1. Edge node and deep learning based scalable system for monitoring of rehabilitation player system is comprises with rehabilitation node and edge node-based hybrid system for monitoring of rehabilitation player.

2. The system is claimed in claim 1, it consists edge node and cloud server architecture for scalable system in rehabilitation centre.

3. The system is claimed in claim 1, it collects information from injured player easily and display it on web dashboard.

Description:Title of The Invention
Edge Node and Deep Learning Based Scalable System for Monitoring of Rehabilitation Player
Field of the Invention
This invention relates to edge node and deep learning based scalable system for monitoring of rehabilitation player.
Background of the Invention
US16/169520: A sports training and guidance platform network which intertwine various wearable is provided. The network includes at least one wearable containing one biosensor worn on the body, and input from a data stream, analytic and accessed on mobile devices. According to this aspect, the wearable network will measure and compare individual or team performance with various biosensor data, and motion i.e., accelerometer, gyroscopes. A sports training toolkit which includes the wearable communication network and platform and system is also provided.
KR1020167034652A: Apparatuses, systems, and methods are provided for providing substantially continuous biometric identification (CBID) for an individual using eye signals in real time. The device is contained within a wearable computing device, wherein the device is based on iris recognition using one or more cameras directed towards one eye or two eyes and/or other physiological, anatomical and/or behavioral measurements. On the basis, identification of the device wearer is performed. Verification of the device user identity can be used to enable or disable the display of security information. Further, identity verification may be included in information transmitted from the device to determine appropriate security measures by remote processing units. The device is a wearable computing device that performs other functions including field of view correction, a head-mounted display, viewing the surrounding environment using the scene camera(s), recording audio data via a microphone, and/or other sensing equipment can be integrated.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is relates to edge node and deep learning based scalable system for monitoring of rehabilitation player. The rehabilitation Node (1) and (2) is used to gather information of the injured player and transmit it to edge mode.The edge mode with the assistance of Wi-Fi transferred the data obtained from Node(1) and Node(2) to cloud server and web dashboard. We used EMG sensor (electromyography), EMC (Electronically), Optical, Electrical, and Gyroscope sensors.The computing unit with the help of battery power supply and keypad sends the information to display unit and speaker.

SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The present invention relates to edge node and deep learning based scalable system for monitoring of rehabilitation player. Sport biomechanics represents an important research field aimed at analyzing sport movements in order to quantitatively evaluate athlete performance, offer useful tools and guidelines for coaches to apply during athlete training and prevent or minimize the risk of injury. Recent technological innovations allow the performance of movement analysis during sporting activities thanks to the compact wearable sensors that do not influence the technical movements of athletes. The present study deals with AI and Cloud assisted system rehabilitation of in sports.The rehabilitation Node (1) and (2) is used to gather information of the injured player and transmit it to edge mode.The edge mode with the assistance of Wi-Fi transferred the data obtained from Node(1) and Node(2) to cloud server and web dashboard. We used EMG sensor (electromyography), EMC (Electronically), Optical, Electrical, and Gyroscope sensors.The computing unit with the help of battery power supply and keypad sends the information to display unit and speaker.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1 illustrates the proposed rehabilitation centre using AI (Artificial intelligence) and cloud assisted system for rehabilitation center for injury in sports. We use Rehabilitation node (1) and Rehabilitation node (2) which is connected to the Wi-Fi and sends information to the Edge Node (20). The cloud server and Web Dashboard collects the information and inform us about the injury of a player or athlete.
Figure 2 illustrates the rehabilitation Node (10-11) contains several sensors that is EMG sensor, ECG sensors, Optical sensors, Chemical sensor, Gyroscope sensor, Chemical sensor and proximity sensors. These sensors transmit the data to Computing unit (56) with the help of Wi-Fi module (51), Keypad (52) and Battery power supply. The Display Unit (54) and speaker is used to illustrate the information. The CO processor (62) and Deep Learning Node (61) process the information to the computing unit.
Figure 3 illustrates edge node, where it comprises of the computing Unit (60) Buzzers the information with the help of battery power supply (64) put back the information to edge Node (20) with the help of Wireless Fidelity. The deep learning model with the co-processor helps to detect the performance of rehabilitation player. The updated status of rehabilitation player is logged on the rehabilitation node and cloud server.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
This invention relates to edge node and deep learning based scalable system for monitoring of rehabilitation player. Sport biomechanics represents an important research field aimed at analyzing sport movements in order to quantitatively evaluate athlete performance, offer useful tools and guidelines for coaches to apply during athlete training and prevent or minimize the risk of injury. Recent technological innovations allow the performance of movement analysis during sporting activities thanks to the compact wearable sensors that do not influence the technical movements of athletes. The present study deals with AI and Cloud assisted system rehabilitation of in sports.The rehabilitation Node (1) and (2) is used to gather information of the injured player and transmit it to edge mode.The edge mode with the assistance of Wi-Fi transferred the data obtained from Node(1) and Node(2) to cloud server and web dashboard. We used EMG sensor (electromyography), EMC (Electronically), Optical, Electrical, and Gyroscope sensors.The computing unit with the help of battery power supply and keypad sends the information to display unit and speaker.
ADVANTAGES OF THE INVENTION:
• It collects information from injured player easily and display it on web dashboard.
• Use of sensors to Detect the information effortlessly.
• Use of Wi-Fi makes the system run steadfastly.
• Collect vague injury of the player such as internal bleeding etc.
, Claims:We Claim:
1. Edge node and deep learning based scalable system for monitoring of rehabilitation player system is comprises with rehabilitation node and edge node-based hybrid system for monitoring of rehabilitation player.
2. The system is claimed in claim 1, it consists edge node and cloud server architecture for scalable system in rehabilitation centre.
3. The system is claimed in claim 1, it collects information from injured player easily and display it on web dashboard.