We Claim:
1. A system for analysing sports and improving performance by providing feedback in near real time without any visual data capture mechanisms, the system comprising: a monitoring device, wherein the monitoring device comprises:
a plurality of sensors for detecting a plurality of data points from a plurality of actions and poses comprised in a physical motion of a player, wherein the plurality of data points or parameters captured by the plurality of sensors includes a motion of the player, a speed at which the player hits a ball, a linear motion, an angular motion, a direction of a shot, an amount of pressure put on the ball by a bat, quaternions, linear acceleration in three axes, angular velocity, a plurality of rotational parameters including roll, pitch and yaw values, and a plurality of physiological features of the player, and wherein the plurality of sensors includes motion sensors, temperature sensors, pressure sensors, position sensors, proximity sensors, speed sensors, inertial measurement sensors, an audio sensor, a pyroelectric sensor, and a piezoelectric sensor, and wherein the monitoring device is configured to synchronize the plurality of sensors to generate a synchronized output data from the plurality of data points captured by the plurality of sensors; and
a communication module; a remote server, wherein the remote server comprises a filtering and signal processing module, a database, an analytics module, a machine learning module, an artificial intelligence module, and a gaming engine, and wherein the analytics module is configured to analyze output signals from the filtering and signal processing module based on preset rules and predetermined techniques to compare an action and a performance of the player with a reference template of best practices or ways for performing the physical motion, or an action, or a pose to provide a feedback to the player for improving the performance of the player,

and wherein the database is configured to store analysis data for future reference, retrieval data, meta-data, and information related to a plurality of physical motions of a plurality of players, and wherein the artificial intelligence module is configured to process the stored analysis data, the retrieval data, the meta-data, and the information related to the plurality of physical motions of the plurality of players to provide a predictive analytics on any one of the physical motion, the action, and the pose of the player at a plurality of levels, and wherein the artificial intelligence module is configured to contextually determine and identify noise from useful information, and wherein the gaming engine is configured to perform post-noise filtering and error correction in motion data received from the inertial measurement sensors to recreate motion in a three-dimensional (3D) visual space using a plurality of data points measured with the inertial measurement sensors, and wherein the gaming engine is configured to derive linear motion using the linear acceleration in three axes, and wherein the gaming engine is configured to derive angular motion using the quaternions and the plurality of rotational parameters;
a microphone module provided on the bat used by the player, and wherein the microphone module is connected to the communication module of the monitoring device, and wherein the microphone module is configured to transmit audio information related to an impact of the bat with other player accessories to identify an accurate impact of the bat with the ball and assist in triangulating an exact impact time during a swing of the bat, and wherein the microphone module is configured to distinguish an actual action played with the bat and an unwanted action of the bat and the player;
a plurality of end-point computing devices, wherein the plurality of end-point computing devices comprise a plurality of portable computing devices located remotely;
a communication network; and

a gateway device;
wherein the remote server is configured to receive the synchronized output data from the plurality of sensors to generate a visual simulation of the player and the actions of the player captured by the monitoring device, in near real time, without using any video recording equipment, and wherein the remote server is configured to provide a simulation of the physical motion captured through the monitoring device in the plurality of end-point computing devices, wherein the filtering and signal processing module is configured to filter unwanted noise signals received from the plurality of sensors in the monitoring device, wherein the plurality of sensors in the monitoring device is communicatively connected through the communication network to synchronize a capturing of information or data or an event or a physical motion, and wherein the plurality of sensors is configured to capture the information of the event simultaneously, and wherein the plurality of sensors is configured to recreate the event from synchronously captured information even when one of the plurality of sensors has failed to capture the event, and wherein one of the plurality of sensors on the bat of the player is a hub for rest of the plurality of sensors, and wherein the one of the plurality of sensors on the bat is configured to detect the impact of the ball on the bat to trigger and activate all the plurality of sensors to start a recording operation of the actions of the player, and wherein the time of impact is a reference point for starting the recording operation of the actions of the player.
2. The system according to claim 1, wherein the monitoring device comprises a gyroscope, an accelerometer, a magnetometer, a compass, motion sensors, temperature sensors, pressure sensors, position sensors, proximity sensors, speed sensors, audio sensor, a pyroelectric sensor, a piezoelectric sensor, a

communication module, a microcontroller, a memory, and a battery power supply.
3. The system according to claim 1, wherein the monitoring device comprises a communication circuit for transmitting the detected data points to the remote server or the plurality of end-point computing devices, and wherein the plurality of sensors in the monitoring device is placed in the vicinity of the player, or mounted or embedded in any one of the attires and the equipment used by the player in performing the plurality of physical actions or poses, and wherein the monitoring device is configured to filter noise signals from the synchronized output data received from the plurality of sensors, and wherein the monitoring device is configured to detect data points and process the output signals to derive a pattern.
4. The system according to claim 1, wherein the gaming engine is provided at each of the plurality of end-point computing devices, and wherein the gaming engine is configured to receive motion data from accelerometer, gyroscope and magnetometer modules, and wherein the gaming engine is configured to enable post-noise filtering and error correction in a measured data, and wherein the motion is recreated in the 3D visual space using a plurality of data points measured with the inertial measurement sensors.
5. The system according to claim 1, wherein the monitoring device is placed in a plurality of locations on a sports gear and the attire of the player, and wherein the number of monitoring devices and a configuration of the monitoring devices are determined by a plurality of parameters, and wherein the plurality of parameters includes a location and a placement of the monitoring device on sports equipment, an impact threshold, and a visual context, and wherein the location and the placement of the monitoring device on the sports equipment are

determined based on an axis orientation to be identified, three-axis of the monitoring device to be mapped, and a speed range of motion of the sports equipment to be estimated, and wherein a point of impact is captured using the impact threshold based on a type of sport and a type of sport activity, and wherein the parameters for determination of the impact include acceleration and a rate of change of the acceleration, and wherein the parameters for the visual context are configured with respect to the type of sport activity, and wherein the sport activity is any one of cricket, hockey, baseball, tennis, and table tennis.
6. The system according to claim 1, wherein the analytics module is configured to analyze the synchronized output data from the monitoring device, and wherein the analytics module is configured to contextually render the synchronized output data from the monitoring device to the plurality of end-point computing devices based on the preset rules, and wherein the end-point computing devices are configured to access the remote server to access any data stored in the remote server.
7. The system according to claim 1, wherein the analytics module is configured to analyze the synchronized output data from the monitoring device in a plurality of end-point computing devices based on preset parameters, wherein the preset parameters for analysis include a position of the bat, the direction of the shot, a type of the shot, a swing analysis, a shot analysis, a direction analysis, a pressure analysis, an audio analysis, a pattern determination, a comparative analysis, a virtual replay of a game, and a mechanics of the game.
8. The system according to claim 1, wherein the analytics module is configured to provide a comparison of an action of the player with a reference action, and wherein the analytics module is configured to provide a guidance on necessary changes and/or improvements needed to reach to a level of the reference action.

9. The system according to claim 1, wherein the analytics module is configured to combine an analysis of each shot to derive an analysis report for each delivery, shot, game, session, and player, and wherein the analytics module is configured to combine a plurality of game patterns of the player to derive optimum factors or parameters for the player, and wherein the optimum factors or parameters for the player include a body dynamics of the player relative to a position of the bat at the time of impact for increasing a game performance.
10. The system according to claim 1, wherein the machine learning module is configured to identify patterns from observed data and provide contextual suggestions on the plurality of end-point computing devices.
11. The system according to claim 1, wherein the communication network comprises an Internet, an intranet, a radio-frequency network, a telephonic network, a local area network (LAN), a wide area network (WAN), and a proximity network including Bluetooth, near-field communication (NFC), Wi-Fi, ZigBee, and Bluetooth Low Energy (BLE).
12. The system according to claim 1, wherein the monitoring device, the remote server, and the plurality of end-point computing devices are connected through the communication network for establishing a wireless communication.
13. The system according to claim 1, wherein the gateway device is configured to enable the plurality of end-point computing devices to interface with the remote server, and wherein the gateway device is configured to communicate with the remote server and the plurality of end-point computing devices through a plurality of communication protocols.
14. The system according to claim 1, wherein the artificial intelligence module is configured to automatically identify a type of the shot.

15. The system according to claim 1, wherein the artificial intelligence module is configured to generate an automated audio and text commentary of the physical motion, the actions, and the poses that are captured.
16. The system according to claim 1, wherein the artificial intelligence module is configured to learn from stored previous data or past data of historical data of the player to provide the predictive analytics at the plurality of levels, and wherein the plurality of levels includes a player level analytics, a game level analytics, and a match level analytics, and wherein the artificial intelligence module is configured to predict common mistakes or errors made by the player based on an analysis of past or previous games to provide a recommendation to help the player to overcome or avoid the common mistakes or errors in the game on action, and wherein the artificial intelligence module is configured to provide recommendations to enhance skill and performance of the player.
17. The system according to claim 1, wherein the plurality of end-point computing devices is configured to communicate with the remote server, and wherein the plurality of end-point computing devices is configured to analyze the stored past data of physical actions, poses, or and a motion of the player and quantitatively analyze the physical actions, by comparing preset or observed data from the same player or a plurality of other players.
18. The system according to claim 1, further comprising a user interface for displaying analysis, visuals, and results computed by the remote server, and wherein the user interface is configured to display a plurality of information of the player and a game, and wherein the plurality of information displayed on the end-point computing devices through the user interface includes a visual playback of previous shots played by the player, an analysis and a visualization

of the previous shots played by the player, an analysis and a visualization of previous games played by the player, and a comparative analysis of the game and shots of the player with a plurality of other players.
19. The system according to claim 1, wherein the monitoring device comprises an impact sensor module, and wherein the impact sensor module is configured to detect and determine an impact of a ball on the bat, and wherein the impact sensor module is configured to determine a position of a plurality of sensors that is attached to the bat, the ball, and the player.
20. The system according to claim 1, wherein the filtering and signal processing module is configured to automatically identify noise from information, and wherein the filtering and signal processing module is configured to contextually determine and identify noise from useful information and pattern.
21. The system according to claim 1, wherein the bat is a ball hitting device used by the player engaged in a sports activity, and wherein the bat is any one of a cricket bat, a tennis racket, a badminton racket, a baseball bat, a table tennis bat, and a hockey stick.
22. The system according to claim 1, wherein the ball is any one of a cricket ball, a tennis ball, a badminton shuttlecock, a baseball, a table tennis ball, and a hockey ball.
23. A method for analysing sports and improving performance by providing feedback in near real time without any visual data capture mechanisms, the method comprising:
collecting a plurality of data points with a plurality of sensors provided in a monitoring device, wherein the monitoring device is configured to detect the plurality of data points from a plurality of actions and poses comprised in a

physical motion of a player, and wherein the plurality of data points or parameters captured by the plurality of sensors includes a motion of the player, a speed at which the player hits a ball, a linear motion, an angular motion, a direction of the shot, an amount of pressure put on the ball by a bat, quaternions, linear acceleration in three axes, angular velocity, a plurality of rotational parameters including roll, pitch and yaw values, and a plurality of physiological features of the player;
transmitting audio information related to an impact of the bat with other player accessories by a microphone module provided on the bat used by the player to identify an accurate impact of the bat with the ball and assist in triangulating an exact impact time during a swing of the bat, and wherein the microphone module is connected to the monitoring device, and wherein the microphone module is configured to distinguish an actual action played with the bat and an unwanted action of the bat and the player;
pairing a plurality of end-point computing devices with the monitoring device for processing the collected data points and a plurality of signals, wherein the plurality of end-point computing devices comprise a plurality of portable computing devices located remotely;
synchronizing the data points collected from the plurality of sensors to recreate or simulate an action of the player in near real time, wherein one of the plurality of sensors acts as a point of reference for synchronizing the data points from the plurality of sensors;
processing the synchronized data points with the plurality of end-point computing devices or a remote server to provide a visual feedback on the actions or poses;
generating and rendering a visual simulation of the actions and poses captured by the monitoring device on the plurality of end-point computing devices, without a need of any video recording equipment; and

contextually rendering the processed data points in the plurality of end-point computing devices;
wherein the remote server comprises a filtering and signal processing module, a database, an analytics module, a machine learning module, an artificial intelligence module, and a gaming engine, and wherein the analytics module is configured to analyze output signals from the filtering and signal processing module based on preset rules and predetermined techniques to compare an action and a performance of the player with a reference template of best practices or ways of performing the physical motion, or an action, or a pose to provide a feedback to the player for improving the performance of the player, and wherein the database is configured to store an analysis data for future reference, retrieval data, meta-data, and information related to a plurality of physical motions of a plurality of players, and wherein the artificial intelligence module is configured to process the stored analysis data, the retrieval data, the meta-data, and the information related to the plurality of physical motions of the plurality of players to provide a predictive analytics on any one of the physical motion, the action, and the pose of the player at a plurality of levels, and wherein the artificial intelligence module is configured to contextually determine and identify noise from useful information, and wherein the gaming engine is configured to perform post-noise filtering and error correction in motion data received from the inertial measurement sensors to recreate motion in a three-dimensional (3D) visual space using a plurality of data points measured with the inertial measurement sensors, and wherein the gaming engine is configured to derive linear motion using the linear acceleration in three axes, and wherein the gaming engine is configured to derive angular motion using the quaternions and the plurality of rotational parameters, and wherein the plurality of sensors in the monitoring device is communicatively connected through a communication network to synchronize a capturing of information or data or an event or a physical motion, and wherein

the plurality of sensors is configured to capture the information of the event simultaneously, and wherein the plurality of sensors is configured to recreate the event from synchronously captured information even when one of the plurality of sensors has failed to capture the event, and wherein one of the plurality of sensors on the bat is a hub for rest of the plurality of sensors, and wherein the one of the plurality of sensors on the bat is configured to detect the impact of the ball on the bat to trigger and activate all the plurality of sensors to start a recording operation of the actions of the player, and wherein the time of impact is a reference point for starting the recording operation of the actions of the player.
24. The method according to claim 23, wherein the plurality of data points from the plurality of actions and poses of the player is detected with the monitoring device, and wherein the monitoring device comprises a gyroscope, an accelerometer, a magnetometer, a compass, motion sensors, temperature sensors, pressure sensors, position sensors, proximity sensors, speed sensors, an audio sensor, a pyroelectric sensor, and a piezoelectric sensor, a communication module, a microcontroller, a memory, and a battery power supply.
25. The method according to claim 23, further comprising the steps of:
transmitting the collected data points to the plurality of end-point computing devices or the remote server through a communication circuit configured in the monitoring device;
detecting the data points and processing the output signals to derive a pattern with the monitoring device; and
synchronizing the plurality of sensors to fuse the plurality of data points received from the plurality of sensors to generate the visual simulation of the player and the actions of the player in near real time.

26. The method according to claim 23, wherein an analysis of output data from the monitoring device is performed on the analytics module, and wherein the output data from the monitoring device is contextually rendered in the plurality of end-point computing devices by the analytics module based on the preset rules, and wherein the end-point computing devices are configured to access the remote server to access any data stored in the remote server.
27. The method according to claim 23, wherein an analysis of output data from the monitoring device is performed on the analytics module, and wherein the analytics module is configured to run on a plurality of end-point computing devices based on preset parameters, and wherein the preset parameters for analysis include a position of the bat, the direction of the shot, a type of the shot, a swing analysis, a shot analysis, a direction analysis, a pressure analysis, an audio analysis, a pattern determination, a comparative analysis, a virtual replay of a game, and a mechanics of the game.
28. The method according to claim 23, wherein the analytics module is configured to combine an analysis of each shot to derive an analysis report for each delivery, shot, game, session, and player, and wherein the analysis module is configured to combine a plurality of game patterns of the player to derive optimum factors or parameters for the player, and wherein the optimum factors or parameters for the player include a body dynamics of the player relative to a position of the bat at the time of impact for increasing a game performance.
29. The method according to claim 23, wherein the filtering and signal processing module is configured to automatically identify noise from information, and wherein the filtering and signal processing module is configured to contextually determine and identify noise from useful information and pattern.

30. The method according to claim 23, wherein the artificial intelligence module is configured to generate an automated audio and text commentary of the physical motion, the actions, and the poses that are captured.
31. The method according to claim 23, wherein the artificial intelligence module is configured to learn from stored previous data or past data of historical data of the player to provide the predictive analytics at the plurality of levels, and wherein the plurality of levels includes a player level analytics, a game level analytics, and a match level analytics, and wherein the artificial intelligence module is configured to predict common mistakes or errors made by the player based on an analysis of past or previous games to provide a recommendation to help the player to overcome or avoid the common mistakes or errors in the game on action, and wherein the artificial intelligence module is configured to provide recommendations to enhance skill and performance of the player.
32. The method according to claim 23, wherein the plurality of end-point computing devices is configured to communicate with the remote server, and wherein the plurality of end-point computing devices is configured to analyze the stored past data of physical actions, poses, or motion of the player and quantitatively analyze the physical actions, by comparing preset or observed data from the same player or a plurality of other players.
33. The method according to claim 23, further comprising the step of displaying analysis, visuals, and results computed by the remote server through a user interface, wherein the user interface is configured to display a plurality of information of the player and a game, and wherein the plurality of information displayed on the end-point computing devices through the user interface includes a visual playback of previous shots played by the player, an analysis and a visualization of the previous shots played by the player, an analysis and a

visualization of previous games played by the player, and a comparative analysis of the game and shots of the player with a plurality of other players.
34. The method according to claim 23, wherein the monitoring device comprises an impact sensor module, and wherein the impact sensor module is configured to detect and determine an impact of a ball on the bat, and wherein the impact sensor module is configured to determine a position of a plurality of sensors that is attached to the bat, the ball, and the player.
35. The method according to claim 23, wherein the machine learning module is configured to identify patterns from observed data and provide contextual suggestions on the plurality of end-point computing devices.