Wearable-AI Platform for Supporting and Assessing Teamwork in Sport Environments

This application claims benefit to U.S. Provisional Application No. 63/652,971, filed May 29, 2024, entitled “Wearable-AI Platform for Supporting and Assessing Teamwork in Sport Environments” hereby incorporated by reference in its entirety.

This disclosure relates to the field of enhancing teamwork in sports environments through the use of innovative wearable technology.

Current sports technologies enable tracking and improving individual metrics such as speed, agility, and precision, but they fall short in nurturing the team skills that are equally critical for better game performance and holistic development of young athletes. This oversight results in an unmet need to enhance essential team and life skills such as teamwork, communication, empathy, and leadership among youth participating in sports programs.

Traditional wearable fitness and sports technologies primarily emphasize biometric and movement-related data (e.g., heart rate monitors, GPS trackers, and accelerometers). While such tools are valuable for measuring individual performance, they are ill-equipped to capture the complex and dynamic interactions that define effective team play. Team dynamics are influenced by a blend of real-time communication, spatial awareness, and collaborative decision-making, elements often overlooked in current wearable designs.

Coaches and team leaders lack access to real-time feedback tools capable of providing actionable insight into how players interact and communicate during game play. Most post-game evaluations rely on video review or anecdotal assessments, which are retrospective, time-consuming, and subject to bias. There is a need for a solution that provides objective, immediate, and behavior-reinforcing feedback to enhance teamwork as it unfolds on the field.

Furthermore, there exists a gap in integrating artificial intelligence (AI) and machine learning into real-time feedback systems for team sports, especially in a wearable format. AI can provide continuous learning and performance adjustment based on data patterns, making it highly suitable for fostering communication habits and social dynamics in sports settings.

The systems and methods disclosed herein address these unmet needs by leveraging wearable audio collection, AI processing, and multi-modal feedback mechanisms to provide a groundbreaking platform for training, assessing, and enhancing communication and collaboration in team-based sports environments.

This Summary introduces a selection of concepts relating to this technology in a simplified form as a prelude to the Detailed Description that follows. This Summary is not intended to identify key or essential features.

In some aspects, a method for improving sports player communication habits is disclosed that may include the steps of collecting, via an audio collection mechanism affixed to a wearable jersey of a player, audio communications of the player, analyzing, via a processor affixed to the wearable jersey, the audio communications of the player, and displaying, via an LED matrix affixed to the wearable jersey, a visual reinforcement signal based upon the collected player audio data, wherein the visual reinforcement signal permits the player to keep track of communication efforts.

In some examples, the audio communications may be processed using natural language processing (NLP) to detect keywords or behavioral markers. In other examples, the processor may further determine a score or a metric based on a frequency or a quality of communication. In another example, the visual reinforcement signal can vary in color or intensity based on the score or the metric. In still other examples, the method may include transmitting analyzed audio communications of the player to a central server for aggregation and team-level analysis. In yet another example, the wearable jersey can include a vibration actuator configured to provide haptic feedback in response to communication analysis. In one example, the LED matrix may display symbols or coded icons indicative of communication types, and the communication types may be directive, collaborative, and/or supportive. In certain examples, the method may include the step of storing communication data in a user profile. In another example, the method may include the step of identifying a lack of audio communication and transmitting corrective prompts.

In other aspects, a system for improving sports player communication habits may include a process, a wearable jersey, wherein the processor is affixed to the wearable jersey, an audio collection mechanism is affixed to the wearable jersey and configured to collect player audio data, wherein the audio collection mechanism is affixed to the wearable jersey, an LED matrix affixed to the wearable jersey and configured to provide players a visual reinforcement signal based upon the collected player audio data, wherein the visual reinforcement signal permits the player to keep track of communication efforts.

In some examples, a processor may be embedded in the wearable jersey and is configured to analyze the collected player audio data. In other examples, the system may include a vibration actuator affixed to the wearable jersey for providing tactile feedback based on the analyzed collected player audio data. In other examples, the LED matrix may be configured to visually differentiate between types of audio communication, and the audio communication types may be directive, collaborative, and/or supportive. In yet another example, the wearable jersey may include a wireless communication module configured to send and receive data from a central server. In certain examples, the system may include a battery module integrated into the jersey for powering the audio collection mechanism and the LED matrix. In one example, the audio communication collection mechanism may include a microphone configured to isolate a player voice from background noise. In yet other examples, the LED matrix may provide a real-time visualization of communication patterns using color-coded signals.

These and other features, advantages, and objects of the present disclosure will be further understood and appreciated by those skilled in the art by reference to the following specification, claims, and appended drawings, where various embodiments of the design illustrate how concepts of this disclosure may be used.

In the following description of the various embodiments, reference is made to the accompanying drawings identified above and which form a part hereof, and in which is shown by way of illustration various embodiments in which features described herein may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope described herein. Various features are capable of other embodiments and of being practiced or being carried out in various different ways.

As disclosed herein, an advanced system designed specifically for enhancing teamwork in sports environments through the use of innovative wearable technology. This system consists of state-of-the-art wearable devices equipped with a range of sensors (audio and location) and actuators. These wearables are designed to be worn by athletes and are interconnected via a centralized, server-based web application that performs sophisticated data processing and analysis. The core of each wearable device is a custom printed circuit board (PCB) engineered to gather critical data, including audio communications and precise location information. A distinctive feature of the technology is the integration of haptic feedback mechanisms, speakers, and LEDs displays within the wearables, providing real-time, actionable feedback to athletes. This feedback is crucial for improving communication, coordination, collaboration, and overall performance during sports activities. Moreover, the wearables have the capability to communicate with each other, facilitating a seamless exchange of data that is essential for completing specific team-based activities. At the heart of the system's data processing capabilities are advanced AI algorithms, hosted on the central web application. These algorithms are adept at analyzing the collected data to generate invaluable insights into team dynamics, communication patterns, and spatial strategies. These insights are then made accessible to coaches, players, and sports analysts through a user-friendly application interface, thereby enhancing strategic decision-making and team coordination to unprecedented levels. The system sets a new benchmark in sports technology by fostering an environment of continuous improvement for sports teams. By offering a unique blend of real-time feedback, inter-device communication, and deep data analysis, the invention holds the promise to improve the team sports experience, making it more fun, interactive, analytical, and performance-driven.

An amount of talk as a team can be measured by tracking when and how much each player speaks, with findings suggesting that higher communication levels improve task cohesion and help teams reach their goals. Asking for help and providing help is another crucial indicator, assessed through sentiment analysis, which shows that supportive behaviors like these enhance adaptability and performance. The concept of confrontation is explored by using sentiment analysis to identify conflict situations. Understanding and resolving such sentiments are linked to stronger team cohesion. Diversity of talk may also measure who talks to whom and how often. Inclusive communication, where many team members participate, is associated with a shared understanding and stronger commitment to team objectives. Moreover, critical points and phases during a game when the most talking occurs may involve analyzing time-stamped audio to detect critical communication periods. This helps researchers understand how teams coordinate during pivotal moments, a concept well-supported in sports psychology literature. Accordingly, the systems and methods disclosed herein provide a solution to the long-felt need to improve player performance through the analyzing and improvement of real-time communication skills between teammates.

As shown in, the systems and methods may include a development PCB including a Bluetooth 5.0 wireless capability. Sensors may be included for audio collection, LEDs, buzzer or speakers, and haptic actuators for real time feedback. A central server may receive audio data, processes it, and transmit feedback to the development PCB. The system and method may include the steps of establishing communication between the central server and the PCBs. As shown in, the PCB may be the size of a dime or a quarter. Communication among PCBs may be selected between a first mode and a second mode, mode 1 and mode 2. As shown in, a user interface may consist of multiple graphical user interfaces (GUIs).

Mode 1 focuses on processing audio data and reinforcing a behavior (e.g., communication, collaboration) that coaches want for their teams. Mode 2 sets up a network between wearables. It focuses on spacing and timing between players in different sports environments. This is based on foundational principles of team sports in which coaches want to use space effectively. For example, as shown in, communication between players is limited with most players lacking desired communications, but improves with reinforcement by the AI Teams system.

In mode 1, the central server and PCBs collect communication data. Testing was conducted on jerseys, armbands, headbands, leg pants, chest, and stomach/waist area for desired areas to place PCBs and peripherals. The PCB is configured/programmed to collect audio data as shown in. A PDM (Pulse Density Modulation) Microphone, for example, which can receive audio data in real-time was used. The PDM may be incorporated or built into various microcontrollers. As shown in, received audio data is converted into the right frequency for speaker verification and identification by an AI API (Artificial Intelligence Application Programming Interface). After processing by the API, as shown in, a visual and/or audio signal(s) is transmitted to the speaker for reinforcement of communication behaviors.

Reinforcement of behaviors may include determining and display of visual feedback and/or haptic feedback and/or sound patterns to signal to users that they are completing the game/activity correctly and/or incorrectly. Upon receipt of the data from the API, a pattern may be deployed and transmitted to the PCB. Once the PCB receives a signal or transmission, the respective pattern may be displayed as shown in.

As shown in, audio data collected during activities may be used by the AI API to detect sentiment as shown in. As noted above, sentiment analysis may be used to highlight supportive behaviors to enhance adaptability and performance. As shown in, the AI API may generate data providing insight into player communication patterns.

In mode 2, the ideal distances between players in a network can be determined to determine the appropriate distance each PCB should be within a distance of others. E.g., No more than 3 seconds within a distance of one PCB to the other for a basketball game. Based upon those parameters, the PCBs can process Bluetooth signals from other PCBs and do so within a set timeframe. If the spacing and timing meet set rules, feedback (e.g., sound, visual, or haptic) can be sent between PCBs. Use existing machine learning and natural language processing algorithms to derive metrics about the communication and teamwork patterns on court may be used to identify communication patterns and provide feedback. Metrics may include Amount of Talk as a Team, Asking for Help, Providing Help, Confrontation Diversity of Talk, Points During the Game, and When the Most Talking is Happening. As discussed above, the dashboard GUIs (see) permit users to select different team games/activities, connect PCBs to the server, display metrics during games, and create players profiles. This dashboard serves as the central hub for selecting team activities, connecting PCBs, displaying real-time metrics during games, and creating player profiles, thus providing a comprehensive tool for enhancing team performance and strategy on the court.

The software used in the systems and methods disclosed herein is engineered to enhance team dynamics in sports through two innovative communication modes between wearable devices and a central server. Mode 1 capitalizes on audio data processing to foster desirable team behaviors such as effective communication and collaboration, as specified by coaches. It involves the strategic placement of development PCBs across various body locations-jerseys, armbands, headbands, and more—to collect audio via PDM microphones integrated into microcontrollers. This data is then processed for speaker verification, and subsequent reinforcement signals are provided through visual, haptic, or auditory feedback. Mode 2 creates an interactive network among the wearables, focusing on the critical use of spacing and timing, vital in team sports for strategic gameplay. This mode sets rules for proximity and timing, leveraging Bluetooth signals within set distances and timeframes. Adherence or deviation from these rule triggers feedback between the PCBs, aiding in real-time spatial strategy development. The software's metrics system utilizes advanced machine learning and natural language processing algorithms to provide quantifiable insights into team communication dynamics.

The systems and methods disclosed herein may collect real time audio data from athletes in sports spaces, provides reinforcement of behaviors based on audio data, provide metrics over time enabling users to track improvements, provide team building activities specifically tailored to enhance teamwork, collaboration, and team cohesion, and address foundational principles of team sports in spacing and timing. Moreover, the systems and methods disclosed herein may be adapted for Team Performance Enhancement: Real-time performance analytics to identify strengths and weaknesses. Customized feedback for players to improve specific skills. Strategy adjustments during games based on live data analysis.

Coaching Tools: provides detailed insights for coaches on team dynamics and player contributions. Automated suggestions for training drills based on team performance data. Longitudinal tracking of player development over seasons.

Player Development: Personalized feedback for players to foster individual growth. Identification of areas requiring improvement through data analytics. Encouragement of leadership and communication skills among young athletes.

Educational Applications: Teaching teamwork and collaboration skills in an educational setting. Integration with physical education curriculums to promote active learning. Data-driven insights to support research in sports science and education.

Socialization and Inclusion: Facilitating inclusive play by ensuring all voices are heard and valued. Promoting social interactions and friendships through team activities. Supporting team bonding and cooperative learning experiences.

Esports and Virtual Teams: Adapting the platform for esports teams to enhance teamwork and communication. Tracking virtual interactions and performance in online games. Encouraging physical activity and team dynamics in virtual environments.

Corporate Team Building: Implementing the platform in corporate settings for team-building activities. Enhancing communication and collaboration among employees. Providing a fun and engaging way to improve workplace relationships.

Value for Team Performance and Social Skill Development: The activities in the systems and methods disclosed herein are meant to be inclusive and adaptable, ensuring that every participant, regardless of skill level, can contribute to and benefit from the team-building process. Additionally, for the first time, coaches and youth programs would be able to use this data to tailor their team-level approaches, focusing on reinforcing positive behaviors and addressing any gaps in teamwork and collaboration. Finally, this technology enables stakeholders to gain a deeper understanding of team dynamics, allowing for personalized feedback to athletes and teams. The system and methods identify strengths to be leveraged and areas needing development, thereby fostering a supportive and growth-oriented team environment.

The systems and methods disclosed herein may include a development board like XIAO nRF52840 Sense that is equipped with Bluetooth 5.0 wireless capability, a PDM (Pulse Density Modulation) microphone. The PCB may include a LED matrix, buzzer (or speaker), and a vibration actuator. PCBs facilitate audio data collection, basic player recognition, and LED feedback mechanisms. Again, the enhanced AI algorithms may be used for data analysis, extending communication metrics, improving player recognition accuracy, and integrating comprehensive real-time feedback mechanisms (including speaker and haptic feedback). An integrated network among wearable devices may be used for detailed location indoors and/or outdoors.

An example systemfor improving sports player communication habits is shown in. The systemincludes one or more networked players(individual players-), user interface, an application/module, a database store, an API provider, and an inference engine. Each component may be implemented by one or more computing devices, which may communicate via a network using any suitable communication protocols, including TCP/IP, FTP, HTTP, GSM, LTE, WiFi, or others. The networked playersmay have wearable devices, edge sensors, or other input components capable of transmitting and receiving data. These players communicate bi-directionally with the application, which serves as a central control unit within the system. Communication between networked players, the user interface/dashboardand applicationmay be enabled using short-range communication protocols, including Bluetooth Low Energy (BLE).

The applicationreceives data from the networked players, processes that data, and interacts with the user interfaceto display results to users. The user interfacemay present a variety of interactive elements such as performance metrics, game features, and profile management tools. The interface may be rendered on a mobile device, tablet, desktop computer, or wearable display, and may include graphical, audio, or textual outputs. The applicationalso exchanges audio data with the API provider. The API providermay supply external analytics functionality, voice recognition services, or other third-party API capabilities. Output from the API providermay be processed by the inference engine, which is configured to perform analysis on audio streams, behavioral patterns, sentiment, and/or real-time sensor data. Inference results may be passed back to the applicationfor use in feedback, adjustments, or adaptive functionality.

The applicationfurther communicates with the databaseto store, retrieve, and update persistent records. The database storemay be implemented using any combination of relational databases, NoSQL data systems, distributed databases, or in-memory storage platforms. The stored data may include metrics, logs, user profiles, and historical inferences generated during operation. Secure data transmission across the systemmay be implemented using protocols such as SFTP, TLS, or HTTPS. In some embodiments, data may be protected in transit and at rest using encryption techniques such as PGP. Integration between components may occur through service-based APIs, file-based exchange mechanisms, or messaging protocols. Optionally, secure network appliances or middleware layers may be employed between external devices and internal modules for enhanced security and performance.

Each computing device in systemmay include one or more processors, RAM, ROM, I/O interfaces, and communication hardware. These devices may support various input methods including touch, audio, stylus, or gesture-based commands. Software components stored in memory may include operating systems, application logic, and local databases. Caching systems such as CPU caches, memory caches, or database caches may be employed to reduce latency and improve responsiveness. The architecture illustrated insupports a modular, scalable platform for real-time data exchange, multimodal user interaction, and automated inference processing. Such a configuration may be adapted to a variety of use cases, including sports analytics, training systems, behavioral monitoring, or real-time team collaboration environments.

The systems and methods disclosed herein are superior and novel to existing systems for the capabilities to collect real time audio data from athletes in sports spaces, provide reinforcement of behaviors based on audio data, provide metrics over time enabling users to track improvements, provide team building activities specifically tailored to enhance teamwork, collaboration, and team cohesion, and address foundational principles of team sports in spacing and timing.

The foregoing has been presented for purposes of example. The foregoing is not intended to be exhaustive or to limit features to the precise form disclosed. The examples discussed herein were chosen and described in order to explain principles and the nature of various examples and their practical application to enable one skilled in the art to use these and other implementations with various modifications as are suited to the particular use contemplated. The scope of this disclosure encompasses, but is not limited to, any and all combinations, subcombinations, and permutations of structure, operations, and/or other features described herein and in the accompanying drawing figures.

Although examples are described above, features and/or steps of those examples may be combined, divided, omitted, rearranged, revised, and/or augmented in any desired manner. Various alterations, modifications, and improvements will, in view of the foregoing disclosure, readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this description, though not expressly stated herein, and are intended to be within the spirit and scope of the disclosure. Accordingly, the foregoing description is by way of example only, and is not limiting.