Description:WEARABLE DEVICE FOR IMPROVED HUMAN MOBILITY
Field of the Invention
[0001] The present invention relates to wearable technology focused on enhancing human mobility. Specifically, it pertains to a device equipped with sensors, microprocessors, and feedback mechanisms designed to monitor, analyze, and guide users in their movements for improved mobility and muscle functionality.
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] Human mobility, the ability to move and function efficiently, has always been a paramount concern. From athletes trying to optimize performance to patients rehabilitating from injuries or managing degenerative conditions, the quest for better movement is universal. Traditionally, methods for improving mobility have relied on human observation, physical therapy, or cumbersome equipment. These methods, although effective, come with limitations: they lack continuous monitoring, are often subjective, or are limited to clinical settings.
[0004] The advancement of wearable technology in recent decades has brought about a paradigm shift. Initially, wearable devices were simple step counters or heart rate monitors. However, as technology evolved, so did the potential applications. Incorporating sensors and advanced computational methods into wearables opened avenues for more sophisticated health and fitness tracking. Despite these advances, a gap existed in providing real-time feedback to users about their movement quality, especially in a manner that's adaptive and personalized.
[0005] Muscle activity, posture, stride, and other motion parameters are crucial for understanding movement quality. Until recently, a comprehensive analysis of these parameters required extensive lab equipment and expert intervention. Outside these settings, individuals often lacked insights into their movement patterns, leading to inefficient practices, potential injuries, or prolonged rehabilitation.
[0006] Moreover, generic feedback, like simple buzzes or beeps, often lacks context, making it hard for users to understand and act upon. There was a pressing need for a system that could seamlessly blend into everyday life, continuously monitor movement, provide contextual feedback, and adapt as the user progresses.
[0007] 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.
[0008] 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
[0009] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00010] The present invention relates to wearable technology focused on enhancing human mobility. Specifically, it pertains to a device equipped with sensors, microprocessors, and feedback mechanisms designed to monitor, analyze, and guide users in their movements for improved mobility and muscle functionality.
[00011] In an embodiment, the wearable device is built around a state-of-the-art sensor array. This array is adept at monitoring intricate aspects of human motion, capturing parameters like stride length, gait, posture, and muscle activity. The incorporation of accelerometers, gyroscopes, and electromyography (EMG) sensors ensures a holistic understanding of the user's movement.
[00012] In an embodiment, the feedback mechanism of the wearable is intricately designed to be intuitive and actionable. Instead of ambiguous signals, users receive haptic feedback that guides them towards optimal motion patterns. For instance, someone unknowingly slouching might feel a gentle nudge or vibration, prompting them to correct their posture.
[00013] In an embodiment, the wearable device is enhanced with wireless communication capabilities. This feature allows the wearable to sync with smartphones, computers, or dedicated platforms. Users can review detailed reports on their movement patterns, receive expert advice remotely, or update the device's firmware, ensuring it remains at the forefront of technological advancements.
[00014] In an embodiment, the device's microprocessor unit stands out due to its machine learning capabilities. By continuously analyzing data from the user, the device adapts its feedback over time. As a result, as users progress in their mobility journey—whether it's rehabilitation, athletic training, or posture correction—the device's guidance evolves, ensuring that the feedback remains relevant and challenging.
[00015] In an embodiment, historical data plays a pivotal role. The wearable comes with a dedicated memory storage unit that maintains records of the user's mobility patterns over extended periods. This longitudinal data allows users to track their progress, observe trends, and even predict potential future challenges or plateaus.
[00016] In an embodiment, modularity is a highlight of the wearable's design. Recognizing that mobility challenges vary from person to person, the device allows users to attach or detach specific components based on their needs. For example, someone focusing on upper body posture might use a different module compared to someone targeting lower body stride optimization.
[00017] In an embodiment, the wearable device's power source is eco-friendly. Relying on a rechargeable battery, the device also incorporates energy-harvesting technologies. As users move, piezoelectric materials within the device convert some of that mechanical energy into electrical energy, thereby prolonging the battery's life and ensuring the device remains operational for longer durations without frequent recharges.
[00018] In an embodiment, user interaction with the device is elevated through an integrated user interface. An LED display provides quick stats, battery status, and feedback summaries. Alternatively, for those who prefer auditory interaction, a voice assistant offers guidance, alerts, and progress updates.
[00019] In an embodiment, the method to utilize the device involves a user-friendly approach. After securing the wearable to the appropriate body part, the device's sensors continuously capture movement and muscle data. This data feeds into the microprocessor unit, where real-time analysis identifies any inefficiencies or areas for improvement. Based on the analysis, the feedback mechanism springs into action, guiding the user to enhance their movement. Over time, as the user progresses, the device stores this mobility data, serving as a repository for subsequent reviews or professional evaluations.
Brief Description of the Drawings
[00020] 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:
[00021] FIG. 1 illustrates a wearable device for improved human mobility, according to some embodiments of the present disclosure.
[00022] FIG. 2 illustrates a method for improving human mobility using the wearable device, in accordance with an embodiment of the present disclosure.
Detailed Description
[00023] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00024] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00025] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00026] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00027] 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.
[00028] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00029] 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.
[00030] The present invention relates to wearable technology focused on enhancing human mobility. Specifically, it pertains to a device equipped with sensors, microprocessors, and feedback mechanisms designed to monitor, analyze, and guide users in their movements for improved mobility and muscle functionality.
[00031] 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.
[00032] FIG. 1 illustrates a wearable device 100 for improved human mobility, according to some embodiments of the present disclosure. The wearable device 100 comprises a sensor array 102, a microprocessor unit 104, a feedback mechanism 106 and a power source 108.
[00033] The core of this device is its sophisticated sensor array, designed meticulously to detect even the subtlest of movement patterns. The sensors not only track the spatial orientation and movement velocity but also delve deep into the intricacies of muscle activity. This provides an in-depth and comprehensive understanding of a user's motion, from the stride of a leg to the flex of an elbow.
[00034] In an embodiment, the sensor array is a blend of accelerometers, gyroscopes, and electromyography (EMG) sensors. Accelerometers measure acceleration forces, determining if the user is stationary or in motion. Gyroscopes measure angular velocity, crucial for understanding rotational movements. On the other hand, EMG sensors monitor electrical activity produced by muscles, offering insights into muscle engagement, strength, and fatigue levels.
[00035] In an embodiment, once the sensor array captures the data, it gets funneled to a cutting-edge microprocessor unit. This unit is not just a mere data repository; it's the brain of the wearable. It constantly analyzes the incoming data, making sense of the myriad movements and muscle activities. Advanced algorithms work tirelessly, discerning patterns, identifying inefficiencies, and isolating anomalies.
[00036] In an embodiment, based on the microprocessor's analysis, the device employs a feedback mechanism. This mechanism is dedicated to assisting the user in real-time, offering guidance on how to move more efficiently, how to correct certain posture defects, or even how to optimize muscle engagement for specific tasks. This feedback could range from gentle vibrations indicating a posture misalignment to more pronounced alerts when the user engages in potentially harmful motions.
[00037] In an embodiment, haptic feedback plays a pivotal role in guiding users. By receiving tactile responses directly on their skin, users can instantly understand and rectify their movement patterns. For instance, if a user is running with a sub-optimal foot strike, a gentle vibration on the heel or toe can alert them to adjust their stride in real-time, preventing potential injuries.
[00038] In an embodiment, the wearable device is not isolated. With wireless communication capabilities, it can synchronize with external platforms, be it a smartphone app, a computer software, or a cloud-based data storage. This allows users to review detailed reports, visualize their movement patterns, and even share this data with professionals, such as physiotherapists or trainers, for expert advice.
[00039] In an embodiment, the device's microprocessor unit isn't static in its functionality. It incorporates machine learning algorithms, allowing the wearable to "learn" from the user. Over time, as the device gathers more data, it fine-tunes its feedback, ensuring that the user always receives the most relevant and personalized guidance. It’s akin to having a personal trainer, one that understands the nuances of the user's mobility and constantly adapts to provide the best support.

[00040] In an embodiment, powering this sophisticated device is an power source. Ensuring that the wearable remains lightweight yet powerful, the power source is designed for longevity and efficiency. Modern battery technologies are employed, but the device also explores energy-harvesting mechanisms. As the user moves, kinetic energy is captured and converted back into electrical energy, supplementing the battery and extending the device's operational duration.
[00041] In an embodiment, modularity is a theme that echoes throughout the device's design. Recognizing the diverse range of human mobility challenges, the wearable is designed such that users can tailor it to their needs. Whether focusing on rehabilitating a specific limb, optimizing athletic performance, or general posture correction, modules can be added or removed to fit the purpose.
[00042] Imagine Sarah, a professional ballet dancer recovering from a knee injury. Sarah wears the device on her injured leg during her rehabilitation exercises. The device's sensors monitor her knee's movement, muscle engagement, and overall leg motion. When Sarah unconsciously moves in a way that might strain her knee, the device's haptic feedback gently vibrates, signaling her to adjust her movement. Over time, as she practices, the device "learns" from her progress, adjusting its feedback to offer more advanced guidance. Through a companion app on her smartphone, Sarah can review her progress, seeing how her mobility has improved over weeks. Moreover, she can share this data with her physiotherapist, who can then provide further specialized guidance.
[00043] In an embodiment, the wearable device is equipped with a sophisticated sensor array that combines accelerometers, gyroscopes, and electromyography (EMG) sensors. This powerful combination enables the device to obtain an intricate understanding of the user's motion and muscle activity. The accelerometers and gyroscopes precisely track movements in various planes, providing data on acceleration, orientation, and rotational motion. Additionally, the EMG sensors monitor muscle activity, capturing subtle muscle contractions and exertions. This holistic sensor setup facilitates a comprehensive and real-time analysis of the user's mobility, allowing the device to provide accurate feedback and guidance for improved movement patterns.
[00044] In an embodiment, the wearable device integrates a feedback mechanism that enriches the user experience through haptic feedback components. These components deliver tactile responses directly to the user's body, creating a nuanced way of guiding them towards desired motion patterns. As the user engages in movements, the haptic feedback system responds by generating vibrations or sensations. These sensations can be programmed to correspond to specific motions, postures, or muscle activations, effectively guiding the user towards optimal movement techniques. This tangible guidance enhances the user's proprioceptive awareness and contributes to the refinement of their mobility skills.
[00045] In an embodiment, the wearable device incorporates a wireless communication module that expands its capabilities beyond standalone functionality. This module allows the device to establish seamless connections with external devices or platforms, unlocking a realm of possibilities. Data synchronization facilitates the transfer of mobility data to compatible devices or cloud-based platforms, enabling comprehensive monitoring and analysis. Additionally, remote monitoring becomes feasible, enabling healthcare professionals, trainers, or caregivers to assess the user's progress from a distance. The wireless communication module also facilitates convenient software updates, ensuring that the device remains up-to-date with the latest enhancements and features.
[00046] In an embodiment, the wearable device exhibits an advanced level of intelligence through its microprocessor unit, which is enhanced with machine learning algorithms. These algorithms enable the device to evolve its functionality over time. By continuously analyzing the user's mobility patterns and progress, the device becomes adept at recognizing individual strengths, weaknesses, and preferences. Leveraging this understanding, the device can dynamically adapt and customize its feedback strategies. This personalized approach optimizes the user's experience, enabling them to embark on a journey of improvement that is uniquely tailored to their specific needs and objectives.
[00047] In an embodiment, the wearable device 1 integrates a memory storage unit that plays a pivotal role in tracking and analyzing user progress. This memory unit securely stores historical mobility data accumulated over time. This wealth of data enables trend analysis, revealing insights into the user's mobility journey. Long-term monitoring becomes possible, as users and healthcare professionals can review historical performance and progress trends. This feature not only provides users with a comprehensive perspective on their achievements but also empowers healthcare practitioners with valuable information for informed decision-making and goal-setting.
[00048] In an embodiment, the wearable device introduces a modular design approach that offers remarkable flexibility and adaptability. The device is ingeniously constructed as a modular system, enabling users to effortlessly attach or detach specific components as their mobility challenges or training needs evolve. This modular design empowers users to tailor the device to their unique requirements, whether it's focusing on specific muscle groups, adapting to varying levels of mobility, or accommodating changing training goals. By allowing component customization, the wearable device maximizes its utility and relevance throughout the user's mobility journey.
[00049] In an embodiment, the wearable device demonstrates an approach to power efficiency. The device is powered by a rechargeable battery that not only ensures consistent operation but also incorporates energy-harvesting technologies. Utilizing materials like piezoelectric elements, the battery can harness energy from the user's movement. As the user engages in motion, such as walking or other physical activities, the piezoelectric materials convert mechanical energy into electrical energy, which is then used to charge the battery. This dynamic power solution effectively extends the battery life, reducing the frequency of recharges and enhancing the device's sustainability.
[00050] In an embodiment, the wearable device includes a user interface, such as an LED display or voice assistant, offering real-time feedback, guidance, and status updates to the user. This interface enhances user-device interaction, conveying movement insights, posture corrections, and progress updates, fostering improved mobility through intuitive communication.
[00051] FIG. 2 illustrates a method 200 for improving human mobility using the wearable device, comprising the steps of; At step 202, the process commences by attaching the wearable device to the user's body in a manner that ensures secure and comfortable placement. The device may be affixed to an appropriate location, such as the user's limb or torso, to effectively capture movement data. At step 204, once the device is attached, it begins its active role in monitoring and capturing the user's movement patterns and muscle activity. The sensor array, which includes accelerometers, gyroscopes, and electromyography (EMG) sensors, collaborates to record a comprehensive dataset of the user's physical movements. This dataset includes information on posture, gait, muscle contractions, and other relevant parameters. At step 206, The wearable device's microprocessor unit takes center stage in this step, utilizing its processing power and potentially integrated machine learning algorithms. The unit analyzes the captured data with a keen focus on identifying mobility issues, inefficiencies, or anomalies. By comparing the user's movements to established norms or predefined profiles, the device pinpoints areas where mobility can be enhanced, such as posture corrections or muscle engagement improvements. At step 208, building upon the insights gleaned from data analysis, the device actively engages its feedback mechanism to facilitate real-time intervention. This mechanism, which may involve haptic feedback components or other modes of communication, delivers targeted guidance to the user. For instance, if the device detects suboptimal posture during walking, it may provide gentle vibrations or tactile cues that prompt the user to make adjustments. Similarly, if muscle engagement is uneven, the device may offer cues that encourage balanced muscle activation. At step 210, throughout the user's engagement with the wearable device, mobility data is diligently stored within the memory storage unit. This unit maintains a historical record of the user's movement patterns, improvements, and overall progress over time. By accumulating this data, the device enables subsequent analysis and in-depth progress tracking. This information can be pivotal for individuals seeking to monitor their mobility journey, healthcare professionals aiming to assess rehabilitation or training outcomes, or researchers studying movement-related trends.The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.
[00052] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.
[00053] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00054] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.

Claims
I/We Claim:
Claim 1:
A wearable device for improved human mobility, comprising:
a sensor array configured to detect and monitor the user's movement patterns and muscle activity;
a microprocessor unit for analyzing data from the sensor array;
a feedback mechanism designed to assist, correct, or enhance user mobility based on the analysis; and
a power source to energize the components of the wearable device.
Claim 2:
The wearable device of claim 1, wherein the sensor array includes accelerometers, gyroscopes, and electromyography (EMG) sensors to capture a comprehensive understanding of user motion and muscle activity.
Claim 3:
The wearable device of claim 1, wherein the feedback mechanism incorporates haptic feedback components that provide tactile responses to the user, guiding them towards desired motion patterns.
Claim 4:
The wearable device of claim 1, further comprising a wireless communication module, enabling the device to synchronize with external devices or platforms for data transfer, remote monitoring, or software updates.
Claim 5:
The wearable device of claim 1, wherein the microprocessor unit incorporates machine learning algorithms, allowing the device to adapt and customize its feedback based on the individual user's mobility patterns and progress over time.
Claim 6:
The wearable device of claim 1, further including a memory storage unit, which stores historical mobility data, allowing for trend analysis and long-term monitoring of user progress.
Claim 7:
The wearable device of claim 1, designed as a modular system, enabling users to attach or detach specific components based on their specific mobility challenges or training needs.
Claim 8:
The wearable device of claim 1, wherein the power source is a rechargeable battery equipped with energy-harvesting technologies, such as piezoelectric materials, to extend battery life based on user movement.
Claim 9:
The wearable device of claim 1, further comprising a user interface, such as an LED display or a voice assistant, providing real-time feedback, guidance, or status updates to the user.
Claim 10:
A method for improving human mobility using the wearable device, comprising the steps of:
attaching the device to the user;
utilizing the sensor array to continuously monitor and capture the user's movement patterns and muscle activity;
analyzing the captured data with the microprocessor unit to identify mobility issues or inefficiencies;
employing the feedback mechanism to guide or assist the user in real-time to correct or enhance mobility; and
storing mobility data in the memory storage unit for subsequent analysis or progress tracking.

WEARABLE DEVICE FOR IMPROVED HUMAN MOBILITY
Abstract
The invention introduces a wearable device explicitly designed for improving human mobility. Equipped with a diverse sensor array, this device can detect and monitor user movement patterns and muscle activity in real-time. A microprocessor unit, incorporating advanced algorithms, analyzes the sensor data, enabling the device to provide instant feedback aimed at assisting, correcting, or enhancing the user's mobility. This feedback is tailored to the individual, ensuring personalized guidance. The device also includes features like wireless communication for data synchronization, memory storage for monitoring long-term progress, and a power source ensuring longevity. Whether you're an athlete optimizing your performance, a patient in rehabilitation, or someone keen on improving posture and stride, this wearable offers a comprehensive solution for enhanced mobility. , C , Claims:Claims
I/We Claim:
Claim 1:
A wearable device for improved human mobility, comprising:
a sensor array configured to detect and monitor the user's movement patterns and muscle activity;
a microprocessor unit for analyzing data from the sensor array;
a feedback mechanism designed to assist, correct, or enhance user mobility based on the analysis; and
a power source to energize the components of the wearable device.
Claim 2:
The wearable device of claim 1, wherein the sensor array includes accelerometers, gyroscopes, and electromyography (EMG) sensors to capture a comprehensive understanding of user motion and muscle activity.
Claim 3:
The wearable device of claim 1, wherein the feedback mechanism incorporates haptic feedback components that provide tactile responses to the user, guiding them towards desired motion patterns.
Claim 4:
The wearable device of claim 1, further comprising a wireless communication module, enabling the device to synchronize with external devices or platforms for data transfer, remote monitoring, or software updates.
Claim 5:
The wearable device of claim 1, wherein the microprocessor unit incorporates machine learning algorithms, allowing the device to adapt and customize its feedback based on the individual user's mobility patterns and progress over time.
Claim 6:
The wearable device of claim 1, further including a memory storage unit, which stores historical mobility data, allowing for trend analysis and long-term monitoring of user progress.
Claim 7:
The wearable device of claim 1, designed as a modular system, enabling users to attach or detach specific components based on their specific mobility challenges or training needs.
Claim 8:
The wearable device of claim 1, wherein the power source is a rechargeable battery equipped with energy-harvesting technologies, such as piezoelectric materials, to extend battery life based on user movement.
Claim 9:
The wearable device of claim 1, further comprising a user interface, such as an LED display or a voice assistant, providing real-time feedback, guidance, or status updates to the user.
Claim 10:
A method for improving human mobility using the wearable device, comprising the steps of:
attaching the device to the user;
utilizing the sensor array to continuously monitor and capture the user's movement patterns and muscle activity;
analyzing the captured data with the microprocessor unit to identify mobility issues or inefficiencies;
employing the feedback mechanism to guide or assist the user in real-time to correct or enhance mobility; and
storing mobility data in the memory storage unit for subsequent analysis or progress tracking.