Patent Publication Number: US-2022221571-A1

Title: Systems and methods for managing motion detection of an electronic device, and associated electronic devices

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure relates to managing sensor operation on an electronic device and, more particularly, to managing the operation of various sensors and algorithms that process the corresponding sensor data. 
     BACKGROUND 
     The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. 
     Electronic devices, such as smartphones and other devices, continue to improve technologically. Generally, electronic devices incorporate more and/or improved sensors to facilitate various functionalities, modes, and applications associated with the electronic devices. With the additional and/or improved sensors along with the increased device capabilities, device resource management becomes more difficult. In particular, additional sensor usage consumes more power and utilizes more central processing unit (CPU) bandwidth, among other increased resource usage. 
     To accommodate this increased power and CPU demand, electronic devices are typically designed with sufficient CPU and memory capabilities. However, this can increase hardware costs associated with manufacturing the electronic devices. Additionally, sensors that actively operate in their highest signal-to-noise ratio (SNR) mode increase the noise in the environment for other devices. Moreover, because devices are often designed with sufficient heat dissipation to compensate for worst case operating conditions, the resulting devices are often larger than necessary, which can constrain the physical design and increase user expectations with potential adverse market implications. 
     SUMMARY 
     According to implementations, an electronic device may manage multiple algorithms that process data from one or more sensors, for example a radar sensor and/or an ultrasound sensor. The sensor(s) may continually generate a set of sensor data, which an initial motion recognition algorithm may analyze and, based on the analysis, detect a change in motion of a target in proximity to the electronic device. When the change in motion is detected, the electronic device may cache the set of sensor data in memory and initiate a supplemental motion recognition algorithm that processes data from the sensor. The electronic device may also facilitate “clutter removal” in which a portion of the set of sensor data that does not indicate motion may be removed from the set of sensor data cached in memory. 
     The supplemental motion recognition algorithm may analyze the set of sensor data that was cached in memory and, based on the analysis, confirm the change in motion that was initially detected by the initial motion recognition algorithm. If the supplemental motion recognition algorithm does not confirm the change in motion, then the change in motion initially detected by the initial motion recognition algorithm may be deemed a false positive. 
     In situations in which a false positive is detected, the electronic device may cease the supplemental motion recognition algorithm. Therefore, the electronic device may revert to executing only the initial motion recognition algorithm, which conserves compute, memory, power, and/or thermal resources. 
     In situations in which the change in motion is confirmed, the supplemental motion recognition algorithm may process additional sensor data generated by the sensor and facilitate various functionalities. For example, the supplemental motion recognition algorithm may be a gesture recognition algorithm that may detect user gestures performed in proximity to the electronic device. 
     In other implementations, a sensor may operate in a first mode and generate a corresponding first set of first mode sensor data. An initial algorithm may analyze the first set of first mode sensor data and detect a change in motion of a target in proximity to the electronic device. In response, the sensor may additionally operate in a second mode and generate a corresponding set of second mode sensor data, which may be cached in memory. 
     The electronic device may initiate a supplemental algorithm that retrieves and analyzes the cached sensor data. Based on any motion detected in the cached sensor data, the electronic device may continue or terminate operation of the supplemental algorithm. 
     In additional implementations, a sensor may operate in a lower-sensitivity mode and generate a corresponding set of lower-sensitivity sensor data. An initial algorithm may analyze the set of lower-sensitivity sensor data and detect a change in motion of a target in proximity to the electronic device. 
     The electronic device may start a timeout window and, during the timeout window, the same sensor or a different sensor may operate in a higher-sensitivity mode and generate a corresponding set of higher-sensitivity sensor data. The electronic device may further initiate a supplemental algorithm that processes the set of higher-sensitivity sensor data and confirms the originally-detected change in motion. 
     Additionally, the initial algorithm may analyze an additional set of lower-sensitivity generated during the timeout window and determine that additional motion was not detected during the timeout window. As a result, the electronic device may determine that the change in motion initially confirmed by the set of higher-sensitivity sensor data was actually a false positive. 
     One example embodiment of the techniques of this disclosure is a computer-implemented method of managing motion detection features on an electronic device. The computer-implemented method includes retrieving, by a processor from a sensor of the electronic device, a set of sensor data, detecting a change in motion of a target relative to the electronic device based on analyzing, by the processor, the set of sensor data, and caching the set of sensor data in a memory of the electronic device. The method further includes, based on detecting the change in motion, initiating, by the processor, a supplemental motion recognition algorithm, analyzing, by the supplemental motion recognition algorithm initiated by the processor, the set of sensor data that was cached in the memory, and based on analyzing the set of sensor data that was cached in the memory, confirming, by the supplemental motion recognition algorithm, the change in motion. 
     Another example embodiment of the techniques of this disclosure is an electronic device. The electronic device comprises a sensor, a memory, and a processor interfaced with the sensor and the memory. The processor is configured to retrieve, from the sensor, a set of sensor data, detect a change in motion of a target relative to the electronic device based on analyzing the set of sensor data, and cause the memory to cache the set of sensor data. The processor is further configured to, based on detecting the change in motion, initiate a supplemental motion recognition algorithm, retrieve, from the memory, the set of sensor data that was cached, analyze, by the supplemental motion recognition algorithm, the set of sensor data that was cached in the memory, and based on analyzing the set of sensor data that was cached in the memory, confirm, by the supplemental motion recognition algorithm, the change in motion. 
     A further example embodiment of the techniques of this disclosure is a computer-implemented method of managing motion detection features on an electronic device. The method includes retrieving, by a processor from a sensor of the electronic device operating in a first sensitivity mode, a first set of first mode sensor data, and detecting a change in motion of a target relative to the electronic device based on analyzing, by an initial motion recognition algorithm, the first set of first mode sensor data. The method further includes, based on detecting the change in motion: retrieving, by the processor from the sensor operating in a second sensitivity mode, a set of second mode sensor data, caching the set of second mode sensor data in a memory of the electronic device, retrieving, by the processor from the sensor operating in the first sensitivity mode, a second set of first mode sensor data, and initiating, by the processor, a supplemental motion recognition algorithm. Additionally, the method includes analyzing, by the supplemental motion recognition algorithm initiated by the processor, the set of second mode sensor data that was cached in the memory. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts that include the claimed embodiments, and explain various principles and advantages of those embodiments. 
         FIG. 1  illustrates an example electronic device that may facilitate the described features, in accordance with some embodiments. 
         FIG. 2  is an example diagram associated with a technique for managing sensor operation using a memory cache, in accordance with some embodiments. 
         FIG. 3  is an example diagram associated with another technique for managing sensor operation using a memory cache, in accordance with some embodiments. 
         FIG. 4  is an example diagram associated with managing the operation of one or more sensors, in accordance with some embodiments. 
         FIGS. 5A-5F  illustrate example representations of interactions between an electronic device and a user, as well as various sensor operations based on the interactions, in accordance with some embodiments. 
         FIG. 6  is a flowchart of a method for an electronic device to manage motion detection features, in accordance with some embodiments. 
         FIG. 7  is a flowchart of another method for an electronic device to manage motion detection features, in accordance with some embodiments. 
         FIG. 8  is a flowchart of a method for an electronic device to manage sensor activity, in accordance with some embodiments. 
         FIG. 9  is a block diagram of an electronic device, in accordance with some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Systems and method for managing motion detection features in an electronic device are described. According to certain aspects, an electronic device may be configured with one or more sensors, such as a radar sensor(s), an ultrasound sensor(s), and/or others. The sensor(s) may operate in different modes according to various contexts and instructions. Similarly, the electronic device may execute different algorithms and applications that process the sensor data from the sensor(s) operating in the different modes. Generally, the different sensor modes and the different algorithms consume different amounts of device resources. Accordingly, the described aspects are configured to manage the operation of the sensor(s) and device algorithms to reduce or otherwise increase the efficiency of the resource consumption. 
     Generally, the electronic device may execute an initial algorithm to detect motion or other events in proximity to 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 additionally, the electronic device may activate an additional sensor and process resulting data generated by the additional sensor. The electronic device may utilize a memory cache to more efficiently and effectively determine whether to continue execution of the subsequent algorithm. If the electronic device determines that the subsequent algorithm is not needed, the electronic device may terminate the subsequent algorithm. 
     By managing the operation of sensors, sensor modes, and algorithms that process sensor data, the electronic device may more efficiently and effectively manage its resource consumption. Accordingly, more affordable hardware components may be used and fewer hardware components may be needed in the electronic device, thus lowering the costs of manufacturing such electronic devices. Additionally, the electronic device may experience longer battery life and longer device longevity. Moreover, sensor noise in the environment of such electronic devices may be decreased. It should be appreciated that additional benefits are envisioned. 
       FIG. 1  illustrates an example electronic device  100  in which the described embodiments may be incorporated, and/or on which the described embodiments may be facilitated or implemented. The electronic device  100  may be any type of electronic device such as a mobile device (e.g., a smartphone), display assistant device, desktop computer, notebook computer, tablet, phablet, GPS (Global Positioning System) or GPS-enabled device, watch, glasses, bracelet, other wearable electronic device, PDA (personal digital assistant), pager, and/or the like. 
     The electronic device  100  may include a user interface  101  that may be embodied as a touchscreen or other type of display that may display or present visual content. In embodiments in which the user interface  101  is a touchscreen, the user interface  101  may incorporate a set of touch-sensitive input panels that may enable a user to make selections or otherwise interface with the electronic device  100 . The electronic device  100  may further include a housing  102  in which various components (including the user interface  101 ) may be incorporated or contained. Additionally, the electronic device  100  may include an alternating current (AC) power component  116  configured to power the electronic device  100  and components thereof, where the AC power component  116  may supply the power or otherwise receive the power from a power supply. It should be appreciated that additional or alternative power supplies are envisioned, for example one or more batteries or other power supplies. 
     According to embodiments, the electronic device  100  may include a variety of sensors, user interface components, and/or the like. In particular, as depicted in  FIG. 1 , the electronic device  100  may include a pair of infrared image sensors  103 ,  109  as well as an image sensor  104 . Additionally, the electronic device  100  may include a speaker  106  that may be configured to output audio. Further, the electronic device  100  may include a dot projector  108  and a flood illuminator  110 . According to embodiments, the pair of infrared image sensors  103 ,  109 , the dot projector  108 , and the flood illuminator  110  may be used singularly or in combination by the electronic device  100  to unlock various features of the electronic device  100  (e.g., unlock the user interface  101 ), among other functionalities. Moreover, the electronic device  100  may include an ambient light sensor (ALS)  105  that may support various proximity detection features. It should be appreciated that alternative and additional sensors are envisioned. For example, one of the sensors may be an ultrasound sensor configured to emit ultrasonic waves. Additionally, it should be appreciated that the sensors and components may be disposed in different arrangements and combinations. 
     The electronic device  100  may additionally include a radar chip  107 . Generally, the radar chip  107  may detect and measure properties of remote and/or proximate objects based on their interactions with radio waves. The radar chip  107  may include a set of transmitter components that emits radio waves, which are then scattered, or redirected, by objects within their paths, with some portion of energy reflected back and intercepted by a set of receiver components. The radar chip  107  may be bidirectional and thus configured to emit signals in two or more directions (e.g., in front of and in back of the electronic device  100 ), or directional and thus configured to emit signals in one direction (e.g., in front of the electronic device  100 ). A processor  115  or controller may examine or analyze the received waveforms to detect the presence of objects as well as estimate certain properties of these objects, such as distance and size, among other properties. 
     Conventional radar designs rely on fine spatial resolution relative to target size in order to resolve different objects and distinguish their spatial structures. Such spatial resolution typically requires broad transmission bandwidth, narrow antenna beamwidth, and large antenna arrays. According to embodiments, the radar chip  107  may employ a sensing paradigm that may be based on motion, rather than spatial structure, which may enable the radar chip  107  to be integrated in the top of the electronic device  100  (or otherwise integrated in another location or portion of the electronic device  100 ). The electronic device  100  may thus support algorithms, applications, and the like (generally, “algorithms”) that may not require forming a well-defined image of a spatial structure of a target, which is in contrast to an optical imaging sensor, for example. Therefore, the algorithms may not generate or use distinguishable images of a target for certain purposes, such as presence detection and/or gesture detection. 
     The processor  115  may implement various signal processing features and functionalities to process temporal changes in received radar signals, such as to detect and resolve subtle motions. In operation, the radar chip  107  may transmit a frequency-modulated signal in a certain frequency or frequency range (e.g., 50-70 GHz, or other frequencies or frequency ranges), and receive a superposition of reflections off of a nearby object(s) or person(s) (generally, a “target”). The processor  115  may detect a sub-millimeter-scale displacement in a position of a target from one transmission to the next, which may induce a distinguishable timing shift in the received signal(s). Over a window of multiple transmissions, these shifts may manifest as a Doppler frequency that is proportional to a velocity of the target. By resolving different Doppler frequencies, the signal processing features may distinguish different targets moving with different motion patterns. According to embodiments, the signal processing features may include a combination of custom filters and coherent integration steps that may boost the underlying signal-to-noise ratio (SNR), attenuate unwanted interference, and differentiate reflections off a target from noise and clutter. These signal processing features enable the radar chip  107  to operate at low-power within the constraints of the electronic device  100 . 
     The electronic device  100  may support various modes or algorithms that process data from the radar chip  107  and/or from other of the sensors as described herein. Generally, one mode may process and analyze signals having a first frequency (or range of frequencies) and another of the modes may process and analyze signals having a second frequency (or range of frequencies) different from the first frequency. For example, one of the modes may be “presence mode” that may be configured to detect the presence of a target (e.g., a person) in proximity to the electronic device  100 . In this example, the presence of a target may be detected when the target enters a room in which the electronic device  100  is located. Another of the modes may be “gesture mode” that may be configured to detect and recognize gestures performed by a person in proximity to the electronic device  100  (e.g., in front of the user interface  101 ). In this example, the gestures may be “macro” gestures (e.g., a movement of a hand to switch active applications) or “micro” gestures (e.g., simulating the turning of a dial using an index finger and thumb). Generally, the processor  115  may execute the various algorithms using various machine learning techniques. 
     The modes may operate differently and may thus have varying degrees of accuracy as well as varying degrees of resource consumption, including compute, memory, electricity, and/or thermal. For example, the gesture mode may process shorter-range waves emitted from the radar chip  107  and the presence mode may process longer-range waves emitted from the radar chip  107 . Additionally, the shorter-range waves may have a higher SNR and may be more accurate, and the longer-range waves may have a lower SNR and may be less accurate. However, the gesture mode that processes the shorter-range waves may have a higher resource consumption, and the presence mode that processes the longer-range waves may have a lower resource consumption. 
     The radar chip  107  may be capable of operating in multiple modes simultaneously (i.e., emitting both shorter-range and longer-range waves over a period of time), and similarly the processor  115  of the electronic device  100  may simultaneously execute algorithms that process the received waveforms resulting from the emitted shorter-range and longer-range waves. According to embodiments, longer-range radar sensing may be accomplished by a Frequency Modulated Continuous Wave (FMCW) radar using a slower frequency sweep from a first frequency to a second frequency. In contrast, a shorter-range radar mode may use a faster frequency sweep from a first frequency to a second frequency. To conserve resource usage, it may be advantageous to limit the amount and types of algorithms that the processor  115  executes. For example, in cases in which a person is not actually performing gestures in proximity to the electronic device  100 , it is advantageous for the processor  115  to deactivate or otherwise not execute the gesture mode, such as to conserve various resources. 
     According to embodiments, the different sensor modes and device algorithms may operate based on different contexts. In particular, certain sensor modes and/or algorithms may be disabled or enabled based on time of day, day of week/month/year, month of year, time since last motion detection, and/or other situations. For example, the gesture mode may be deactivated between the hours of 1:00 AM and 5:00 AM. For further example, a sleep sensing mode may be deactivated between the hours of 9:00 AM and 9:00 PM. As an additional example, a higher-sensitivity sensor mode may be disabled if the time since last motion detection exceeds ten (10) minutes. Thus, resources may be conserved based on these modes and algorithms being selectively disabled. It should be appreciated that these contexts may be static or may adjust according to usage of the electronic device  100 , among other factors or conditions, or may be explicitly activated or deactivated by a user. 
     Further, according to embodiments, certain sensors or detections by the sensors may trigger the activation or initiation of other certain sensor modes and device algorithms. Generally, a lower-sensitivity mode having lower resource usage for a given sensor may detect motion in proximity to the electronic device  100 , and may cause a higher-sensitivity mode having higher resource usage to initiate, where the higher-sensitivity mode may operate on that given sensor or on another sensor. The electronic device  100  may also initiate an algorithm that processes the higher-sensitivity sensor data. Further, sensor data generated as a result of the higher-sensitivity mode may be used to confirm (or not confirm) the initially-detected motion. If additional motion is indicated in the higher-sensitivity data, the electronic device  100  may continue to operate the initiated algorithm and facilitate operations and functionalities accordingly. 
     The electronic device  100  may include a memory  120  with which the processor  115  may interface. The memory  120  may include a main memory portion as well as a cache portion for the temporary storage of certain data that the processor  115  may access. According to embodiments, the processor  115  may store certain sensor data (e.g., sensor data from the radar chip  107 ) in the cache portion, to enable the processor  115  to confirm certain detected movements or motion. Additionally or alternatively, the processor  115  may initiate a certain algorithm that accesses and analyzes data stored in the cache portion to enable for the more accurate detection or recognition of motion or movement by the certain algorithm. Because machine learning algorithms need a certain amount of time prior to accurately detecting targets and motion, the cache portion of the memory  120  enables the algorithms with access to the needed data before the target arrives in a zone and/or performs a certain motion or gesture in that zone. 
       FIG. 2  is a signal diagram  200  associated with various embodiments of the systems and methods. The signal diagram  200  includes a memory  220  (such as the memory  120  as described with respect to  FIG. 1 ), a processor  215  (such as the processor  115  as described with respect to  FIG. 1 ), and one or more sensors  225  (such as one or more of the various sensors as discussed with respect to  FIG. 1 ). In embodiments, the sensor(s)  225  may include a single sensor (e.g., a single radar sensor), or multiple sensors (e.g., a radar sensor and an ultrasound sensor). Further, the memory  220 , the processor  215 , and the sensor(s)  225  may be embodied or incorporated in a single electronic device. Additionally, the memory  220  may be a hardware cache accessible by the processor  215  and configured to cache data for faster access by the processor  215 . It should be appreciated that various of the functionalities as illustrated in the signal diagram  200  may occur at various times or orders relative to the other functionalities. 
     The signal diagram  200  may begin when the sensor(s)  225  generates ( 230 ) sensor data. For example, if the sensor  225  is a radar sensor, the radar sensor may emit radio waves and detect the resulting waveforms. According to embodiments, in generating the sensor data, the sensor(s)  225  may operate in a first operating mode. For example, if the sensor  225  is a radar sensor, the radar sensor may emit radio waves having a longer-range frequency, such as to detect the presence of targets in a proximity of the electronic device. The processor  215  may retrieve ( 232 ), from the sensor(s)  225 , the generated sensor data. It should be appreciated that the sensor(s)  225  may continuously generate, and the processor  215  may continuously retrieve, the sensor data. 
     The processor  215  may analyze the retrieved sensor data and determine ( 234 ) whether there is motion detected. In particular, the processor  215  may analyze the sensor data (e.g., received waveforms) and determine if motion is indicated in the sensor data. In analyzing the sensor data, the processor  215  may execute an initial algorithm, such as a motion detection algorithm, that is configured to process the sensor data resulting from the sensor(s)  225  operating in the first operating mode. If the processor  215  does not detect motion (“NO”), processing may return to ( 230 / 232 ), end, or proceed to other functionality. Otherwise, if the processor  215  detects motion (“YES”), the processor  215  may provide ( 236 ) the sensor data to the memory  220 . After receiving the sensor data, the memory  220  may store (cache) ( 240 ) the sensor data for subsequent access by the processor  215 . It should be appreciated that the memory  220  may cache the sensor data on a rolling basis as the sensor(s)  225  generates additional sensor data and the processor  215  retrieves the additional sensor data. 
     After detecting motion, the processor  215  may also initiate ( 238 ) a supplemental motion recognition algorithm. According to embodiments, the supplemental motion recognition algorithm may be different than the initial algorithm that the processor  215  executes in detecting the motion in ( 234 ). For example, the processor  215  may detect the motion in ( 234 ) while operating in a presence mode and the supplemental motion recognition algorithm may be associated with a gesture mode. Generally, the supplemental motion recognition algorithm may consume more resources (e.g., compute, memory, electrical, and/or thermal) than the initial algorithm. 
     The processor  215  may also retrieve ( 242 ) the sensor data that is cached in the memory  220 . In certain embodiments, the processor  215  may perform ( 244 ) a “clutter removal” or background subtraction technique on the sensor data that is retrieved from the memory  220 . In performing the clutter removal, the processor  215  may analyze the retrieved sensor data and remove, from the retrieved sensor data, a portion of the data that does not indicate motion. Accordingly, the remaining data may include data that indicates (or may indicate) motion. 
     The processor  215 , in executing the supplemental motion recognition algorithm, may analyze ( 246 ) the cached sensor data, with or without the background data (i.e., data without detected motion) removed. In association with the supplemental motion recognition algorithm analyzing the cached sensor data, the processor  215  may determine whether the motion detected in ( 234 ) is confirmed. By analyzing the cached sensor data, the supplemental motion recognition algorithm may determine whether the motion detection in ( 234 ) by the initial algorithm was a false positive. If the supplemental motion recognition algorithm does not confirm the detected motion (“NO”; i.e., the motion detection by the initial algorithm was a false positive), processing may return to ( 230 / 232 ), end, or proceed to other functionality. 
     If the supplemental motion recognition algorithm does confirm the detected motion (“YES”; i.e., the motion detection by the initial algorithm was not a false positive), the processor may continue operation of the supplemental motion recognition algorithm. In particular, the processor  215  may request ( 249 ) the sensor(s)  225  to generate and provide additional sensor data. After receiving the request, the sensor(s)  225  may generate ( 250 ) the additional sensor data. According to embodiments, the sensor(s)  225  may generate the additional sensor data while operating in a mode different from the mode the sensor(s)  225  operated in when generating the sensor data in ( 230 ). For example, the initial sensor data generated in ( 230 ) may result from the sensor(s)  225  operating in a presence mode that generates longer-range waves, and the additional sensor data generated in ( 250 ) may result from the sensor(s)  225  operating in a gesture mode that generates shorter-range waves. The sensor(s)  225  may provide ( 251 ) the additional sensor data to the processor  215 . 
     It should be appreciated that an additional sensor other than the sensor that generated the sensor data in ( 230 ) may generate the additional sensor data. Thus, in this implementation, the processor  215  may request ( 249 ) the additional sensor to generate the additional sensor data, the additional sensor may generate ( 250 ) the additional sensor data, and the additional sensor may provide ( 251 ) the additional sensor data to the processor  215 . 
     After receiving the additional sensor data, the supplemental motion recognition algorithm executed by the processor  215  may analyze ( 252 ) the additional sensor data. In analyzing the additional sensor data, the supplemental motion recognition algorithm may facilitate various functionalities. For example, if the supplemental motion recognition algorithm is associated with a gesture mode, the supplemental motion recognition algorithm may detect a specific gesture (e.g., the clockwise rotation of a dial) and facilitate a certain action based on the specific gesture (e.g., turning up the volume of a music playback application). 
     If the supplemental motion recognition algorithm fails to detect an additional change in motion from the additional sensor data, the processor  215  may terminate the supplemental motion recognition algorithm. Accordingly, the processor  215  may revert to solely executing the initial algorithm, such as to conserve various resources of the electronic device. It should be appreciated that the processor  215  may continually execute the initial algorithm throughout the signal diagram  200 , or may terminate the initial algorithm in response to the supplemental motion recognition algorithm confirming the motion detection in ( 248 ). 
       FIG. 3  is a signal diagram  300  associated with various embodiments of the systems and methods. The signal diagram  300  includes a memory  320  (such as the memory  120  as described with respect to  FIG. 1 ), a processor  315  (such as the processor  115  as described with respect to  FIG. 1 ), and one or more sensors  325  (such as one or more of the various sensors as discussed with respect to  FIG. 1 ). In embodiments, the sensor(s)  325  may include a single sensor (e.g., a single radar sensor), or multiple sensors (e.g., a radar sensor and an ultrasound sensor). Further, the memory  320 , the processor  315 , and the sensor(s)  325  may be embodied or incorporated in a single electronic device. Additionally, the memory  320  may be a hardware cache accessible by the processor  315  and configured to cache data for faster access by the processor  315 . It should be appreciated that various of the functionalities as illustrated in the signal diagram  300  may occur at various times or orders relative to the other functionalities. 
     The signal diagram  300  may begin when the sensor(s)  325  generates ( 330 ) first mode sensor data. For example, if the sensor  325  is a radar sensor, the radar sensor may emit radio waves and detect the resulting waveforms. According to embodiments, in generating the first mode sensor data, the sensor(s)  325  may operate in a first operating mode having a first sensitivity. For example, if the sensor  325  is a radar sensor, the radar sensor may emit radio waves having a longer-range frequency, such as to detect the presence of targets in a proximity of the electronic device. The processor  315  may retrieve ( 332 ), from the sensor(s)  325 , the generated first mode sensor data. It should be appreciated that the sensor(s)  325  may continuously generate, and the processor  315  may continuously retrieve, the first mode sensor data. 
     The processor  315  may analyze the retrieved first mode sensor data and determine ( 334 ) whether there is motion detected. In particular, the processor  315  may analyze the first mode sensor data (e.g., received waveforms) and determine if motion is indicated in the first mode sensor data. In analyzing the first mode sensor data, the processor  315  may execute an initial motion recognition algorithm that is configured to process the first mode sensor data resulting from the sensor(s)  325  operating in the first operating mode. If the processor  315  does not detect motion (“NO”), processing may return to ( 330 / 332 ), end, or proceed to other functionality. 
     Otherwise, if the processor  315  detects motion (“YES”), the processor  315  may request ( 336 ) the sensor(s)  325  to generate and provide second mode sensor data. After receiving the request, the sensor(s)  325  may generate ( 338 ) the second mode sensor data. According to embodiments, the sensor(s)  325  may generate the second mode sensor data while operating in a second mode different from the first mode the sensor(s)  325  operated in when generating the first mode sensor data in ( 330 ). For example, the first mode sensor data generated in ( 330 ) may result from the sensor(s)  325  operating in a presence mode that generates longer-range waves, and the second mode sensor data generated in ( 338 ) may result from the sensor(s)  325  operating in a gesture mode that generates shorter-range waves. The sensor(s)  325  may provide ( 340 ) the second mode sensor data to the processor  315 . 
     In some scenarios, the determination in ( 334 ) may be based on the processor  315  determining whether the first mode sensor data includes motion above a certain velocity, where this velocity may represent the lowest velocity motion that may contain a gesture. If the processor  315  determines that the first mode sensor data includes motion above the certain velocity (“YES”), processing may continue to ( 336 ). If the processor  315  determines that the first mode sensor data does not include motion above the certain velocity (“NO”), processing may return to ( 330 / 332 ), end, or proceed to other functionality. 
     It should be appreciated that an additional sensor other than the sensor that generated the sensor data in ( 330 ) may generate the second mode sensor data. Thus, in this implementation, the processor  315  may request ( 336 ) the additional sensor to generate the second mode sensor data, the additional sensor may generate ( 338 ) the second mode sensor data, and the additional sensor may provide ( 340 ) the second mode sensor data to the processor  315 . 
     Additionally, the processor  315  may provide ( 342 ) the second mode sensor data to the memory  320 . After receiving the second mode sensor data, the memory  320  may store (cache) ( 348 ) the second mode sensor data for subsequent access by the processor  315 . It should be appreciated that the memory  320  may cache the second mode sensor data on a rolling basis as the sensor(s)  325  generates the second mode sensor data and the processor  315  retrieves the second mode sensor data. 
     The processor  315  may retrieve ( 344 ) additional first mode sensor data that is generated by the sensor(s)  325  operating in the first operating mode. Additionally, the processor  315  may initiate ( 346 ) a supplemental motion recognition algorithm. According to embodiments, the supplemental motion recognition algorithm may be different than the initial motion recognition algorithm that the processor  315  executes in detecting the motion in ( 334 ). For example, the processor  315  may detect the motion in ( 334 ) while operating in a presence mode and the supplemental motion recognition algorithm may be associated with a gesture mode. Generally, the supplemental motion recognition algorithm may consume more resources (e.g., compute, memory, electrical, and/or thermal) than the initial motion recognition algorithm. 
     The processor  315  may also retrieve ( 350 ) the second mode sensor data that is cached in the memory  320 . In certain embodiments, the processor  315  may perform ( 352 ) a “clutter removal” or background subtraction technique on the second mode sensor data retrieved from the memory  320 . In performing the clutter removal, the processor  315  may analyze the retrieved second mode sensor data and remove, from the retrieved second mode sensor data, a portion of the data that does not indicate motion. Accordingly, the remaining data may include data that indicates (or may indicate) motion. 
     The processor  315 , executing the supplemental motion recognition algorithm, may analyze ( 354 ) the cached second mode sensor data, with or without the background data (i.e., data without detected motion) removed. In embodiments, this step enables the supplemental motion recognition algorithm access to the initially-detected motion data in advance of a target being located in a zone tailored to the supplemental motion recognition algorithm, which may improve the motion detection and recognition features of the supplemental motion recognition algorithm. Additionally, the processor  315 , in executing the initial motion recognition algorithm, may analyze ( 356 ) the additional first mode sensor data. Accordingly, the processor  315  may concurrently execute the initial motion recognition algorithm and the supplemental motion recognition algorithm. 
     The processor  315  may retrieve ( 358 ), from the sensor(s)  325 , additional second mode sensor data, where the additional second mode sensor data is generated by the sensor(s)  325  operating in the second mode. The processor  315  may also execute the supplemental motion recognition algorithm to analyze the additional second mode sensor data and, based on the analysis, determine ( 360 ) whether there is motion detected. According to embodiments, if the processor  315  detects motion based on the additional second mode sensor data, there may be a target in the zone tailored to the supplemental motion recognition algorithm. For example, the supplemental motion recognition algorithm may be associated with a gesture mode, and the detected motion be associated with a gesture performed by a target. By analyzing both the initial (cached) second mode sensor data and the additional second mode sensor data, the supplemental motion recognition algorithm may experience improved accuracy with motion detection and recognition. In some scenarios, for example, a first motion algorithm may initiate a second algorithm when the first motion algorithm detects motion above a certain velocity, where this velocity may represent the lowest velocity motion that may contain a gesture. Additionally, the second algorithm, once activated, may search/analyze the cached data to determine if the cached data contains or includes a gesture. 
     If the processor  315  detects motion (“YES”), the processor  315  may continue to retrieve and analyze additional second mode sensor data. If the processor  315  does not detect motion (“NO”), the processor  315  may terminate ( 362 ) the supplemental motion recognition algorithm. Accordingly, the electronic device may experience improved resource savings by not executing the supplemental motion recognition algorithm when the second mode sensor data does not indicate motion that the supplemental motion recognition algorithm typically processes. Thus, the processor  315  may revert to solely executing the initial motion recognition algorithm, such as to conserve various resources of the electronic device. 
       FIG. 4  is a signal diagram  400  associated with various embodiments of the systems and methods. The signal diagram  400  includes a processor  415  (such as the processor  115  as described with respect to  FIG. 1 ) and one or more sensors  425  (such as one or more of the various sensors as discussed with respect to  FIG. 1 ). In embodiments, the processor  415  and the sensor(s)  425  may be embodied or incorporated in a single electronic device. It should be appreciated that various of the functionalities as illustrated in the signal diagram  400  may occur at various times or orders relative to the other functionalities. 
     In a first implementation, the sensor(s)  425  may be two separate sensors. For example, the sensors  425  may include a radar sensor configured to detect gross motion and an ultrasound sensor configured to detect breathing rate. In this implementation, a first sensor of the sensor(s)  425  may be a lower-sensitivity sensor and may correspondingly generate lower-sensitivity sensor data having a lower false positive rate; and a second sensor of the sensor(s)  425  may be a higher-sensitivity sensor and may correspondingly generate higher-sensitivity sensor data having a higher false positive rate. Further, in this implementation, the first sensor may be bidirectional and thus configured to emit signals in two or more directions (e.g., in front of and in back of the electronic device), and the second sensor may be directional and thus configured to emit signals in one direction (e.g., in front of the electronic device). It should be appreciated that both sensors may be either bidirectional or directional. 
     In a second implementation, the sensor(s)  425  may be a single sensor, for example a radar sensor. In this implementation, the single sensor may operate in multiple modes: a first mode that generates lower-sensitivity data having a lower false positive rate (e.g., a long range radar configuration), and a second mode that generates higher-sensitivity data having a higher false positive rate (e.g., a short range radar configuration). Therefore, although  FIG. 4  is illustrated as though a single sensor is interfacing with the processor  415  and generating both the lower-sensitivity and the higher-sensitivity sensor data, it should be appreciated that multiple sensors may interface with the processor  415  and respectively generate the respective sensor data. 
     The signal diagram  400  may begin when the sensor(s)  425  generates ( 430 ) a set of lower-sensitivity sensor data. For example, if the sensor  425  is a radar sensor, the radar sensor may emit radio waves and detect the resulting waveforms. According to embodiments, in generating the sensor data, the sensor(s)  425  may operate in a first operating mode. For example, if the sensor  425  is a radar sensor, the radar sensor may emit radio waves having a longer-range frequency, such as to detect the presence of targets in a proximity of the electronic device. The processor  415  may retrieve ( 432 ), from the sensor(s)  425 , the generated lower-sensitivity sensor data. It should be appreciated that the sensor(s)  425  may continuously generate, and the processor  415  may continuously retrieve, the lower-sensitivity sensor data. 
     The processor  415  may analyze the retrieved lower-sensitivity sensor data and determine ( 434 ) whether there is motion detected. In particular, the processor  415  may analyze the lower-sensitivity sensor data (e.g., received waveforms) and determine if motion is indicated in the lower-sensitivity sensor data. In analyzing the lower-sensitivity sensor data, the processor  415  may execute an initial algorithm, such as a motion detection algorithm, that is configured to process the lower-sensitivity sensor data resulting from the sensor(s)  425  operating in the first operating mode. If the processor  415  does not detect motion (“NO”), processing may return to ( 430 / 432 ), end, or proceed to other functionality. 
     Otherwise, if the processor  415  detects motion (“YES”), the processor  415  may start ( 435 ) a timeout window, where the timeout window may be various lengths (e.g., ten seconds, twenty seconds, or other lengths of time). Additionally, the processor  415  may request ( 436 ) the sensor(s)  425  to generate and provide higher-sensitivity sensor data. After receiving the request, the sensor(s)  425  may generate ( 438 ) the higher-sensitivity sensor data. 
     According to embodiments, the sensor(s)  425  may generate the higher-sensitivity sensor data while operating in a subsequent mode different from the first operating mode the sensor(s)  425  operated in when generating the lower-sensitivity sensor data in ( 430 ). For example, the lower-sensitivity sensor data generated in ( 430 ) may result from the sensor(s)  425  operating in a presence mode that generates longer-range waves, and the higher-sensitivity sensor data generated in ( 438 ) may result from the sensor(s)  425  operating in a gesture mode that generates shorter-range waves. It should be appreciated that different sensors may generate the lower-sensitivity sensor data and the higher-sensitivity sensor data. 
     The sensor(s)  425  may provide ( 442 ) the higher-sensitivity sensor data to the processor  415 . Additionally, the processor  425  may retrieve ( 444 ), from the sensor(s)  425 , additional lower-sensitivity sensor data. According to embodiments, the sensor(s)  425  may continue operation of the first operating mode, where the sensor(s)  425  may continuously generate, and the processor  415  may retrieve, lower-sensitivity sensor data. 
     The processor  415  may analyze ( 446 ) the additional lower-sensitivity and the higher-sensitivity sensor data. In analyzing the additional lower-sensitivity sensor data, the processor  415  may execute the initial algorithm that is configured to process the lower-sensitivity sensor data resulting from the sensor(s)  425  operating in the first operating mode. Further, in analyzing the higher-sensitivity sensor data, the processor  415  may execute a subsequent algorithm that is configured to process the higher-sensitivity sensor data resulting from the sensor(s)  425  operating in the subsequent operating mode. The processor  415  may analyze the higher-sensitivity sensor data to confirm ( 447 ) the change in motion that the processor  415  detects in ( 434 ). According to embodiments, the processor  415  may confirm the change in motion by detecting a separate or subsequent change in motion that is indicated in the higher-sensitivity sensor data, which may be a continuation of or related to the motion detected in ( 434 ). 
     At ( 448 ), the processor  415  may determine if the time window started in ( 435 ) is expired. For example, if the time window is ten seconds, the time window expires after ten seconds have elapsed. If the time window is not expired (“NO”), processing may return to ( 442 ) where additional lower-sensitivity sensor data and higher-sensitivity sensor data may be retrieved and analyzed. According to embodiments, the sensor(s)  425  may continue to generate, and the processor  415  may continue to analyze, the higher-sensitivity and the lower-sensitivity sensor data. 
     If the time window is expired (“YES”), the processor  415  may determine ( 450 ) whether additional motion is detected. According to embodiments, in determining whether additional motion is detected, the processor  415  may analyze the additional set of lower-sensitivity sensor data. That is, the processor  415  may determine whether additional motion is detected from the additional lower-sensitivity sensor data that is retrieved in ( 444 ) after the timeout window is started. If the processor  415  detects additional motion (“YES”), processing may end, repeat, or proceed to other functionality. In an embodiment, the processor  415  may continue to retrieve and analyze higher-sensitivity sensor data generated by the sensor(s)  425  operating in the subsequent operating mode, and facilitate functionalities accordingly. Additionally, the processor  415  may continually determine whether additional motion is detected, and if not, may proceed to ( 452 ). 
     If the processor  415  does not detect additional motion (“NO”), the processor  415  may deem ( 452 ), as a false positive, the confirmation of the change in motion from ( 447 ). In other words, by the processor  415  not detecting additional motion from the additional lower-sensitivity sensor data, the processor  415  may deem the motion confirmed from the higher-sensitivity sensor data to be a false positive. The processor  415  may further terminate ( 454 ) the subsequent algorithm that processes the higher-sensitivity sensor data. Additionally, the processor  415  may request ( 456 ) the sensor(s)  225  to cease generating the higher-sensitivity sensor data. Processing may then end, repeat, or proceed to other functionality. 
       FIGS. 5A-5F  illustrate example representations of interactions between an electronic device and a user, as well as various sensor operations based on the interactions. In each of  FIGS. 5A-5F , a user  503  is shown at a certain relative distance from a device  502 , where a distance scale  504  generally illustrates how far the user  503  is from the device  502  (e.g., short range, medium range, long range, out of range, or somewhere in between). Additionally, each of  FIGS. 5A-5F  depicts a frequency(ies) at which a radar sensor emits waves, as well as an amplitude of ultrasonic waves emitted by an ultrasound sensor. The representations of  FIGS. 5A-5F  may be read sequentially as the user  503  moves from out of range of the device  502  to within a short range of the device  502 . It should be appreciated that the representations depicted in  FIGS. 5A-5F  are merely examples and that additional or alternative representations are envisioned. 
       FIG. 5A  illustrates a representation  500  of the user  503  who is positioned out of range from the device  502 . In this configuration, the radar sensor emits ( 501 ) longer-range waves, which generally have a lower SNR (i.e., are less accurate) and allow detection of objects at a further distance from the device  502 . Additionally, the ultrasound sensor may be off ( 505 ). 
       FIG. 5B  illustrates a representation  506  of the user  503  who is positioned at a long range from the device  502 . In this configuration, the radar sensor still emits ( 507 ) longer-range waves, as the user  503  is still located a long range from the device  502 . Additionally, the ultrasound sensor remains off ( 508 ). 
       FIG. 5C  illustrates a representation  510  of the user  503  who approaches a medium range from the device  502 . In this configuration, the radar sensor emits ( 511 ) both longer-range waves and medium-range waves. According to embodiments, the radar sensor may emit the longer-range and medium-range waves within a transmission window, such as in an alternating or sequential fashion. By the radar sensor emitting the medium-range waves before the user  503  is positioned a medium range from the device  502 , the device  502  may begin to analyze the resulting sensed data in association with an algorithm that processes medium-range waves (e.g., a gesture mode). Additionally, the device  502  may activate the ultrasound sensor which can emit ( 512 ) higher-amplitude ultrasonic waves. 
       FIG. 5D  illustrates a representation  515  of the user  503  who is positioned a medium range from the device  502 . In this configuration, the radar sensor emits ( 516 ) longer-range waves, medium-range waves, and shorter-range waves. According to embodiments, the radar sensor may emit the longer-range, medium-range, and shorter-range waves within a transmission window, such as in an alternating or sequential fashion. By the radar sensor emitting both the shorter-range and medium-range waves, the device  502  may begin to analyze the resulting sensed data in association with an algorithm(s) that processes medium-range and/or short-range waves (e.g., a “gross” gesture mode and/or a “micro” gesture mode). Additionally, the ultrasound sensor may emit ( 517 ) higher-amplitude ultrasonic waves. 
       FIG. 5E  illustrates a representation  520  of the user  503  who approaches a short range from the device  502 . In this configuration, the radar sensor emits ( 521 ) longer-range waves, medium-range waves, and shorter-range waves. Compared to the emission ( 516 ) of  FIG. 5D , the emission ( 521 ) of  FIG. 5E  may include more shorter-range waves (and/or fewer longer-range and/or medium-range waves). According to embodiments, the radar sensor may emit the longer-range, medium-range, and shorter-range waves within a transmission window, such as in an alternating or sequential fashion. By the radar sensor emitting both the shorter-range and medium-range waves, the device  502  may begin to analyze the resulting sensed data in association with an algorithm(s) that processes medium-range and/or short-range waves (e.g., a “gross” gesture mode and/or a “micro” gesture mode). Additionally, the ultrasound sensor may emit ( 522 ) medium-amplitude ultrasonic waves. 
       FIG. 5F  illustrates a representation  525  of the user  503  who is positioned a short range from the device  502 . In this configuration, the radar sensor emits ( 526 ) longer-range waves and shorter-range waves. Compared to the emission ( 516 ) of  FIG. 5D  and the emission ( 521 ) of  FIG. 5E , the emission ( 526 ) of  FIG. 5F  may include more shorter-range waves (and/or fewer or no longer-range and/or medium-range waves). According to embodiments, the radar sensor may emit the longer-range and shorter-range waves within a transmission window, such as in an alternating or sequential fashion. By the radar sensor emitting the shorter-range waves, the device  502  may begin to analyze the resulting sensed data in association with an algorithm(s) that processes the short-range waves (e.g., a “micro” gesture mode). Additionally, by the radar sensor emitting the longer-range waves, the device  502  may still operate a presence mode configured to detect additional objects as well as continue consuming sensor data in case the user  503  retreats from the device  502 . Moreover, the ultrasound sensor may emit ( 527 ) lower-amplitude ultrasonic waves. 
       FIG. 6  is a flowchart of a method  600  for an electronic device to manage motion detection features. The method  600  begins with the electronic device retrieving (block  605 ), from a sensor of the electronic device, a set of sensor data. According to embodiments, the sensor may be a radar sensor, an ultrasound sensor, or another type of sensor. The electronic device may analyze the set of sensor data and determine (block  610 ) if motion is detected (i.e., if the set of sensor data indicates a change in motion of a target relative to the electronic device). In analyzing the set of sensor data, the electronic device may execute an initial motion recognition algorithm that may generally consume a lower amount of resources. 
     If the electronic device does not detect motion (“NO”), processing may return to block  605 , end, or proceed to other functionality. If the electronic device detects motion (“YES”), the electronic device may cache (block  615 ) the set of sensor data in memory. Generally, the electronic device may retrieve and cache the set of sensor data on a rolling basis as the sensor generates the set of sensor data. Additionally, the electronic device may remove (block  620 ) at least a portion of the set of sensor data that was cached, for example using a background subtraction or clutter removal technique. The removed sensor data may be sensor data that does not indicate motion. 
     The electronic device may initiate (block  625 ) a supplemental motion recognition algorithm. According to embodiments, the supplemental motion recognition algorithm may generally consume a higher amount of resources than does the initial motion recognition algorithm. The electronic device may analyze (block  630 ), by the supplemental motion recognition algorithm, the set of sensor data that was cached and that had at least the portion removed therefrom. 
     Based on the analysis of block  630 , the electronic device may determine (block  635 ) whether the change in motion detected in block  610  is confirmed (i.e., whether the change in motion detected in block  610  is not a false positive). If the electronic device determines that the change in motion is not confirmed (“NO”), processing may return to block  605 , end, or proceed to other functionality. Additionally, the electronic device may terminate the supplemental motion recognition algorithm. 
     If the electronic device determines that the change in motion is confirmed (“YES”), the electronic device may retrieve (block  640 ), from the sensor, an additional set of sensor data. In another implementation, the electronic device may retrieve the additional set of sensor data from an additional (i.e., different) sensor. The electronic device may analyze (block  645 ), by the supplemental motion recognition algorithm, the additional set of sensor data, and facilitate various functionalities of the electronic device accordingly. 
     Based on the analysis of block  645 , the electronic device may determine (block  650 ) whether there is an additional change in motion detected. According to embodiments, the additional change in motion may be indicative of performed gestures or other user movements performed in proximity to the electronic device. If the electronic device detects the additional change in motion (“YES”), processing may return to block  640 , or proceed to other functionality. If the electronic device does not detect the additional change in motion (“NO”), processing may end, repeat, or proceed to other functionality. Additionally, if the electronic device does not detect the additional change in motion, the electronic device may terminate the supplemental motion recognition algorithm. 
       FIG. 7  is a flowchart of another method  700  for an electronic device to manage motion detection features. The method  700  begins with the electronic device retrieving (block  705 ), from a sensor of the electronic device operating in a first sensitivity mode, a first set of first mode sensor data. According to embodiments, the sensor may be a radar sensor, an ultrasound sensor, or another type of sensor. The electronic device may analyze the first set of first mode sensor data and determine (block  710 ) if motion is detected (i.e., if the first set of first mode sensor data indicates a change in motion of a target relative to the electronic device). In analyzing the first set of first mode sensor data, the electronic device may execute an initial motion recognition algorithm that may generally consume a lower amount of resources. 
     If the electronic device does not detect motion (“NO”), processing may return to block  705 , end, or proceed to other functionality. If the electronic device detects motion (“YES”), the electronic device may retrieve (block  715 ), from the sensor operating in a second sensitivity mode, a set of second mode sensor data. Additionally, the electronic device may cache (block  720 ) the set of second mode sensor data in memory. Further, the electronic device may remove (block  725 ) at least a portion of the set of second mode sensor data that was cached, for example using a background subtraction or clutter removal technique. The removed sensor data may be sensor data that does not indicate motion. 
     The electronic device may retrieve (block  730 ), from the sensor operating in the first sensitivity mode, a second set of first mode sensor data. Further, electronic device may analyze, by the initial motion recognition algorithm, the second set of first mode sensor data. Thus, the sensor may continue to operate in the first sensitivity mode, and the initial motion recognition algorithm may continue to analyze the resulting sensor data. 
     The electronic device may also initiate (block  735 ) a supplemental motion recognition algorithm. According to embodiments, the supplemental motion recognition algorithm may generally consume a higher amount of resources than does the initial motion recognition algorithm. The electronic device may analyze (block  740 ), by the supplemental motion recognition algorithm, the set of second mode sensor data that was cached and that had at least the portion removed therefrom. Further, the electronic device may retrieve (block  745 ), from the sensor operating in the second sensitivity mode, an additional set of second mode sensor data, and analyze, using the supplemental motion recognition algorithm, the additional set of second mode sensor data. 
     Based on this analysis, the electronic device may determine (block  750 ) whether there is additional motion detected (i.e., whether the additional set of second mode sensor data indicates motion). If the electronic device determines that additional motion is detected (“YES”), processing may return to block  745 , end, or proceed to other functionality. If the electronic device determines that additional motion is not detected (“NO”), the electronic device may terminate (block  755 ) the supplemental motion recognition algorithm. 
       FIG. 8  is a flowchart of a method  800  for an electronic device to manage sensor activity. The method  800  begins with the electronic device retrieving (block  805 ), from a sensor of the electronic device operating in a lower-sensitivity mode, a set of lower-sensitivity sensor data. According to embodiments, the sensor may be a radar sensor, an ultrasound sensor, or another type of sensor. The electronic device may analyze the set of lower-sensitivity sensor data and determine (block  810 ) if motion is detected (i.e., if the set of lower-sensitivity sensor data indicates a change in motion of a target relative to the electronic device). In analyzing the set of lower-sensitivity sensor data, the electronic device may execute an initial algorithm that may generally consume a lower amount of resources. 
     If the electronic device does not detect motion (“NO”), processing may return to block  805 , end, or proceed to other functionality. If the electronic device detects motion (“YES”), the electronic device may start ( 815 ) a timeout window, which may vary in length (e.g., ten seconds, one minute, etc.). Further, the electronic device may retrieve (block  820 ), from the sensor operating in a higher-sensitivity mode, a set of higher-sensitivity sensor data. According to embodiments, the electronic device may request the sensor to generate the set of higher-sensitivity sensor data and initiate a subsequent algorithm that processes the set of higher-sensitivity sensor data and that may generally consume a higher amount of resources than does the initial algorithm. Additionally, the sensor may continue to operate in the lower-sensitivity mode, and the electronic device may retrieve (block  825 ), from the sensor operating in the lower-sensitivity mode, an additional set of lower-sensitivity sensor data. 
     The electronic device may analyze (block  830 ) the set of higher-sensitivity sensor data and the additional set of lower-sensitivity sensor data. In particular, the electronic device may analyze the set of higher-sensitivity sensor data using the subsequent algorithm and analyze the additional set of lower-sensitivity sensor data using the initial algorithm. Based on analyzing the set of higher-sensitivity sensor data, the electronic device may confirm (block  835 ) the change in motion that was detected in block  810 . In confirming the change in motion, the electronic device may detect a subsequent change in motion relative to the electronic device, which may be separate from or a continuation of the change in motion that was detected in block  810 , where the change in motion may be associated with the same or different target. In some situations, the electronic device may not confirm the change in motion as a result of analyzing the set of higher-sensitivity sensor data. 
     At block  840 , the electronic device may determine whether the timeout window has expired. If the timeout window has not expired (“NO”), processing may return to block  820 , end, or proceed to other functionality. If the timeout window has expired (“YES”), processing may proceed to block  845  at which the electronic device may determine, based on analyzing the additional set of lower-sensitivity sensor data, whether an additional change in motion relative to the electronic device was detected. If the electronic device detects the additional change in motion (“YES”), processing may end, repeat, or proceed to other functionality. 
     If the electronic device does not detect the additional change in motion (“NO”), the electronic device may deem (block  850 ), as a false positive, the confirmation of the change in motion from block  835 . Additionally, the electronic device may terminate (block  855 ) the subsequent algorithm that processes the higher-sensitivity sensor data. Further, in embodiments, the electronic device may request the sensor to cease generating the set of higher-sensitivity sensor data. 
     Although  FIG. 8  describes the method  800  as operating using a single sensor, it should be appreciated that the method  800  may operate using multiple sensors. In particular, a first sensor may operate in the lower-sensitivity mode and generate the lower-sensitivity sensor data, and a second, different sensor may operate in the higher-sensitivity mode and generate the higher-sensitivity sensor data. 
       FIG. 9  illustrates an example electronic device  945  in which the functionalities as 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 a 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 supply or provide power to the electronic device  945  and components thereof. 
     The memory  978  may store an operating system  979  capable of facilitating the functionalities as discussed herein as well as a cache  980  configured to store/cache various sensor data and/or other data. The processor  981  may interface with the memory  978  to execute the operating system  979  and retrieve data from the cache  980 , as well as execute a set of applications  971  such as one or more motion detection applications  972  (which the memory  978  can also store). For example, the motion detection applications  972  may include multiple motion detection algorithms configured to analyze various types of sensor data. The memory  978  can include one or more forms of volatile and/or non-volatile, fixed and/or removable memory, such as read-only memory (ROM), electronic programmable read-only memory (EPROM), random access memory (RAM), erasable electronic programmable read-only memory (EEPROM), and/or other hard drives, flash memory, MicroSD cards, and others. 
     The electronic device  945  may further include a communication module  975  configured to interface with the one or more external ports  973  to communicate data via one or more networks  950 . For example, the communication module  975  may leverage the external ports  973  to establish TCP connections for connecting 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 transceivers functioning in accordance with IEEE standards, 3GPP standards, or other standards, and configured to receive and transmit data via the one or more external ports  973 . More particularly, 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 to communicatively connect the electronic device  945  to additional devices or components. Further, the communication module  945  may include one or more LAN and/or WPAN transceivers configured to connect the electronic device  945  to local area networks and/or personal area networks, such as a Bluetooth® network. 
     The electronic device  945  may further include a set of sensors  964 . In particular, the set of sensors  964  may include one or more radar sensors  965 , one or more ultrasound sensors  966 . one or more proximity sensors  967 , one or more image sensors  969 , and/or one or MOM other sensors  969  (e.g., accelerometers, touch sensors, NFC components, etc.). The electronic device  945  may include an audio module  977  including hardware components such as a speaker  985  for outputting audio and a microphone  986  for detecting or receiving audio. The electronic device  945  may further include a user interface  974  to present information to the user and/or receive inputs from the user. As shown in  FIG. 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, and others). The user interface  974  may also include the speaker  985  and the microphone  986 . In embodiments, the display screen  987  is a touchscreen display using singular of combinations of display technologies and may include a thin, transparent touch sensor component superimposed upon a display section that is viewable by a user. For example, such displays include capacitive displays, resistive displays, surface acoustic wave (SAW displays, optical imaging displays, and the like. 
     In general, a computer program product in accordance with an embodiment includes a computer usable storage medium (e.g., standard random access memory (RAM), an optical disc, a universal serial bus (USB) drive, or the like) having computer-readable program code embodied therein, wherein the computer-readable program code is adapted to be executed by the processor  981  (e.g., working in connection with the operating system  979 ) to facilitate the functions as described herein. In this regard, the program code may be implemented in any desired language, and may be implemented as machine code, assembly code, byte code, interpretable source code or the like (e.g., via C, C++, Java, Actionscript, Objective-C, Javascript, CSS, XML, and/or others). 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter of the present disclosure. 
     Additionally, certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code stored on a machine-readable medium) or hardware modules. A hardware module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) 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 a hardware module that operates to perform certain operations as described herein. 
     In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the term hardware should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The methods  600 ,  700 ,  800  may include one or more function blocks, modules, individual functions or routines in the form of tangible computer-executable instructions that are stored in a non-transitory computer-readable storage medium and executed using a processor of a computing device (e.g., a server device, a personal computer, a smartphone, a tablet computer, a watch, a mobile computing device, or other client computing device, as described herein). The methods  600 ,  700 ,  800  may be included as part of any backend server, client computing device modules of the example environment, for example, or as part of a module that is external to such an environment. Though the figures may be described with reference to the other figures for ease of explanation, the methods  600 ,  700 ,  800  can be utilized with other objects and user interfaces. Furthermore, although the explanation above describes steps of the methods  600 ,  700 ,  800  being performed by specific devices, this is done for illustration purposes only. The blocks of the methods  600 ,  700 ,  800  may be performed by one or more devices or other parts of the environment. 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. 
     Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as an SaaS. For example, as indicated above, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., APIs). 
     Still further, the figures depict some embodiments of the example environment for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. 
     Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for managing sensor operation through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.