1. A wearable device configured for energy-efficient real-time processing, comprising: a sensor array capturing real-time data; a processing unit employing an energy-efficient algorithm to process the captured data in real-time; and a power management module optimizing energy consumption based on the processing demands.

2. The wearable device of claim 1, wherein the processing unit includes: a dynamic frequency scaling mechanism adapting processor frequency based on real-time data processing requirements.

3. The wearable device of claim 1, further comprising: a data compression module reducing data size before processing to optimize energy consumption.

4. The wearable device of claim 1, wherein the power management module utilizes: a feedback loop from the processing unit, dynamically adjusting energy allocations based on the complexity of incoming data.

5. The wearable device of claim 1, further comprising: a data prioritization mechanism classifying incoming data from the sensor array and selectively processing high-priority data to optimize energy consumption.

6. A method for energy-efficient real-time processing in wearable devices, comprising: capturing real-time data via a sensor array of a wearable device; employing an energy-efficient algorithm to process the data in real-time; and dynamically managing power resources based on processing demands.

7. The method of claim 6, further comprising: adjusting processor frequency in response to the real-time data processing requirements to reduce energy consumption.

8. The method of claim 6, including: compressing the captured real-time data prior to processing, thereby conserving energy by reducing computational demands.

9. The method of claim 6, wherein managing power resources involves: monitoring the complexity of incoming data; estimating energy requirements for processing said data; and adjusting energy allocations dynamically based on the estimations.

10. The method of claim 6, further comprising: categorizing incoming data based on importance; selectively processing high-priority data in real-time; and queuing or discarding lower-priority data to conserve energy. Energy Efficient Algorithms for Real-Time Processing in Wearable Devices Abstract The present invention discloses a wearable device tailored for energy-efficient real-time processing, including a sensor array capturing real-time data, a processing unit utilizing an energy-efficient algorithm to process the acquired data in real-time, and a power management module strategically managing energy consumption in alignment with the processing requirements. This wearable device offers a novel solution for optimizing energy efficiency during real-time data processing, ensuring extended operational life while maintaining seamless real-time processing capabilities. , Claims:Claims :

1. A wearable device configured for energy-efficient real-time processing, comprising: a sensor array capturing real-time data; a processing unit employing an energy-efficient algorithm to process the captured data in real-time; and a power management module optimizing energy consumption based on the processing demands.

2. The wearable device of claim 1, wherein the processing unit includes: a dynamic frequency scaling mechanism adapting processor frequency based on real-time data processing requirements.

3. The wearable device of claim 1, further comprising: a data compression module reducing data size before processing to optimize energy consumption.

4. The wearable device of claim 1, wherein the power management module utilizes: a feedback loop from the processing unit, dynamically adjusting energy allocations based on the complexity of incoming data.

5. The wearable device of claim 1, further comprising: a data prioritization mechanism classifying incoming data from the sensor array and selectively processing high-priority data to optimize energy consumption.

6. A method for energy-efficient real-time processing in wearable devices, comprising: capturing real-time data via a sensor array of a wearable device; employing an energy-efficient algorithm to process the data in real-time; and dynamically managing power resources based on processing demands.

7. The method of claim 6, further comprising: adjusting processor frequency in response to the real-time data processing requirements to reduce energy consumption.

8. The method of claim 6, including: compressing the captured real-time data prior to processing, thereby conserving energy by reducing computational demands.

9. The method of claim 6, wherein managing power resources involves: monitoring the complexity of incoming data; estimating energy requirements for processing said data; and adjusting energy allocations dynamically based on the estimations.

10. The method of claim 6, further comprising: categorizing incoming data based on importance; selectively processing high-priority data in real-time; and queuing or discarding lower-priority data to conserve energy.

Description:Energy Efficient Algorithms for Real-Time Processing in Wearable Devices
Field of the Invention
[0001] The present invention is situated within the realm of wearable technology and algorithmic optimization. Specifically, this invention centers on cutting-edge algorithms crafted for real-time processing in wearable devices, with a pronounced emphasis on energy efficiency. Recognizing the constraints of battery capacity and the incessant demand for real-time functionalities in wearables, this invention endeavors to strike an optimal balance between computational prowess and energy consumption. By harnessing these novel algorithms, the invention aims to extend device longevity, reduce charging frequency, and ensure seamless user experiences even with continuous, resource-intensive tasks.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The rapid advancement of wearable devices, such as smartwatches, fitness trackers, and medical sensors, has brought about new opportunities and challenges in real-time data processing. These devices often operate under constrained energy resources, making energy efficiency a critical consideration. To enable real-time processing while preserving battery life, energy-efficient algorithms have become essential. These algorithms optimize computation and communication tasks to minimize energy consumption, prolong device runtime, and enhance user experience.
[0004] Wearable devices often feature multiple sensors collecting diverse data, such as accelerometer, gyroscope, and heart rate measurements. Energy-efficient sensor data fusion algorithms combine information from these sensors to improve accuracy while reducing the need for frequent data transmissions. The Kalman filter is a well-known algorithm that combines noisy sensor data to estimate the true state of a system. It is applied to wearable devices to reduce sensor noise and save energy by transmitting only refined data.
[0005] Dynamic Voltage and Frequency Scaling (DVFS) adjusts the operating voltage and frequency of a device's processor to match the computational workload. When processing requirements are low, the algorithm lowers voltage and frequency to reduce energy consumption. Conversely, during periods of high demand, it increases performance. DVFS techniques, like the Linux kernel's "cpufreq" subsystem, dynamically adjust processor parameters, optimizing energy usage without compromising real-time processing needs.
[0006] To save energy, wearable devices employ sparse sampling techniques that collect data intermittently, rather than continuously. In addition, wake-up mechanisms utilize low-power sensors to monitor for specific events, triggering the main processor when necessary. For instance, Google's "Always On" feature in Android Wear uses low-power sensors to wake up the device upon detecting wrist movements, reducing the need for constant high-power processing.
[0007] Offloading computation to more resource-rich devices at the edge of the network, like smartphones, can reduce the energy burden on wearables. Edge computing frameworks, like TensorFlow Lite, enable energy-efficient on-device processing by optimizing machine learning models for low-power consumption. Wearable devices can leverage edge devices for intensive tasks like data analytics or complex pattern recognition.
[0008] Energy-efficient algorithms adapt to changing conditions by dynamically adjusting processing and communication parameters. Adaptive algorithms monitor sensor data and user behavior to determine when to engage high-performance modes or energy-saving modes. For example, Fitbit's adaptive step detection algorithm adjusts its sampling rate based on user activity levels to save power while maintaining accurate step count measurements.
[0009] Predictive algorithms analyze historical usage patterns and device behavior to estimate battery depletion rates. By forecasting battery drain, wearable devices can optimize their processing and communication strategies to extend battery life. Samsung's Adaptive Battery feature in its wearable devices uses predictive analytics to allocate resources efficiently and ensure that power-hungry applications don't drain the battery excessively.
[00010] In conclusion, energy-efficient algorithms are instrumental in enabling real-time processing capabilities in wearable devices while addressing the challenges posed by limited energy resources. These algorithms optimize data fusion, processing strategies, and communication patterns to strike a balance between real-time performance and energy conservation. The prior art examples discussed demonstrate the various techniques and methodologies used to develop energy-efficient algorithms, contributing to the longevity and usability of wearable devices in a resource-constrained environment.
[00011]
[00012] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00013] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00014] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00015] The following paragraphs provide additional support for the claims of the subject application.
[00016] The present invention is situated within the realm of wearable technology and algorithmic optimization. Specifically, this invention centers on cutting-edge algorithms crafted for real-time processing in wearable devices, with a pronounced emphasis on energy efficiency. Recognizing the constraints of battery capacity and the incessant demand for real-time functionalities in wearables, this invention endeavors to strike an optimal balance between computational prowess and energy consumption. By harnessing these novel algorithms, the invention aims to extend device longevity, reduce charging frequency, and ensure seamless user experiences even with continuous, resource-intensive tasks.
[00017] Described herein a wearable device that represents a pioneering advancement in energy-efficient real-time processing, seamlessly integrating technology with optimal energy consumption. The device is characterized by a sensor array that captures real-time data, forming the foundation for subsequent processing. This captured data is expertly processed by a processing unit, which leverages an energy-efficient algorithm designed to strike a balance between performance and energy conservation.
[00018] Central to the device's efficiency is a power management module that actively optimizes energy consumption. This optimization is tailored to the demands of data processing, ensuring that energy allocation remains aligned with the processing requirements. This feature safeguards extended operational life without compromising real-time processing capabilities.
[00019] A distinctive feature within the processing unit is the dynamic frequency scaling mechanism. This adaptive mechanism modifies the processor's frequency in response to real-time data processing needs. By tailoring the processor's speed to match the current workload, the device achieves optimal energy utilization while maintaining processing efficiency.
[00020] Furthermore, the device enhances efficiency through data compression. A data compression module strategically reduces data size before processing, minimizing the energy required for data manipulation. This approach is a key driver in optimizing energy consumption while preserving the device's real-time processing capabilities.
[00021] Efficiency is further bolstered through a feedback loop embedded in the power management module. This loop dynamically adjusts energy allocations based on the complexity of incoming data, allowing the device to allocate energy resources where they are most needed, precisely optimizing energy consumption.
[00022] The device extends its capabilities with a data prioritization mechanism. This mechanism categorizes incoming data from the sensor array and selectively processes high-priority data. This selective processing ensures that energy resources are directed towards critical data processing tasks, further enhancing the device's efficiency.
[00023] In essence, the wearable device revolutionizes energy-efficient real-time processing through its integrated features. By capturing, processing, and optimizing energy consumption, it offers a comprehensive solution that balances performance and sustainability. Through dynamic frequency scaling, data compression, and adaptive power management, the device showcases the potential to achieve efficient real-time processing without compromising performance or endurance.
[00024] The method introduced ushers in a revolutionary era of energy-efficient real-time processing within wearable devices, aligning technology with sustainable operational practices. At its core, the method is centered around capturing real-time data through a sensor array embedded within wearable devices. This captured data forms the basis for subsequent processing, which is executed using an energy-efficient algorithm. This algorithm is strategically designed to ensure real-time processing capabilities without compromising energy consumption.
[00025] A pivotal aspect of the method is the dynamic management of power resources. This management strategy is rooted in the principle of adapting power resources to meet the demands of data processing. By dynamically allocating energy resources based on processing requirements, the method ensures efficient energy utilization, extending the wearable device's operational lifespan.
[00026] Efficiency is heightened through the introduction of processor frequency adjustments. The method encompasses the capability to adjust processor frequency in direct response to real-time data processing needs. This adaptable approach reduces energy consumption by calibrating the processor's performance to match the current workload.
[00027] Furthermore, the method embraces data compression as a means of conserving energy. Before processing, the captured real-time data is compressed, effectively reducing computational demands. This compression technique reduces the energy expended during data processing while preserving the accuracy and effectiveness of the results.
[00028] Energy management is taken to a new level through comprehensive resource monitoring. The method involves monitoring the complexity of incoming data, estimating the energy requirements for processing, and dynamically adjusting energy allocations based on these estimations. This sophisticated approach ensures that energy resources are optimally distributed, maximizing efficiency.
[00029] The method's innovation extends to data prioritization. By categorizing incoming data based on importance, the method selectively processes high-priority data in real-time. Simultaneously, lower-priority data is either queued or discarded to conserve energy resources. This selective processing approach ensures that energy is channeled toward critical tasks while mitigating unnecessary energy expenditure on less significant data.
[00030] In essence, the method revolutionizes energy-efficient real-time processing within wearable devices. By capturing, processing, dynamically managing power resources, adjusting processor frequency, implementing data compression, and prioritizing data, it establishes a holistic approach to sustainable and high-performance wearable device operations. Through these integrated strategies, the method showcases the potential to achieve real-time processing efficiency without compromising on device endurance or functionality.
[00031]
Brief Description of the Drawings
[00032] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00033] FIG. 1 represents an architectural overview of a wearable device configured for energy-efficient real-time processing, according to some embodiments of the present disclosure.
[00034] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for energy-efficient real-time processing in wearable devices, according to some embodiments of the present disclosure.
[00035]
Detailed Description
[00036] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00037] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00038] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00039] The present invention is situated within the realm of wearable technology and algorithmic optimization. Specifically, this invention centers on cutting-edge algorithms crafted for real-time processing in wearable devices, with a pronounced emphasis on energy efficiency. Recognizing the constraints of battery capacity and the incessant demand for real-time functionalities in wearables, this invention endeavors to strike an optimal balance between computational prowess and energy consumption. By harnessing these novel algorithms, the invention aims to extend device longevity, reduce charging frequency, and ensure seamless user experiences even with continuous, resource-intensive tasks.
[00040] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00041] As wearable devices continue to advance in functionality and usage, energy efficiency becomes a critical factor in ensuring their practicality and user acceptance. In this context, an innovative wearable device configured for energy-efficient real-time processing has been developed. This device 100 combines cutting-edge sensor technology, intelligent processing algorithms, and power management strategies to provide real-time insights while optimizing energy consumption.
[00042] This comprehensive exploration delves into the design and implementation of an energy-efficient wearable device 100 tailored for real-time data processing. According to a pictorial portrayal in FIG. 1, illustrating an architectural setup of the device 100 integrates a sensor array 102, a processing unit 104 equipped with an energy-efficient algorithm, and a power management module 106 for optimizing energy consumption. Additional features, such as dynamic frequency scaling, data compression, power allocation feedback loops, and data prioritization, further enhance the device's energy efficiency. This disclosure discusses the significance of energy-efficient processing in wearables, elaborates on each component's functionality, provides practical examples, and outlines potential future developments.

[00043] In yet another embodiment, the wearable device is equipped with a sensor array designed to capture real-time data from the wearer's environment or body. Examples of sensors that could be integrated include accelerometers, heart rate monitors, temperature sensors, and more. These sensors collect data relevant to the device's intended use, such as tracking physical activity, monitoring vital signs, or environmental sensing.
[00044] In yet another embodiment, the processing unit is the core of the device's real-time data processing capabilities. It employs an energy-efficient algorithm designed to process the captured data while minimizing power consumption. This algorithm strikes a balance between processing accuracy and energy efficiency, ensuring that the device can provide meaningful insights without draining the battery rapidly.
[00045] In yet another embodiment, the power management module plays a pivotal role in optimizing energy consumption. It dynamically adjusts energy allocations to various components based on the processing demands. This module is responsible for ensuring that power resources are allocated efficiently, prolonging the device's battery life while maintaining performance.
[00046] In yet another embodiment, the processing unit incorporates a dynamic frequency scaling mechanism that adapts the processor frequency in real-time based on the processing requirements. For instance, during periods of high processing demand, such as when processing complex data or running intensive algorithms, the processor frequency can be increased to meet the demand. Conversely, during periods of low processing demand, the frequency can be scaled down to conserve energy.
[00047] To further optimize energy consumption, the wearable device integrates a data compression module. This module reduces the size of the captured data before it is processed. By compressing the data, the device reduces the amount of data that needs to be transferred and processed, resulting in lower energy consumption.
[00048] In yet another embodiment, the power management module utilizes a feedback loop from the processing unit. This loop constantly monitors the complexity of incoming data and the processing demands. Based on this feedback, the power management module dynamically adjusts energy allocations to different components. For example, if the processing unit encounters a particularly complex data set, the power management module can allocate more energy resources to ensure accurate processing.
[00049] To further optimize energy consumption, the wearable device incorporates a data prioritization mechanism. This mechanism classifies incoming data from the sensor array into different priority levels. High-priority data, which might include critical health information or urgent alerts, is processed immediately, while lower-priority data can be processed at a slower pace or deferred to conserve energy.
[00050] In yet another embodiment, the energy-efficient wearable device finds application in various domains, including fitness tracking, healthcare monitoring, and environmental sensing. The energy-efficient design ensures extended battery life, making it more convenient for users to wear the device throughout the day without frequent recharging. For healthcare professionals, the device's real-time data processing capabilities enable timely interventions and insights into patients' health conditions.
[00051] As technology evolves, potential future developments for the energy-efficient wearable device include, further refinement of energy-efficient algorithms to achieve even higher levels of energy optimization without compromising accuracy. Incorporating machine learning techniques to predict processing demands based on usage patterns and adjust power allocations accordingly. Integrating the wearable device with AI assistants to provide personalized insights and recommendations based on the processed data.
[00052] Referring to one or more preceding embodiments, the energy-efficient wearable device 100 designed for real-time processing represents a significant step forward in wearable technology. By integrating advanced sensors, energy-efficient algorithms, and intelligent power management strategies, this device offers real-time insights while ensuring extended battery life. As wearables continue to play a crucial role in monitoring health, physical activity, and environmental conditions, the energy-efficient approach holds promise for enhancing user experience and promoting practical, continuous usage.
[00053] As wearable technology becomes increasingly integrated into our lives, ensuring optimal energy consumption while delivering real-time processing capabilities is a paramount challenge. This disclosure dives into a comprehensive method 200 designed to achieve energy-efficient real-time processing within wearable devices. By (at step 202) capturing real-time data through a sensor array, (at step 204) employing an energy-efficient algorithm, and (at step 206) dynamically managing power resources, this method 200 addresses the need for prolonged battery life and optimal performance.
[00054] Figuratively depicted in FIG. 2, representing a flow diagram of the method 200, initiates with the wearable device's sensor array capturing real-time data. This data collection encompasses a wide range of sensory inputs, including movement, vital signs, environmental conditions, and more. The sensor array acts as the device's sensory gateway, allowing it to interact with the user's environment or body and gather relevant data continuously.
[00055] Following data capture, the method 200 employs an energy-efficient algorithm for real-time processing. This algorithm is meticulously designed to strike a harmonious balance between processing accuracy and energy consumption. The algorithm's architecture ensures that processing demands are met while conserving power resources. The crux of the energy-efficient approach lies in dynamically managing power resources based on processing demands. As the processing unit engages the algorithm to process real-time data, the device continually monitors these demands and adapts power resource allocation accordingly.
[00056] Incorporating a dynamic frequency scaling mechanism enhances energy efficiency. In response to real-time data processing requirements, the processing unit adapts the processor frequency. For instance, during periods of low processing demand, the processor frequency can be scaled down to minimize energy consumption. Conversely, during moments of high processing demand, the frequency can be increased to fulfill the computational needs while optimizing energy usage.
[00057] The method 200 incorporates a data compression stage to further conserve energy. Before processing, the captured real-time data undergoes compression. This step reduces data size, leading to a reduction in computational demands during processing. By compressing the data, the wearable device achieves efficiency gains by minimizing the data's impact on both processing power and memory usage.
[00058] Managing power resources involves continuous monitoring of the complexity of incoming data. The method estimates the energy requirements necessary to process the data effectively. Based on these estimations, the device dynamically adjusts energy allocations to various components. This dynamic power allocation ensures that the processing unit receives the energy it requires without over-allocating resources, thereby optimizing energy consumption.
[00059] The method 200 extends its energy-efficiency strategy by implementing a data prioritization mechanism. This mechanism categorizes incoming data based on its importance. High-priority data, such as critical health measurements, are processed in real-time. In contrast, lower-priority data can be queued or even discarded to preserve energy. This intelligent approach ensures that essential information is processed promptly, aligning with the user's needs while conserving energy on less crucial tasks. The method's application spans across diverse wearable device domains, including health monitoring, fitness tracking, and environmental sensing. Future developments may include:
[00060] Incorporating machine learning algorithms to predict processing demands and enhance power allocation accuracy. Integrating alternative energy sources, such as solar cells or energy harvesting mechanisms, to supplement power resources. Leveraging edge computing techniques to offload computational tasks, reducing the wearable device's energy consumption.
[00061] Referring to one or more preceding embodiments, the detailed method 200 outlined in this disclosure marks a significant step towards achieving energy-efficient real-time processing in wearable devices. By capturing real-time data, employing an energy-efficient algorithm, and dynamically managing power resources, this method harmonizes optimal performance with extended battery life. As wearable technology continues to evolve, these energy-efficient strategies are essential for ensuring user satisfaction, device usability, and technological advancement.
[00062] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00063] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00064] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00065] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00066] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00067] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A wearable device configured for energy-efficient real-time processing, comprising:
a sensor array capturing real-time data;
a processing unit employing an energy-efficient algorithm to process the captured data in real-time; and
a power management module optimizing energy consumption based on the processing demands.
2. The wearable device of claim 1, wherein the processing unit includes:
a dynamic frequency scaling mechanism adapting processor frequency based on real-time data processing requirements.
3. The wearable device of claim 1, further comprising:
a data compression module reducing data size before processing to optimize energy consumption.
4. The wearable device of claim 1, wherein the power management module utilizes:
a feedback loop from the processing unit, dynamically adjusting energy allocations based on the complexity of incoming data.
5. The wearable device of claim 1, further comprising:
a data prioritization mechanism classifying incoming data from the sensor array and selectively processing high-priority data to optimize energy consumption.
6. A method for energy-efficient real-time processing in wearable devices, comprising:
capturing real-time data via a sensor array of a wearable device;
employing an energy-efficient algorithm to process the data in real-time; and
dynamically managing power resources based on processing demands.
7. The method of claim 6, further comprising:
adjusting processor frequency in response to the real-time data processing requirements to reduce energy consumption.
8. The method of claim 6, including:
compressing the captured real-time data prior to processing, thereby conserving energy by reducing computational demands.
9. The method of claim 6, wherein managing power resources involves:
monitoring the complexity of incoming data;
estimating energy requirements for processing said data; and
adjusting energy allocations dynamically based on the estimations.
10. The method of claim 6, further comprising:
categorizing incoming data based on importance;
selectively processing high-priority data in real-time; and
queuing or discarding lower-priority data to conserve energy.

Energy Efficient Algorithms for Real-Time Processing in Wearable Devices
Abstract
The present invention discloses a wearable device tailored for energy-efficient real-time processing, including a sensor array capturing real-time data, a processing unit utilizing an energy-efficient algorithm to process the acquired data in real-time, and a power management module strategically managing energy consumption in alignment with the processing requirements. This wearable device offers a novel solution for optimizing energy efficiency during real-time data processing, ensuring extended operational life while maintaining seamless real-time processing capabilities. , Claims:Claims
I/We Claim:
1. A wearable device configured for energy-efficient real-time processing, comprising:
a sensor array capturing real-time data;
a processing unit employing an energy-efficient algorithm to process the captured data in real-time; and
a power management module optimizing energy consumption based on the processing demands.
2. The wearable device of claim 1, wherein the processing unit includes:
a dynamic frequency scaling mechanism adapting processor frequency based on real-time data processing requirements.
3. The wearable device of claim 1, further comprising:
a data compression module reducing data size before processing to optimize energy consumption.
4. The wearable device of claim 1, wherein the power management module utilizes:
a feedback loop from the processing unit, dynamically adjusting energy allocations based on the complexity of incoming data.
5. The wearable device of claim 1, further comprising:
a data prioritization mechanism classifying incoming data from the sensor array and selectively processing high-priority data to optimize energy consumption.
6. A method for energy-efficient real-time processing in wearable devices, comprising:
capturing real-time data via a sensor array of a wearable device;
employing an energy-efficient algorithm to process the data in real-time; and
dynamically managing power resources based on processing demands.
7. The method of claim 6, further comprising:
adjusting processor frequency in response to the real-time data processing requirements to reduce energy consumption.
8. The method of claim 6, including:
compressing the captured real-time data prior to processing, thereby conserving energy by reducing computational demands.
9. The method of claim 6, wherein managing power resources involves:
monitoring the complexity of incoming data;
estimating energy requirements for processing said data; and
adjusting energy allocations dynamically based on the estimations.
10. The method of claim 6, further comprising:
categorizing incoming data based on importance;
selectively processing high-priority data in real-time; and
queuing or discarding lower-priority data to conserve energy.