 425 may be a high-sensitivity sensor and, accordingly, may generate high-sensitivity sensor data with a high false-positive rate. Furthermore, in this implementation, the first sensor may be bidirectional and therefore configured to emit signals in two or more directions (e.g., in front of and behind the electronic device), while the second sensor may be directional and therefore configured to emit signals in one direction (e.g., in front of the electronic device). It should be understood that both sensors may be bidirectional or directional.

[0092] In the second implementation, the sensor 425 (or multiple sensors) may be a single sensor (e.g., a radar sensor). In this implementation, the single sensor may operate in multiple modes, such as a first mode (e.g., a long-range radar configuration) that generates low-sensitivity data with a low false-positive rate, and a second mode (e.g., a short-range radar configuration) that generates high-sensitivity data with a high false-positive rate. Therefore, although Figure 4 shows a single sensor interface with the processor 415 to generate both low-sensitivity and high-sensitivity sensor data, it should be understood that multiple sensors may interface with the processor 415 to generate their respective sensor data.

[0093] Signal diagram 400 may begin with sensor 425(or more) generating a set of low-sensitivity sensor data (430). For example, if sensor 425 is a radar sensor, the radar sensor may emit radio waves and detect the resulting waveform. According to the embodiment, sensor 425(or more) may operate in a first operating mode when generating sensor data. For example, if sensor 425 is a radar sensor, it may emit radio waves with long-range frequencies, such as to detect the presence of a target in the vicinity of an electronic device. Processor 415 may acquire the generated low-sensitivity sensor data from sensor 425(or more) (432). It should be understood that sensor 425(or more) may continuously generate low-sensitivity sensor data, and processor 415 may continuously acquire it.

[0094] The processor 415 may analyze the acquired low-sensitivity sensor data to determine whether motion has been detected (434). More specifically, the processor 415 may analyze the low-sensitivity sensor data (e.g., the received waveform) to determine whether the low-sensitivity sensor data indicates motion. When analyzing the low-sensitivity sensor data, the processor 415 may execute an initial algorithm, such as a motion detection algorithm configured to process the low-sensitivity sensor data originating from the sensor 425 (or more) operating in a first operating mode. If the processor 415 does not detect motion ("NO"), processing may return to (430 / 432), terminate, or proceed to other functionalities.

[0095] Otherwise, if the processor 415 detects motion ("YES"), it may initiate a timeout window (435), which can be of various lengths (e.g., 10 seconds, 20 seconds, or other lengths). In addition, the processor 415 also detects sensor 4 A request may be made to 25(or more) to generate and supply high-sensitivity sensor data (436). After receiving the request, the sensor 425(or more) may generate high-sensitivity sensor data (438).

[0096] According to the embodiment, the sensor 425(or more) can generate high-sensitivity sensor data while operating in a subsequent mode different from the first operating mode in which low-sensitivity sensor data was generated in (430). For example, the low-sensitivity sensor data generated in (430) may originate from the sensor 425(or more) operating in presence mode, which generates long-range radio waves, and the high-sensitivity sensor data generated in (438) may originate from the sensor 425(or more) operating in gesture mode, which generates short-range radio waves. It should be understood that low-sensitivity sensor data and high-sensitivity sensor data can be generated from different sensors.

[0097] The sensor 425(or more) can supply high-sensitivity sensor data to the processor 415 (442). In addition, the processor 425 can acquire additional low-sensitivity sensor data from the sensor 425(or more) (444). According to the embodiment, the sensor 425(or more) can continue to operate in the first operating mode and continuously generate low-sensitivity sensor data, which the processor 415 can acquire.

[0098] The processor 415 may analyze additional low-sensitivity and high-sensitivity sensor data (446). When analyzing additional low-sensitivity sensor data, the processor 415 may execute an initial algorithm configured to process low-sensitivity sensor data from sensor 425(or more) operating in a first operating mode. Furthermore, when analyzing high-sensitivity sensor data, the processor 415 may execute a subsequent algorithm configured to process high-sensitivity sensor data from sensor 425(or more) operating in a subsequent operating mode. The processor 415 may analyze the high-sensitivity sensor data to confirm changes in motion detected by the processor 415 in (434) (447). According to the embodiment, the processor 415 may confirm changes in motion by detecting individual or subsequent changes in motion that may be a continuation of or related to the motion detected in (434), indicated in the high-sensitivity sensor data.

[0099] In (448), the processor 415 may determine whether the timeout window started in (435) has expired. For example, if the timeout window is 10 seconds, the timeout window expires after 10 seconds have elapsed. If the timeout window has not expired ("NO"), the process returns to (442), where additional low-sensitivity and high-sensitivity sensor data may be acquired and analyzed. According to the embodiment, the sensor 425(or more) may continue to generate high-sensitivity and low-sensitivity sensor data, and the processor 415 may continue to analyze it.

[0100] Once the timeout window has expired ("YES"), the processor 415 may determine whether further motion has been detected (450). In one embodiment, when determining whether further motion has been detected, the processor 415 may analyze an additional set of low-sensitivity sensor data. That is, the processor 415 may determine whether further motion has been detected from additional low-sensitivity sensor data acquired in (444) after the timeout window has started. If the processor 415 detects further motion ("YES"), the process may terminate, be repeated, or proceed to other functionalities. In one embodiment, the processor 415 may continue to acquire and analyze high-sensitivity sensor data generated by the sensor 425(or more) operating in a subsequent operating mode, thereby enhancing functionality. In addition, the processor 415 may continue to determine whether further motion has been detected and proceed to (452) if none has been detected.

[0101] The processor 415 may consider the confirmation of a change in motion from (447) to be a false positive if no further motion is detected ("NO") (452). In other words, the processor 415 may consider the motion confirmed from the high-sensitivity sensor data to be a false positive because no further motion is detected from additional low-sensitivity sensor data. The processor 415 may further terminate the subsequent algorithm that processes the high-sensitivity sensor data (454). In addition, the processor 415 may request the sensor 225(or more) to stop generating high-sensitivity sensor data (456). The process may then terminate, be repeated, or proceed to other functionalities.

[0102] Figures 5A to 5F illustrate illustrative representations of interactions between electronic devices and users, as well as illustrative representations of the operation of various sensors based on these interactions. In each of Figures 5A to 5F, the user 503 is shown at a constant relative distance from the device 502, and the distance scale 504 generally indicates the distance of the user 503 from the device 502 (e.g., short, medium, long, out of range, or somewhere in between). In addition, each of Figures 5A to 5F represents the frequency(s) of the radio waves emitted by the radar sensor and the amplitude of the ultrasound emitted by the ultrasonic sensor. The representations in Figures 5A to 5F can be interpreted as the user 503 moving sequentially from outside the range of the device 502 to within the short-range range of the device 502. It should be understood that the representations shown in Figures 5A to 5F are merely examples, and additional or alternative representations may be conceived.

[0103] Figure 5A shows a representation 500 of user 503 who is out of range of device 502. In this configuration, the long-range radio waves emitted by the radar sensor (501) generally have a low SNR (i.e., are not very accurate) and are capable of detecting objects far from device 502. In addition, the ultrasonic sensor may be turned off (505).

[0104] Figure 5B shows a representation 506 of user 503 at a long distance from device 502. In this configuration, since user 503 is still at a long distance from device 502, the radar sensor continues to emit long-range radio waves (507). In addition, the ultrasonic sensor may remain off (508).

[0105] Figure 5C shows a representation 510 of a user 503 approaching a medium distance from device 502. In this configuration, the radar sensor emits both long-range and medium-range radio waves (511). According to the embodiment, the radar sensor may emit long-range and medium-range radio waves within the range of the transmission window in an alternating or sequential manner. By the radar sensor emitting medium-range radio waves before the user 503 comes within a medium distance from device 502, device 502 may begin to analyze the resulting sensing data in relation to an algorithm for processing medium-range radio waves (e.g., gesture mode). In addition, device 502 may activate an ultrasonic sensor that can emit large-amplitude ultrasonic waves (512).

[0106] Figure 5D shows a representation 515 of a user 503 at a medium distance from device 502. In this configuration, the radar sensor emits long-range, medium-range, and short-range radio waves (516). According to the embodiment, the radar sensor may emit long-range, medium-range, and short-range radio waves within the range of the transmission window in an alternating or sequential manner. By the radar sensor emitting both short-range and medium-range radio waves, device 502 may initiate an analysis of the resulting sensing data related to an algorithm(s) that processes the medium-range and / or short-range radio waves (e.g., a "gross" gesture mode and / or a "micro" gesture mode). In addition, the ultrasonic sensor may emit large-amplitude ultrasonic waves (517).

[0107] Figure 5E shows a representation 520 of user 503 approaching device 502 at a short distance. In this configuration, the radar sensor emits long-range, medium-range, and short-range radio waves (521). The emission (521) in Figure 5E may include more short-range radio waves (and / or fewer long-range and / or medium-range radio waves) compared to the emission (516) in Figure 5D. According to the embodiment, the radar sensor may emit long-range, medium-range, and short-range radio waves within the range of the transmission window in an alternating or sequential manner, etc. By the radar sensor emitting both short-range and medium-range radio waves, device 502 may initiate an analysis of the resulting sensing data related to an algorithm(s) that processes the medium-range and / or short-range radio waves (e.g., a "gross" gesture mode and / or a "micro" gesture mode). In addition, the ultrasonic sensor may emit medium-amplitude ultrasonic waves (522).

[0108] Figure 5F shows a representation 525 of a user 503 at a short distance from device 502. In this configuration, the radar sensor emits long-range and short-range radio waves (526). The emission (526) in Figure 5F may include more short-range radio waves (and / or fewer or none long-range and / or medium-range radio waves) compared to the emission (516) in Figure 5D and the emission (521) in Figure 5E. According to the embodiment, the radar sensor may emit long-range and short-range radio waves within the transmission window in an alternating or sequential manner, etc. By emitting short-range radio waves, device 502 can initiate analysis of the resulting sensing data related to an algorithm(s) that process short-range radio waves (e.g., a “micro” gesture mode). In addition, if user 503 moves away from device 502, the radar sensor may emit long-range radio waves, allowing device 502 to continue operating in presence mode and consuming sensor data configured to detect additional objects. Furthermore, ultrasonic sensors can emit ultrasonic waves of small amplitude (527).

[0109] Figure 6 is a flowchart of method 600 for an electronic device to manage motion detection functionality. Method 600 begins with the step (block 605) of the electronic device acquiring a set of sensor data from the electronic device's sensors. According to the embodiment, the sensors may be radar sensors, ultrasonic sensors, or other types of sensors. The electronic device may analyze the set of sensor data to determine whether motion has been detected (i.e., whether the set of sensor data indicates a change in target motion relative to the electronic device) (block 610). When analyzing the set of sensor data, the electronic device may execute an initial motion recognition algorithm that generally consumes fewer resources.

[0110] If the electronic device does not detect motion ("NO"), the process can return to block 605 and terminate, or proceed to other functionalities. If the electronic device detects motion ("YES"), it may cache a set of sensor data in memory (block 615). Generally, when a sensor generates a set of sensor data, the electronic device may acquire and cache the set of sensor data in a rolling manner. In addition, the electronic device may remove at least a portion of the cached set of sensor data using techniques such as background subtraction or clutter removal (block 620). The sensor data to be removed may be sensor data that does not indicate motion.

[0111] The electronic device may initiate an auxiliary motion recognition algorithm (block 625). According to the embodiment, the auxiliary motion recognition algorithm may generally consume more resources than the initial motion recognition algorithm. The electronic device may analyze a set of sensor data, which has been cached and then had at least the non-motion-indicating portion removed, using the auxiliary motion recognition algorithm (block 630).

[0112] The electronic device analyzes the motion detected in block 610 based on the analysis of block 630. The device can determine whether a change has been detected (i.e., whether the change in motion detected in block 610 is not false positive) (block 635). If the electronic device determines that no change in motion was detected ("NO"), the process can return to block 605 and terminate, or proceed to other functionalities. In addition, the electronic device may terminate the auxiliary motion recognition algorithm.

[0113] If the electronic device determines that a change in motion has been detected ("YES"), it may acquire an additional set of sensor data from the sensor (block 640). In another implementation, the electronic device may acquire an additional set of sensor data from an additional (i.e., another) sensor. The electronic device may analyze the additional set of sensor data using an auxiliary motion recognition algorithm (block 645), thereby facilitating various functionalities of the electronic device.

[0114] Based on the analysis of block 645, the electronic device may determine whether any further changes in motion have been detected (block 650). According to the embodiment, these further changes in motion may indicate an executed gesture or another user action performed in the vicinity of the electronic device. If the electronic device detects any further changes in motion ("YES"), the process may return to block 640 or proceed to other functionalities. If the electronic device does not detect any further changes in motion ("NO"), the process may terminate, be repeated, or proceed to other functionalities. In addition, if the electronic device does not detect any further changes in motion, it may terminate the auxiliary motion recognition algorithm.

[0115] Figure 7 is a flowchart of another method 700 for an electronic device to manage motion detection functionality. Method 700 begins with the step (block 705) of the electronic device obtaining a first set of sensor data in a first mode from a sensor of the electronic device operating in a first sensitivity mode. According to the embodiment, the sensor may be a radar sensor, an ultrasonic sensor, or another type of sensor. The electronic device may analyze the first set of sensor data in the first mode to determine whether motion has been detected (i.e., whether the first set of sensor data in the first mode indicates a change in the target's motion relative to the electronic device) (block 710). When analyzing the first set of sensor data in the first mode, the electronic device may execute an initial motion recognition algorithm that generally consumes fewer resources.

[0116] If the electronic device does not detect motion ("NO"), the process may return to block 705 and terminate, or proceed to other functionalities. If the electronic device detects motion ("YES"), it may obtain a set of sensor data in the second mode from the sensor operating in the second sensitivity mode (block 715). In addition, the electronic device may cache the set of sensor data in the second mode in memory (block 720). Furthermore, the electronic device may remove at least a portion of the cached set of sensor data in the second mode using techniques such as background subtraction or clutter removal (block 725). The sensor data to be removed may be sensor data that does not indicate motion.

[0117] The electronic device may obtain a second set of sensor data in the first mode from a sensor operating in the first sensitivity mode (block 730). Furthermore, the electronic device may analyze the second set of sensor data in the first mode using an initial motion recognition algorithm. Thus, the sensor may continue operating in the first sensitivity mode, and the initial motion recognition algorithm may continue analyzing the resulting sensor data.

[0118] The electronic device may also initiate an auxiliary motion recognition algorithm (block 735). According to the embodiment, the auxiliary motion recognition algorithm may generally consume more resources than the initial motion recognition algorithm. The electronic device caches and then initiates at least, A second set of sensor data in a second mode, from which non-moving portions have been removed, can be analyzed by an auxiliary motion recognition algorithm (block 740). Furthermore, the electronic device obtains an additional set of sensor data in a second mode from the sensor operating in the second sensitivity mode (block 745) and analyzes this additional set of sensor data in a second mode using the auxiliary motion recognition algorithm.

[0119] Based on this analysis, the electronic device may determine whether further motion has been detected (i.e., whether an additional set of sensor data in a second mode indicates motion) (block 750). If the electronic device determines that further motion has been detected ("YES"), the process may return to block 745 and terminate, or proceed to other functionalities. If the electronic device determines that no further motion has been detected ("NO"), the auxiliary motion recognition algorithm may terminate (block 755).

[0120] Figure 8 is a flowchart of method 800 for an electronic device to manage sensor activity. Method 800 begins with the step (block 805) in which the electronic device obtains a set of low-sensitivity sensor data from a sensor of the electronic device operating in low-sensitivity mode. According to the embodiment, the sensor may be a radar sensor, an ultrasonic sensor, or another type of sensor. The electronic device may analyze the set of low-sensitivity sensor data to determine whether motion has been detected (i.e., whether the set of low-sensitivity sensor data indicates a change in the target's motion relative to the electronic device) (block 810). When analyzing the set of low-sensitivity sensor data, the electronic device may execute an initial algorithm that generally consumes fewer resources.

[0121] If the electronic device does not detect motion ("NO"), the process can return to block 805, terminate, or proceed to other functionalities. If the electronic device detects motion ("YES"), it can initiate a variable-length timeout window (e.g., 10 seconds, 1 minute, etc.) (815). Furthermore, the electronic device can obtain a set of high-sensitivity sensor data from the sensor operating in high-sensitivity mode (block 820). According to the embodiment, the electronic device can request the sensor to generate a set of high-sensitivity sensor data to initiate a subsequent algorithm that processes the set of high-sensitivity sensor data, which may generally consume more resources than the initial algorithm. In addition, the sensor may continue operating in low-sensitivity mode, and the electronic device can obtain an additional set of low-sensitivity sensor data from the sensor operating in low-sensitivity mode (block 825).

[0122] The electronic device may analyze a set of high-sensitivity sensor data and an additional set of low-sensitivity sensor data (block 830). More specifically, the electronic device may analyze the set of high-sensitivity sensor data using a subsequent algorithm and analyze the additional set of low-sensitivity sensor data using an initial algorithm. Based on the analysis of the set of high-sensitivity sensor data, the electronic device may confirm the change in motion detected in block 810 (block 835). When confirming the change in motion, the electronic device may detect a subsequent change in motion relative to the electronic device, which may be a continuation of the change in motion detected in block 810 or a different change, and may be associated with the same target or a different target. In some situations, the electronic device may not confirm a change in motion as a result of analyzing the set of high-sensitivity sensor data.

[0123] In block 840, the electronic device may determine whether the timeout window has expired. If the timeout window has not expired ("NO"), the process may return to block 820, terminate, or proceed to other functionalities. If the timeout window has expired ("YES"), the process may proceed to block 845, where the electronic device analyzes an additional set of low-sensitivity sensor data. It may be determined whether any further changes in motion have been detected in the electronic device. If the electronic device detects any further changes in motion ("YES"), the process may terminate, be repeated, or proceed to other functionalities.

[0124] The electronic device may consider the confirmation of the change in motion from block 835 to be a false positive (block 850) if no further changes in motion are detected ("NO"). In addition, the electronic device may terminate the subsequent algorithm for processing the high-sensitivity sensor data (block 855). Furthermore, in the embodiment, the electronic device may request the sensor to stop generating sets of high-sensitivity sensor data.

[0125] Although Method 800 is described in Figure 8 as operating with a single sensor, it should be understood that it can operate with multiple sensors. Specifically, a first sensor may operate in low-sensitivity mode to generate low-sensitivity sensor data, and a second sensor may operate in high-sensitivity mode to generate high-sensitivity sensor data.

[0126] Figure 9 shows an exemplary electronic device 945 in which the functionality discussed herein may be implemented. The electronic device 945 may include a processor 981 or other similar type of controller module or microcontroller, as well as memory 978. The electronic device 945 may further include an AC power component 963 or other type of power source (e.g., one or more batteries) configured to distribute or supply power to the electronic device 945 and its components.

[0127] Memory 978 may store an operating system 979 that can facilitate the functionality discussed herein, as well as a cache 980 configured to store / cache various sensor data and / or other data. Processor 981 interfaces with memory 978 to run the operating system 979, retrieve data from the cache 980, and run a set of applications 971 (memory 978 may also store) such as one or more motion detection applications 972. For example, a motion detection application 972 may include multiple motion detection algorithms configured to analyze various types of sensor data. Memory 978 may include one or more forms of volatile and / or non-volatile fixed storage and / or removable storage, such as read-only memory (ROM), electronically programmable read-only memory (EPROM), random access memory (RAM), erasable electronically programmable read-only memory (EEPROM), and / or other hard disks, flash memory, microSD cards, etc.

[0128] The electronic device 945 may further include a communication module 975 configured to interface with one or more external ports 973 for communicating data over one or more networks 950. For example, the communication module 975 may utilize the external ports 973 to establish a TCP connection to connect the electronic device 945 to other electronic devices via a Wi-Fi direct connection. According to some embodiments, the communication module 975 may include one or more transceiver functions according to IEEE standards, 3GPP® standards, or other standards, configured to send and receive data over one or more external ports 973. More specifically, the communication module 975 may include one or more WWAN transceivers configured to communicate with a wide area network including one or more cell sites or base stations for communicatingly connecting the electronic device 945 to additional devices or components. Furthermore, the communication module 945 may include one or more WLAN transceivers and / or WPAN transceivers configured to connect the electronic device 945 to a local area network and / or a personal area network such as a Bluetooth® network.

[0129] The electronic device 945 may further include a set of sensors 964. More specifically, the set of sensors 964 may include one or more radar sensors 965, one or more ultrasonic sensors 966, one or more proximity sensors 967, one or more image sensors 969, and / or one or more other sensors 969 (e.g., accelerometers, touch sensors, NFC elements, etc.). The electronic device 945 may include an audio module 977 which includes hardware components such as a speaker 985 for outputting sound and a microphone 986 for detecting or receiving sound. The electronic device 945 may further include a user interface 974 for presenting information to a user and / or receiving input from a user. As shown in Figure 9, the user interface 974 includes a display screen 987 and I / O components 988 (e.g., capacitive or resistive touch-sensitive input panels, keys, buttons, lights, LEDs, cursor control devices, haptic devices, etc.). The user interface 974 may also include a speaker 985 and a microphone 986. In embodiments, the display screen 987 is a touchscreen using a single display technology or a combination of display technologies and may include a thin, transparent touch sensor component superimposed on the display portion visible to the user. For example, such displays include capacitive displays, resistive displays, surface acoustic wave (SAW) displays, optical image displays, and the like.

[0130] Generally, a computer program product according to one embodiment includes a computer-usable storage medium (e.g., standard random access memory (RAM), optical disk, universal serial bus (USB) drive, or the like) having computer-readable program code, the computer-readable program code being adapted to be executed by a processor 981 (for example, operating in conjunction with operating system 979) in order to facilitate the functions described herein. In this regard, the program code may be implemented in any desired language, and may be implemented as machine code, assembler code, bytecode, source code of an interpreted language, or the like (e.g., C, C++, Java, Actionscript, Objective-C, Javascript, CSS, XML, and / or others).

[0131] Throughout this specification, multiple instances may implement components, operations, or structures described as a single instance. While individual operations of one or more methods are shown and described as separate operations, one or more of these operations may be performed simultaneously, and they do not need to be performed in the order shown. Structures and functionalities presented as separate components in illustrative configurations may be implemented as combined structures or components. Similarly, structures and functionalities presented as single components may be implemented as individual components. These and other changes, modifications, additions, and improvements are included within the scope of the subject matter of this disclosure.

[0132] In addition, certain embodiments described herein include logical or multiple components, modules, or mechanisms. A module may constitute either a software module (e.g., code stored on a machine-readable medium) or a hardware module. A hardware module is a tangible unit capable of performing a particular operation and may be configured or arranged in a particular manner. In exemplary embodiments, one or more computer systems (e.g., standalone computer systems, client computer systems, or server computer systems) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as hardware modules that operate to perform a particular operation described herein.

[0133] In various embodiments, hardware modules can be implemented mechanically or electronically. For example, a hardware module may have dedicated circuitry or permanently configured logic for performing a particular operation (e.g., as a special-purpose processor such as a field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC)). A hardware module may also include programmable logic or circuitry (e.g., encompassing the range of general-purpose processors or other programmable processors) that is temporarily configured by software to perform a particular operation. It will be understood that the decision of whether to implement a hardware module mechanically with permanently configured dedicated circuitry or with temporarily configured (e.g., software-configured) circuitry is made in consideration of cost and time.

[0134] Therefore, the term hardware should be understood to encompass tangible entities that are permanently configured (e.g., wired), physically configured, or temporarily configured (e.g., programmed) to operate in a particular way or to perform a particular operation as described herein. As used herein, “hardware implementer” refers to a hardware module. Given embodiments in which hardware modules are temporarily configured (e.g., programmed), each hardware module does not need to be configured or instantiated simultaneously in any given instance. For example, if a hardware module includes a general-purpose processor configured using software, the general-purpose processor may be configured as separate hardware modules at different times. Thus, a processor may be configured by software to configure one hardware module in one instance and another hardware module in another instance.

[0135] Hardware modules can exchange information with other hardware. Therefore, each described hardware module can be considered to be communicatively coupled. If multiple such hardware modules exist simultaneously, communication can be achieved by signal transmission connecting the hardware modules (e.g., through appropriate circuits and buses). In embodiments where multiple hardware modules are configured or instantiated at different times, communication between such hardware modules can be achieved, for example, by storing and retrieving information in a memory structure accessed by the multiple hardware modules. For example, one hardware module may perform an operation and cause a communicatively coupled memory device to store the output of that operation. Other hardware modules can then later access the memory device to retrieve and process the stored output. Hardware modules can also initiate communication with input or output devices to operate on resources (e.g., collect information).

[0136] Methods 600, 700, and 800 may include one or more function blocks, modules, individual functions, or routines in the form of clear computer execution instructions, stored in a non-temporary computer-readable storage medium and executed using the processor of a computer device (e.g., a server device, personal computer, smartphone, tablet computer, wristwatch, mobile computer device, or other client computer device as described herein). Methods 600, 700, and 800 may be included, for example, as part of any backend server, client computer device module in the exemplary environment, or as part of a module outside such an environment. While the diagrams may be illustrated with reference to other diagrams for ease of explanation, Methods 600, 700, and 800 may be used with other objects and user interfaces. Furthermore, the above explanation Although the steps in methods 600, 700, and 800 are described by specific devices, this is for illustrative purposes only. Blocks in methods 600, 700, and 800 may be executed by one or more devices or other parts of the environment.

[0137] Various operations of the exemplary methods described herein may be performed at least partially by one or more processors that are temporarily or permanently configured (e.g., by software) to perform the relevant operations. Such processors may constitute processor implement modules that operate to perform one or more operations or functions, whether in a temporary or permanent configuration. The modules referenced herein may include processor implement modules in some exemplary embodiments.

[0138] Similarly, any methods or routines described herein may be implemented by a processor, at least in part. For example, at least some of the operations of a method may be performed by one or more processors or hardware modules that implement processors. The performance of a particular operation may be distributed across one or more processors deployed across multiple machines, rather than being located within a single machine. In some exemplary embodiments, the processors may be located in a single location (e.g., within a home environment, within an office environment, or as a server farm), while in other embodiments they may be distributed across multiple locations.

[0139] One or more processors may also operate to support the performance of related operations in a “cloud computing” environment, or as SaaS (Software as a Service). For example, at least some of the operations indicated above may be performed by a group of computers (as an example of machines containing processors), and these operations may be accessible over a network (e.g., the Internet) and through one or more appropriate interfaces (e.g., APIs (Application Programming Interfaces)).

[0140] Furthermore, the figures illustrate several embodiments of the exemplary environment for illustrative purposes only. Those skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods shown herein can be adopted without departing from the principles described herein.

[0141] Those skilled in the art will, upon reading this disclosure, recognize, through the principles disclosed herein, the design of further additional alternative structures and functions for controlling the operation of sensors. Therefore, while specific embodiments and applications have been shown and described, it should be understood that the disclosed embodiments are not limited to the explicit structures and components disclosed herein. Various modifications, alterations, and variations of the mechanisms, operations, and details of the methods and apparatus disclosed herein, which should be obvious to those skilled in the art, can be made without departing from the spirit and scope defined in the appended claims.

Claims

1. A method that is performed on a computer, The method includes the step of a processor obtaining a first set of low-sensitivity sensor data from a radar sensor operating in low-sensitivity mode, wherein the radar sensor is configured to generate the first set of low-sensitivity sensor data by emitting a first radio wave including a first frequency range, and the method further includes the step of The method includes the step of analyzing a first set of low-sensitivity sensor data using a first motion detection algorithm to detect a first change in the motion of a target relative to an electronic device, wherein the step of analyzing the first set of low-sensitivity sensor data includes the step of identifying a timing shift between at least one of the first radio waves and the radio waves of the first set of low-sensitivity sensor data, and the method further includes In response to detecting the first change in the movement of the target relative to the electronic device, the processor includes the step of obtaining a second set of low-sensitivity sensor data from the radar sensor operating in low-sensitivity mode, wherein the radar sensor is configured to generate the second set of low-sensitivity sensor data by emitting a second radio wave including the first frequency range, and the method further includes, The processor includes the step of acquiring a set of high-sensitivity sensor data from the radar sensor operating in high-sensitivity mode, wherein the radar sensor is configured to generate the set of high-sensitivity sensor data by emitting a third radio wave including a second frequency range different from the first frequency range, and the method further The method includes the step of analyzing the set of highly sensitive sensor data using a second motion detection algorithm to confirm the first change in the motion of the target relative to the electronic device, wherein the step of analyzing the set of highly sensitive sensor data includes the step of identifying a timing shift between at least one of the third radio waves and the radio waves of the set of highly sensitive sensor data, and the method further includes A method performed by a computer, comprising the steps of: analyzing a second set of low-sensitivity sensor data using the first motion detection algorithm to detect a second change in the motion of the target relative to the electronic device, wherein the step of analyzing the second set of low-sensitivity sensor data includes the step of identifying a timing shift between at least one of the second radio waves and the radio waves of the second set of low-sensitivity sensor data.

2. The computer-based method according to claim 1, wherein the step of confirming the first change in the movement of the target relative to the electronic device includes the step of detecting a subsequent change in the movement of the target relative to the electronic device, the subsequent change in the movement being a continuation of the first change in the movement.

3. The computer-based method according to claim 1 or 2, wherein the resource consumption of the electronic device resulting from performing the second motion detection is greater than the resource consumption of the electronic device resulting from performing the first motion detection algorithm.

4. The computer-based method according to any one of claims 1 to 3, wherein the first change in the movement of the target corresponds to the target entering an environment in which the electronic device exists, and the second change in the movement of the target corresponds to a gesture performed by the target within the environment.

5. A computer-based method according to any one of claims 1 to 4, further comprising the step of terminating the second motion detection algorithm in response to detecting the second change in the movement of the target relative to the electronic device.

6. The method performed by a computer according to claim 1 or 2, wherein the resource consumption of the electronic device resulting from operating the radar sensor in the high-sensitivity mode is greater than the resource consumption of the electronic device resulting from operating the radar sensor in the low-sensitivity mode.

7. The computer-based method according to any one of claims 1 to 6, wherein the set of high-sensitivity sensor data is obtained in response to the processor requesting the radar sensor to generate the set of high-sensitivity sensor data.

8. A computer-based method according to any one of claims 1 to 7, further comprising the step of analyzing a second set of low-sensitivity sensor data using the first motion detection algorithm to determine that the first confirmation of a change in the motion of the target relative to the electronic device is false positive.

9. It is an electronic device, Radar sensors and, The radar sensor comprises a processor interfaced with the radar sensor, and the processor is The radar sensor is configured to acquire a first set of low-sensitivity sensor data from the radar sensor operating in low-sensitivity mode, the radar sensor is configured to generate the first set of low-sensitivity sensor data by emitting a first radio wave including a first frequency range, and the processor further, The processor is configured to use a first motion detection algorithm to analyze a first set of low-sensitivity sensor data to detect a first change in the motion of a target relative to the electronic device, wherein the analysis of the first set of low-sensitivity sensor data includes identifying a timing shift between at least one of the first radio waves and the radio waves of the first set of low-sensitivity sensor data, and the processor further, The processor is configured to acquire a second set of low-sensitivity sensor data from the radar sensor operating in low-sensitivity mode in response to detecting the first change in the movement of the target relative to the electronic device, the radar sensor is configured to generate the second set of low-sensitivity sensor data by emitting a second radio wave including the first frequency range, and the processor further, The radar sensor is configured to acquire a set of high-sensitivity sensor data from the radar sensor operating in high-sensitivity mode, and the radar sensor is configured to generate the set of high-sensitivity sensor data by emitting a third radio wave including a second frequency range different from the first frequency range, and the processor further, The processor is configured to analyze the set of highly sensitive sensor data using a second motion detection algorithm to confirm the first change in the motion of the target relative to the electronic device, and the analysis of the set of highly sensitive sensor data includes identifying a timing shift between at least one of the third radio waves and the radio waves of the set of highly sensitive sensor data, and the processor further, An electronic device configured to use the first motion detection algorithm to analyze a second set of low-sensitivity sensor data to detect a second change in the motion of the target relative to the electronic device, wherein the analysis of the second set of low-sensitivity sensor data includes identifying a timing shift between at least one of the second radio waves and the radio waves of the second set of low-sensitivity sensor data.

10. The electronic device according to claim 9, wherein the resource consumption of the electronic device resulting from performing the second motion detection is greater than the resource consumption of the electronic device resulting from performing the first motion detection algorithm.

11. The electronic device according to claim 9 or 10, wherein the first change in the movement of the target corresponds to the target entering the environment in which the electronic device exists, and the second change in the movement of the target corresponds to a gesture performed by the target within the environment.

12. The electronic device according to any one of claims 9 to 11, wherein the processor is further configured to terminate the second motion detection algorithm in response to detecting a second change in the movement of the target relative to the electronic device.

13. The electronic device according to claim 9, wherein the resource consumption of the electronic device resulting from operating the radar sensor in the high-sensitivity mode is greater than the resource consumption of the electronic device resulting from operating the radar sensor in the low-sensitivity mode.

14. The electronic device according to any one of claims 9 to 13, wherein the set of high-sensitivity sensor data is obtained in response to a request to the radar sensor to generate the set of high-sensitivity sensor data.

15. The electronic device according to any one of claims 9 to 14, wherein the processor is further configured to analyze a second set of the low-sensitivity sensor data using the first motion detection algorithm to determine that the first confirmation of a change in the motion of the target relative to the electronic device is false positive.

16. A computer program for causing one or more processors to perform the method described in any one of claims 1 to 8.

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