TECHNICAL FIELD

The present disclosure relates generally to training; and more specifically to system of determining efficiency of a trainer delivering a training session to a batch of trainees. Furthermore, the present disclosure also relates to method for determining efficiency of a trainer delivering a training session to a batch of trainees.

BACKGROUND

In recent years, there has been a substantial growth in interest and activity related to the field of education. Furthermore, such growth in the interest and activity has been greatly aided by computer implemented systems. As a result of this interest and the associated efforts, there exists a large array of frameworks, tools and practices for assessment of trainees, students and the like. Generally, these conventional education systems are operable to perform assessment of the people (such as the trainer, trainee) that are involved to impart trainings based on feedback and/or inputs provided or acquired from the people involved to impart trainings.

However, such conventional education systems encounter multiple technical problems. One such problem with the conventional education systems is that such system is dependent on the feedback and/or inputs of the people involved in trainings. As the feedback and/or inputs provided by the people are based on the sentiment, therefore, such feedback and/or inputs may not be absolute. Consequently, rendering such systems to be inappropriate for determining the productivity of the techniques and/or the people involved to impart trainings to the trainee. Furthermore, as the feedback and/or inputs are not absolute, therefore the system may not be efficient for enhancing skill -imparting strategies or system.

Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the conventional education systems. OBJECT OF THE INVENTION

An object of the present invention is to provide a system and a method for determining efficiency of a trainer delivering a training session to a batch of trainees, wherein the determination of the efficiency of the trainer is based on based on real time physiological data associated with each trainee of the batch of trainees following the training session delivered by the trainer.

SUMMARY

The present disclosure seeks to provide a system of determining efficiency of a trainer delivering a training session to a batch of trainees.

The present disclosure also seeks to provide a method for determining efficiency of a trainer delivering a training session to a batch of trainees.

In a first aspect, an embodiment of the present disclosure provides a system of determining efficiency of a trainer delivering a training session to a batch of trainees, the system comprising:

- a wearable sensing device worn by each trainee of the batch of trainees, wherein the wearable sensing device is operable to capture real time physiological data associated with each of the trainee during the training session delivered by the trainer;

- an end user data communication device communicatively coupled to the wearable sensing device; wherein the end user data communication device is operable to receive the real time physiological data associated with each of the trainee during the training session delivered by the trainer; and

- a server arrangement communicatively coupled to the end user data communication device, wherein the server arrangement is operable to:

- receive the real time physiological data associated with each of the trainee during the training session delivered by the trainer, from the end user data communication device;

- process the real time physiological data associated with each of the trainee during the training session delivered by the trainer to determine a collective cognitive response of the batch of trainees for the training session; - evaluate the collective cognitive response of the batch of trainees for the training session delivered by the trainer to identify an effectiveness value of the training session for the batch of trainees; and

- determine an efficiency value of the trainer delivering the training session to the batch of trainees based on the identified effectiveness value of the training session.

In a second aspect, an embodiment of the present disclosure provides a method for determining efficiency of a trainer delivering a training session to a batch of trainees, the method comprising:

- capturing, at a wearable sensing device, real time physiological data associated with each trainee of the batch of trainees during the training session;

- receiving, at an end user data communication device, the real time physiological data associated with each of the trainee during the training session from the wearable sensing device;

- acquiring, at a server arrangement, the real time physiological data associated with each of the trainee during the training session from the end user data communication device;

- processing, at a server arrangement, the real time physiological data associated with each of the trainee during the training session to determine a collective cognitive response of the batch of trainees for the training session;

- evaluating, at a server arrangement, the collective cognitive response of the batch of trainees for the training session to identify an effectiveness value of the training session for the batch of trainees; and

- determining, at a server arrangement, the efficiency value of the trainer delivering the training session to the batch of trainees based on the identified effectiveness value of the training session.

Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

FIG. 1 is a block diagram of a system of determining efficiency of a trainer, in accordance with an embodiment of the present disclosure; and

FIG. 2 is an illustration of steps of a method for determining efficiency of a trainer delivering the training session to the batch of trainees, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DETAIFED DESCRIPTION OF EMBODIMENTS

The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible. Referring to FIG. 1, illustrated is a block diagram of a system 100 of determining efficiency of a trainer, in accordance with an embodiment of the present disclosure. As shown, the system 100 comprises a wearable sensing device 102 including a first module 104 including at least one first module sensor 106 and at least one first module circuitry 108, a second module 110 including at least one second module sensor 112 and at least one second module circuitry 114, a third module 116 including at least one third module sensor 118 and at least one third module circuitry 120, a fourth module 122 including at least one fourth module sensor 124 and at least one fourth module circuitry 126, a fifth module 128 including at least one fifth module circuitry 130. Furthermore, the system 100 includes an end user data communication device 134 communicatively coupled to the wearable sensing device 102 via a first communication interface 132. Moreover, the system 100 includes a server arrangement 138 that is communicatively coupled to the end user data communication device 134 via a second communication interface 136. Additionally, the server arrangement 138 includes a database 140.

The present disclosure provides the system 100 of determining efficiency of a trainer delivering a training session to a batch of trainees. The system relates to an arrangement of programmable and/or non-programmable components that is configured to acquire, process and evaluate collective cognitive response data of the batch of trainees during the training session, and subsequently determine an efficiency of the trainer delivering the training session to the batch of trainees. The batch of trainees includes training individuals and/or groups of such individuals that are being instructed or coached to attain a specific skill by the trainer. It will be appreciated that the trainer is an individual (namely a teacher, instructor, coach, tutor and the like) or a virtual personal (namely, an autonomous program or a bot) instructing or coaching the batch of trainees. Optionally, the system 100 is operable to determine the specific skill that the trainer is efficient at instructing or coaching, based on the collective cognitive response data of the batch of trainees. For example, the system 100 may compare an efficiency‘A’ of a trainer‘C’ delivering the training session of one skill‘X’ to a batch of trainees‘Z’ to an efficiency‘B’ of the trainer ‘C’ delivering the training session of one skill Ύ to the batch of trainees‘Z’. In such example, the efficiency‘A’ of the trainer‘C’ may be greater than the efficiency‘B’ of the trainer‘C’. Subsequently, the system 100 may determine that the trainer‘C’ is more effective in instructing or coaching the skill‘X’ to the batch of trainees‘Z’. The system 100 comprises the wearable sensing device 102 worn by each trainee of the batch of trainees. The wearable sensing device 102 relates to one or more units/ modules of wearable arrangement including one or more sensory device to collect sensory signals from each trainee of the batch of trainees during the training session delivered by the trainer, and a circuitry that is operable to process and transmit sensory signals collected by the sensory device. Optionally, the wearable sensing device can be a wearable gear that can be worn or carried by each trainee. For example, wearable sensing device may be a helmet, a wrist band, a ring and the like. Optionally, the one or more units/ modules of the wearable sensing device 102 can be suitable for different body parts of each of the trainee. Optionally, the one or more units/ modules of the wearable sensing device 102 can be wired or wirelessly connected to each other. Alternatively, each units/ modules of the one or more units/ modules of the wearable sensing device 102 can be independently communicating with the end user data communication device 134 (described herein later).

The wearable sensing device 102 is operable to capture real time physiological data associated with each of the trainee during the training session delivered by the trainer. The term real time as used refers to instantaneous capture and transmittal of sensory signals. Therefore the real time physiological data refers to the instantaneous capture and transmittal of the sensory signals data describing the physiological condition of each of the trainees during the training session, such as neural condition, breathing, heart rate, perspiration, blood pressure, and the like. In an example, the wearable sensing device 102 may be operable to generate the sensory signals related to brain activity, elevated blood flow in the body and the like for each of the trainees during the training session delivered by the trainer.

Optionally, the wearable sensing device 102 comprises the first module 104. The first module 104 as used herein refers to a unit of the wearable sensing device 102 that can be worn or can be attached to a body part of each of the trainee. For example, the first module 104 may be an arrangement that attaches with the head of the trainees body. Furthermore, the first module 104 includes the at least one first module sensor 106 and the at least one first module circuitry 108. Optionally, the at least one first module sensor 106 is an assembly of sensory elements that detects (and possibly responds to) signals, stimuli or changes in quantitative and/or qualitative physiological features of a given trainee of the batch of trainees following exposure to the training session delivered by the trainer, and provides a corresponding output (namely a first module sensory signal). The output is generally a signal that can be transmitted electronically for reading or further processing. Optionally, the output can be converted to human-readable display at the sensor location. Optionally, the at least one first module circuitry 108 is an arrangement of hardware and software components that are operable to read and further process the output provided by the at least one first module sensor 106. Optionally, the hardware can include a pattern of passive electric conductors including a microcontroller and the software components includes set of instruction configured to digitize the output provided by the at least one first module sensor 106. It will be appreciated that the software components including the set of instruction is hosted by the hardware component. Optionally, the at least one first module sensor 104 is operable to generate the first module sensory signal corresponding to real time neural activity associated with each of the trainee during the training session. In an example, the at least one first module sensor 104 is an electrode arrangement positioned at appropriate positions (namely the head of a given trainee), that pick up brainwaves from the head skin corresponding to real time neural activity of the trainee following exposure to the training session delivered by the trainer. In such example, the output generated by the at least one first module sensor 104 may be in the form of a micro-level electrical voltage variations. Optionally, the at least one first module circuitry 108 is operable to digitize the first module sensory signal. In an example, the at least one first module circuitry 108 may be a multistage amplification circuitry that is operable to amplify the output of the at least one first module sensor 104, namely the micro-level electrical voltage variations. Furthermore, the at least one first module circuitry 108 is operable to filter any noise that the output of the at least one first module sensor 104 may include while capturing the real time neural activity (namely the brainwaves) of the trainee. Moreover, the at least one first module circuitry 108 is operable to filter any noise that the output of the at least one first module sensor 104 may include while the at least one first module circuitry 108 is amplifying the output of the at least one first module sensor 104. Subsequently, the at least one first module circuitry 108 may be operable to appropriately digitize the amplified and noise free output of the at least one first module sensor 104 into a form of data for further computational processing.

Optionally, the wearable sensing device 102 comprises the second module 110. The second module 110 is similar to the first module 104 such that the second module 110 as used herein refers to a unit of the wearable sensing device 102 that can be worn or can be attached to a body part of each of the trainee. For example, the second module 110 may be an arrangement that attaches with a finger of the trainee’s body. Furthermore, the second module 110 includes at least one second module sensor 112 and at least one second module circuitry 114. The at least one second module sensor 112 is similar to the at least one first module sensor 106 such that the at least one second module sensor 112 include sensory elements that detects physiological features of a given trainee of the batch of trainees following exposure to the training session delivered by the trainer, and provides a corresponding output (namely a second module sensory signal) that is further transmitted electronically for reading or processing. Optionally, the at least one second module sensor 112 is operable to detect physiological features optically i.e. using light. Optionally, the at least one second module circuitry 114 is an arrangement of hardware and software components that are operable to read and further process the output provided by the at least one second module sensor 112. Optionally, the hardware can include a pattern of passive electric conductors including a microcontroller and the software components includes a set of instruction configured to digitize the output provided by the at least one second module sensor 112. It will be appreciated that the software components including the set of instruction is hosted by the hardware component.

Optionally, the at least one second module sensor 112 is operable to generate a second module sensory signal corresponding to real time pulse oximetry associated with each of the trainee during the training session. In an example, the at least one second module sensor 112 may be an arrangement (such as a finger pulse oximeter) positioned at appropriate positions (namely a finger of a given trainee), that captures the real time pulse oximetry related parameters, like pulse rate, blood oxygen level and the like, of the trainee following exposure to the training session delivered by the trainer. In such example, the output generated by the at least one second module sensor 112 may be in the form of a micro-level electrical voltage vibration. Optionally, the at least one second module circuitry 114 is operable to digitize the second module sensory signal. In an example, the at least one second module circuitry 114 may be a multistage amplification circuitry that is operable to amplify the output of the at least one second module sensor 112, namely the micro-level electrical voltage variations. Furthermore, the at least one second module circuitry 114 is operable to filter any noise that the output of the at least one second module sensor 112 may include while capturing the real time pulse oximetry related parameters (namely cardiac related parameters) of the trainee. Moreover, the at least one second module circuitry 114 is operable to filter any noise that the output of the at least one second module sensor 112 may include while the at least one second module circuitry 114 is amplifying the output of the at least one second module sensor 112. Subsequently, the at least one second module circuitry 114 may be operable to appropriately digitize the amplified and noise free output of at least one second module sensor 112 into a form of data for further computational processing.

Optionally, the wearable sensing device 102 comprises the third module 116. The third module 116 is a unit of the wearable sensing device 102 that can be worn or can be attached to a body part of each of the trainee. For example, the third module 116 may be an arrangement that attaches with skin of a limb of the trainee’s body. Furthermore, the third module 116 includes at least one third module sensor 118 and at least one third module circuitry 120. Optionally, the at least one third module sensor 118 include sensory elements that detects physiological features from the skin of the body of a given trainee of the batch of trainees following exposure to the training session delivered by the trainer, and provides a corresponding output in the form of micro-level electrical voltage variations. Optionally, the at least one third module circuitry 120 is an arrangement of hardware and software components that are operable to read and further process the micro-level electrical voltage variations provided by the at least one third module sensor 118. Optionally, the at least one third module sensor 118 is operable to generate a third module sensory signal corresponding to real time electrodermal activity associated with each of the trainee during the training session. In an example, the at least one third module sensor 118 generates micro-level electrical voltage variations as the third module sensory signal describing real time change in the electrical properties of the skin of the trainee during the training session. Optionally, the at least one third module circuitry 120 is operable to digitize the third module sensory signal. In an example, the at least one third module circuitry 120 may be a circuitry that is operable to monitor the magnitude and nature of the micro-level electrical voltage variations. Subsequently, the at least one third module circuitry 120 may be operable to appropriately digitize the micro -level electrical voltage variations into a form of data for further computational processing. Optionally, the wearable sensing device 102 comprises the fourth module 122. The forth module 116 is a unit of the wearable sensing device 102 that can be worn or can be attached to a body part of each of the trainee. For example, the fourth module 122 may be an arrangement that attaches with a neck of the trainee’s body. Furthermore, the fourth module 122 includes the at least one fourth module sensor 124 and the at least one fourth module circuitry 126. Optionally, the at least one fourth module sensor 124 include sensory elements that detects movement in the body of a given trainee of the batch of trainees during the training session delivered by the trainer, and provides a corresponding output in the form of the micro-level electrical voltage variations. Optionally, the at least one fourth module circuitry 126 is an arrangement of hardware and software components that are operable to read and further process the micro-level electrical voltage variations provided by the at least one fourth module sensor 124. Optionally, the at least one fourth module sensor 124 is operable to generate a fourth module sensory signal corresponding to real time physical activity associated with each of the trainee during the training session. In an example, the at least one fourth module sensor 124 is an accelerometer that generates fourth module sensory signal in the form of the micro -level electrical voltage variations with respect to the real time physical activity (such a movement) performed by the trainee during the training session. Optionally, the at least one fourth module circuitry 126 is operable to digitize the fourth module sensory signal. In an example, at least one fourth module circuitry 126 may be operable to detect the micro-level electrical voltage variations with respect to the real time physical activity (such a movement) performed by the trainee. Subsequently, the at least one fourth module circuitry 126 may be operable to appropriately digitized the micro-level electrical voltage variations into a form of data for further computational processing.

Optionally, the wearable sensing device 102 comprises the fifth module 128 including at least one fifth module circuitry 130. In an example, the fifth module 128 may be a microcontroller cum wireless communication module that is operable to transfer the data for further computational processing. Optionally, the at least one fifth module circuitry 130 is operable to acquire the digitized first, second, third and fourth module sensory signals, and transmit the digitized sensory signals to the end user data communication device. Specifically, the at least one fifth module circuitry 130 is operable to acquire the data of the digitized micro-level electrical voltage variations in the respective first, second, third and fourth module sensory signals and thereafter transfer the data to the end user data communication device 134 via the first communication interface 132.

Optionally, the first communication interface 132 is low bandwidth radio communication interfaces that are capable of transferring the data corresponding to the digitized first, second, third and fourth module sensory signals from the wearable sensing device 102 to the end user data communication device 134 at a data speed of lOObps, to lOkbps. Optionally, the first communication interface 132 are long range low bandwidth radio communication interface. Optionally, the first communication interface 132 include, but are not limited to Low-Power Wide-Area Network (LPWAN) or other wireless area network technology, such as wireless personal area network technology. In an example, wireless personal area network technology may include INSTEON®, IrDA®, Wireless USB®, Bluetooth®, WiFi, Bluetooth Low Energy (BLE), Z-Wave®, ZigBee®, Body Area Network and so forth.

Optionally, the first communication interface 132 is of higher bandwidth radio communication interfaces that are capable of transferring the data corresponding to the digitized first, second, third and fourth module sensory signals from the wearable sensing device 102 to the end user data communication device 134 at a speed of lOMbps.

The system 100 comprises the end user data communication device 134 communicatively coupled to the wearable sensing device 102. The end user data communication device 134 refers to an electronic device that is capable of performing specific tasks associated with the system 100, such as receiving the data from the wearable sensing device 102 and transferring the received data for further processing at the server arrangement 138. Furthermore, the end user data communication device 134 is operable to receive the real time physiological data associated with each of the trainee during the training session delivered by the trainer. Specifically, the end user data communication device 134 receives the real time physiological data associated with each of the trainee via the first communication interface 132. It will be appreciated that the real time physiological data associated with each of the trainee relates to the digitized first, second, third and fourth module sensory signals acquired and transmitted by the at least one fifth module circuitry device 134 is operable to receive the real time physiological data associated from the wearable sensing device 102 automatically and reputedly after a specific time period.

Optionally, the end user data communication device 134 is portable computing device. In an example, the end user data communication device 134 may include but are not limited to, laptop computers, tablet computers, phablet computers, smartphones, and personal digital assistants.

The system 100 comprises the server arrangement 138 communicatively coupled to the end user data communication device 134. The server arrangement 138 refers to a structure and/or module that include programmable and/or non-programmable components configured to store, process the real time physiological data associated with each of the trainee of the batch of trainees during the training session delivered by the trainer, and subsequently determine the efficiency of the trainer delivering the training session to a batch of trainees. Optionally, the server arrangement 138 can includes both single hardware server and/or plurality of hardware servers operating in a parallel or distributed architecture. Furthermore, the server arrangement 138 communicatively coupled to the end user data communication device 134 via the second communication interface 136. The second communication interface 136 is a wired or wireless communication that can be carried out via any number of known protocols, including, but not limited to, Internet Protocol (IP), Wireless Access Protocol (WAP), Frame Relay, or Asynchronous Transfer Mode (ATM). Moreover, any other suitable protocols using voice, video, data, or combinations thereof, can also be employed. Moreover, the second communication interface 136 may be implemented using various protocols such as, TCP/IP, IPX, Appletalk, IP-6, NetBIOS, OSI, any tunneling protocol (e.g. IPsec, SSH), or any number of existing or future protocols. Optionally, the second communication interface 136 is a high speed data communication channel.

Optionally, the server arrangement 138 is operable to store the real time physiological data associated with each of the trainee during the training session, and a user identification of each trainee of the batch of trainees. Optionally, the user identification of each trainee of the batch of trainees can be specific text and/or number assigned to each trainee of the batch of trainees. Optionally, the server arrangement 138 is operable to store the real time physiological data and the user identification in an organized body of related data (namely a database) in the form of a table, a map, a grid, a packet, a datagram, a file, a document, a list or in any other form.

The server arrangement 138 is operable to receive the real time physiological data associated with each of the trainee during the training session delivered by the trainer, from the end user data communication device 134. Specifically, the server arrangement 138 receives the real time physiological data from the end user data communication device 134 and the second communication interface 136. Optionally, the end user data communication device 134 can automatically upload the real time physiological data to the server arrangement 138 via the second communication interface 136 regularly after a specific period of time. Alternatively, the server arrangement 138 receives the real time physiological data upon requesting the end user data communication device 134 after a specific period of time.

The server arrangement 138 is operable to process the real time physiological data associated with each of the trainee during the training session delivered by the trainer to determine a collective cognitive response of the batch of trainees for the training session. The cognitive response refers to any characteristic related to cognitive, or mental state, of a trainee following exposure to the training session delivered by the trainer. In an example, the cognitive responses include, but are not limited to, perceiving, recognizing, conceiving, judging, memory, reasoning and imagining. Furthermore, the cognitive response of each trainee is tracked with the wearable sensing device 102 including one or more sensors modules (namely the sensors modules 106, 112, 118, 124) that is transferred to the end user data communication device 134 and subsequently to the server arrangement 138. Therefore, the collective cognitive response refers to combined responses of the batch of trainees following exposure to the training session delivered by the trainer.

Optionally, the server arrangement 138 further comprises one or more algorithms. The term algorithm refers to any collection or set of instructions executable by the server arrangement 138 so as to configure the server arrangement 138 to perform one or more task of determining efficiency of a trainer. Optionally, the one or more algorithms executed in the server arrangement 138 are operable to analyze the digitized sensory signals from the wearable sensing device 102. Specifically, the one or more algorithms is operable to perform one or more computation steps to analyze the digitized sensory signals transmitted from the wearable sensing device 102 via the end user data communication device 134. It will be appreciated that the digitized sensory signals received from the wearable sensing device 102 for a given trainee corresponds to the real time physiological data associated with the given trainee during the training session delivered by the trainer. In an example, the one or more algorithms is configured to generate numerical values for the digitized sensory signals received from the wearable sensing device 102 attached to each trainee in the batch of trainees

Optionally, the one or more algorithms executed in the server arrangement 138 are operable to determine the collective cognitive response of the batch of trainees for the training session. Optionally, the one or more algorithms is configured to consider cognitive response of each trainee of the batch of trainees to form the collective cognitive response of the batch of trainees for the training session. In an example, the one or more algorithms may be operable to generate a numerical value for the cognitive response of each trainee in the batch of trainees. In such example, the one or more algorithms may be operable to perform a summation of a numerical value for the cognitive response of each trainee in the batch of trainee, to determine the collective cognitive response of the batch of trainees for the training session.

The server arrangement 138 is operable to evaluate the collective cognitive response of the batch of trainees for the training session delivered by the trainer to identify an effectiveness value of the training session for the batch of trainees. The server arrangement 138 is operable to evaluate the collective cognitive response of the batch of trainees for the training session delivered by the trainer by comparing the collective cognitive response to a predefined condition that is stored (and/or programed) in the server arrangement 138. Optionally, the one or more algorithms executed in the server arrangement 138 are operable to evaluate the collective cognitive response of the batch of trainees for the training session. The one or more algorithms include the predefined condition that is used for the comparison of the collective cognitive response of the batch of trainees for the training session delivered by the trainer. Optionally, the predefined condition can be a predefined value that can be used to determine the condition of the training session delivered by the trainer. In an example, the collective cognitive response of the batch of trainees for the training session delivered by the trainer may be numerical value. In such example, the one or more algorithms may be configured to compare the numerical value corresponding to the collective cognitive response with the predefined value to determine if a training session delivered by the trainer is successful or not. In such example, the collective cognitive response of the batch of trainees for the training session delivered by the trainer may be‘IT and the predefined value that can be used to determine the condition of the training session delivered by the trainer‘P\ Furthermore, a numerical value associated with‘U’ is greater than a numerical value associated with‘P\ In such event, the one or more algorithms may be configured to determine that the training session delivered by the trainer is successful. Furthermore, the effectiveness value of the training session for the batch of trainees is numerical value that describes an amount of successfulness of the training session delivered by the trainer. Optionally, the one or more algorithms executed in the server arrangement 138 are operable to identify the effectiveness value of the training session for the batch of trainees. It will be appreciated that the training session is delivered by the trainer to the batch of trainees. According to the aforementioned example, wherein the numerical value associated with‘U’ is greater than the numerical value associated with ‘P’, the one or more algorithms may be configured to identify the difference between the numerical values associated with‘U’ and ‘P’. In such event, the difference between the numerical values associated with‘U’ and ‘P’ may be‘T’ including numerical value. In such event, the one or more algorithms may be configured to determine the effectiveness value of the training session delivered by the trainer for the batch of trainees to be‘T’.

The server arrangement 138 is operable to determining an efficiency value of the trainer delivering the training session to the batch of trainees based on the identified effectiveness value of the training session. The efficiency value of the trainer is a numerical value that describe the amount of effectiveness of the trainer in delivering the training of specific skill to the batch of trainees. Optionally, the one or more algorithms are operable to identify efficiency of a trainer delivering the training session to the batch of trainees. According to the aforementioned example, wherein the determine the effectiveness value of the training session delivered by the trainer for the batch of trainees to be‘T’, the one or more algorithms may include a predetermined value for comparing with the effectiveness value‘T’. In such event, the one or more algorithms is configured to determine an efficiency value of the trainer delivering the training session to the batch of trainees T form the comparison of the effectiveness value‘T’ to the predetermined value. Optionally, the server arrangement 138 can be configured to identify and store the efficiency values of the trainer delivering the training session of one or more skills to the batch of trainees. Subsequently, the one or more algorithms executed in the server arrangement 138 are operable to compare the efficiency values determined for the trainer while delivering the training session of one or more skills to the batch of trainees, and subsequently, identify the skill corresponding to which the trainer has the highest efficiency value. Beneficially, such identified skill can be determined as an appropriate skill for the trainer to deliver the training session to the batch of trainees.

Referring to FIG. 2, there are shown steps of a method for determining efficiency of a trainer delivering the training session to the batch of trainees, in accordance with an embodiment of the present disclosure. At step 202, real time physiological data associated with each trainee of the batch of trainees during the training session at a wearable sensing device is captured. At step 204, the real time physiological data associated with each of the trainee during the training session from the wearable sensing device is received at an end user data communication device. At step 206, the real time physiological data associated with each of the trainee during the training session from the end user data communication device is acquired at a server arrangement. At step 208, the real time physiological data associated with each of the trainee during the training session to determine a collective cognitive response of the batch of trainees for the training session is processed at the server arrangement. At step 210, the collective cognitive response of the batch of trainees for the training session to identify an effectiveness value of the training session for the batch of trainees is evaluated at the server arrangement. At step 212, the efficiency value of a trainer delivering the training session to the batch of trainees based on the identified effectiveness value of the training session is determined at a server arrangement.

The steps 202 to 212 are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. In one example, the wearable sensing device comprises a first module including at least one first module sensor and at least one first module circuitry, wherein the at least one first module sensor is operable to generate a first module sensory signal corresponding to real time neural activity associated with each of the trainee during the training session, and wherein the at least one first module circuitry is operable to digitized the first module sensory signal, a second module including at least one second module sensor and at least one second module circuitry, wherein the at least one second module sensor is operable to generate a second module sensory signal corresponding to real time pulse oximetry associated with each of the trainee during the training session, and wherein the at least one second module circuitry is operable to digitized the second module sensory signal, a third module including at least one third module sensor and at least one third module circuitry, wherein the at least one third module sensor is operable to generate a third module sensory signal corresponding to real time electrodermal activity associated with each of the trainee during the training session, and wherein the at least one third module circuitry is operable to digitized the third module sensory signal, a fourth module including at least one fourth module sensor and at least one fourth module circuitry, wherein the at least one fourth module sensor is operable to generate a fourth module sensory signal corresponding to real time physical activity associated with each of the trainee during the training session, and wherein the at least one fourth module circuitry is operable to digitized the fourth module sensory signal, and a fifth module including at least one fifth module circuitry, wherein the at least one fifth module circuitry is operable to acquire the digitized first, second, third and fourth module sensory signals, and transmit the digitized sensory signals to the end user data communication device. In another example, the server arrangement further comprises one or more algorithms, wherein the one or more algorithms are operable to analyze the digitized sensory signals from the wearable sensing device, and determine the collective cognitive response of the batch of trainees for the training session, and evaluate the collective cognitive response of the batch of trainees for the training session to identify the effectiveness value of the training session for the batch of trainees. In another example, the one or more algorithms are further operable to identify efficiency of a trainer delivering the training session to the batch of trainees. In yet another example, the server arrangement is operable to store: the real time physiological data associated with each of the trainee during a training session, and a user identification of each trainee of the batch of trainees.

ADVANTAGES OF THE INVENTION

The system and the method comprises the wearable sensing device that is operable to capture real time physiological data of each trainees for determining the efficiency of a trainer delivering a training session to the trainees. Furthermore, the determined efficiency of a trainer can be used to determine the skill that the trainer is most suitable to train or coach. Additionally, the efficiency of a trainer can be used by an organization the trainer is associated with, to determine a trainer that should be employed to train or coach a specific skill. Moreover, the real time physiological data of each trainees can be used to determine the level of engagement (for a specific time period) of each trainee during the training session, and thereby use such information related to the level of engagement to device methodologies to make the training session delivered by the trainer more efficient. Furthermore, such information related to the level of engagement can be used to determine which skills each of the trainees are interested to lean. Additionally, the system comprises the end user data communication device that can be implemented as a portable device (such as a smartphone), for relaying the real time physiological data associated with the trainee from the wearable sensing device to a server arrangement. Such an implementation of the end user data communication device enables to easily relay the data from the wearable sensing device to the server arrangement while reducing a requirement of complex electronic components within the wearable sensing device for such a purpose. Consequently, the wearable sensing device can be fabricated to have a compact form factor that further reduces discomfort caused to trainees while wearing the wearable sensing device

Modifications to embodiments of the invention described in the foregoing are possible without departing from the scope of the invention as defined by the accompanying claims. Expressions such as“including”,“comprising”,“incorporating”,“co nsisting of’,“have”, “is” used to describe and claim the present invention are intended to be construed in a non exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. Numerals included within parentheses in the accompanying claims are intended to assist understanding of the claims and should not be construed in any way to limit subject matter claimed by these claims. CLAIMS

1. A system of determining efficiency of a trainer delivering a training session to a batch of trainees, the system comprising:

- a wearable sensing device worn by each trainee of the batch of trainees, wherein the wearable sensing device is operable to capture real time physiological data associated with each of the trainee during the training session delivered by the trainer;

- an end user data communication device communicatively coupled to the wearable sensing device; wherein the end user data communication device is operable to receive the real time physiological data associated with each of the trainee during the training session delivered by the trainer; and

- a server arrangement communicatively coupled to the end user data communication device, wherein the server arrangement is operable to:

- receive the real time physiological data associated with each of the trainee during the training session delivered by the trainer, from the end user data communication device;

- process the real time physiological data associated with each of the trainee during the training session delivered by the trainer to determine a collective cognitive response of the batch of trainees for the training session;

- evaluate the collective cognitive response of the batch of trainees for the training session delivered by the trainer to identify an effectiveness value of the training session for the batch of trainees; and

- determine an efficiency value of the trainer delivering the training session to the batch of trainees based on the identified effectiveness value of the training session.

2. The system of claim 1, wherein the wearable sensing device comprises:

- a first module including at least one first module sensor and at least one first module circuitry, wherein the at least one first module sensor is operable to generate a first module sensory signal corresponding to real time neural activity associated with each of the trainee during the training session, and wherein the at least one first module circuitry is operable to digitize the first module sensory signal;

- a second module including at least one second module sensor and at least one second module circuitry, wherein the at least one second module sensor is operable to generate a second module sensory signal corresponding to real time pulse oximetry associated with each of the trainee during the training session, and wherein the at least one second module circuitry is operable to digitize the second module sensory signal;

- a third module including at least one third module sensor and at least one third module circuitry, wherein the at least one third module sensor is operable to generate a third module sensory signal corresponding to real time electrodermal activity associated with each of the trainee during the training session, and wherein the at least one third module circuitry is operable to digitize the third module sensory signal;

- a fourth module including at least one fourth module sensor and at least one fourth module circuitry, wherein the at least one fourth module sensor is operable to generate a fourth module sensory signal corresponding to real time physical activity (a body temperature, a body movement) associated with each of the trainee during the training session, and wherein the at least one fourth module circuitry is operable to digitize the fourth module sensory signal; and

- a fifth module including at least one fifth module circuitry, wherein the at least one fifth module circuitry is operable to acquire, process the digitized first, second, third and fourth module sensory signals, and transmit the digitized sensory signals to the end user data communication device.

3. The system of claim 1, wherein the server arrangement further comprises one or more algorithms, wherein the one or more algorithms are operable to:

- analyze the digitized sensory signals from the wearable sensing device; and

- determine the collective cognitive response of the batch of trainees for the training session, and evaluate the collective cognitive response of the batch of trainees for the training session to identify the effectiveness value of the training session for the batch of trainees.

4. The system of claim 3, wherein the one or more algorithms are further operable to identify efficiency of a trainer delivering the training session to the batch of trainees.

5. The system of claim 1, wherein the server arrangement is operable to store: the real time physiological data associated with each of the trainee during a training session, and a user identification of each trainee of the batch of trainees. 6. A method for determining efficiency of a trainer delivering a training session to a batch of trainees, the method comprising:

- capturing, at a wearable sensing device, real time physiological data associated with each trainee of the batch of trainees during the training session;

- receiving, at an end user data communication device, the real time physiological data associated with each of the trainee during the training session from the wearable sensing device;

- acquiring, at a server arrangement, the real time physiological data associated with each of the trainee during the training session from the end user data communication device;

- processing, at the server arrangement, the real time physiological data associated with each of the trainee during the training session to determine a collective cognitive response of the batch of trainees for the training session;

- evaluating, at the server arrangement, the collective cognitive response of the batch of trainees for the training session to identify an effectiveness value of the training session for the batch of trainees; and

- determining, at the server arrangement, the efficiency value of the trainer delivering the training session to the batch of trainees based on the identified effectiveness value of the training session.

7. The method of claim 6, wherein the wearable sensing device comprises:

- a first module including at least one first module sensor and at least one first module circuitry, wherein the at least one first module sensor is operable to generate a first module sensory signal corresponding to real time neural activity associated with each of the trainee during the training session, and wherein the at least one first module circuitry is operable to digitize the first module sensory signal;

- a second module including at least one second module sensor and at least one second module circuitry, wherein the at least one second module sensor is operable to generate a second module sensory signal corresponding to real time pulse oximetry associated with each of the trainee during the training session, and wherein the at least one second module circuitry is operable to digitize the second module sensory signal;

- a third module including at least one third module sensor and at least one third module circuitry, wherein the at least one third module sensor is operable to generate a third module sensory signal corresponding to real time electrodermal activity associated with each of the trainee during the training session, and wherein the at least one third module circuitry is operable to digitize the third module sensory signal;

- a fourth module including at least one fourth module sensor and at least one fourth module circuitry, wherein the at least one fourth module sensor is operable to generate a fourth module sensory signal corresponding to real time physical activity associated with each of the trainee during the training session, and wherein the at least one fourth module circuitry is operable to digitize the fourth module sensory signal; and

- a fifth module including at least one fifth module circuitry, wherein the at least one fifth module circuitry is operable to acquire the digitized first, second, third and fourth module sensory signals, and transmit the digitized sensory signals to the end user data communication device.

8. The method of claim 6, wherein the server arrangement further comprises one or more algorithms, wherein the one or more algorithms are operable to:

- analyze the digitized sensory signals from the wearable sensing device; and

- determine the collective cognitive response of the batch of trainees for the training session, and evaluate the collective cognitive response of the batch of trainees for the training session to identify the effectiveness value of the training session for the batch of trainees.

9. The method of claim 6, wherein the one or more algorithms are further operable to identify efficiency of a trainer delivering the training session to the batch of trainees.

10. The method of claim 6, wherein the server arrangement is operable to store: the real time physiological data associated with each of the trainee during a training session, and a user identification of each trainee of the batch of trainees. ABSTRACT

Disclosed is a system and a method of determining efficiency of a trainer delivering a training session to a batch of trainees. The system comprises a wearable sensing device worn by each trainee of the batch of trainees, an end user data communication device communicatively coupled to the wearable sensing device, a server arrangement communicatively coupled to the end user data communication device. The server arrangement is operable to receive real time physiological data associated with each of the trainee , process the real time physiological data associated with each of the trainee to determine a collective cognitive response of the batch of trainees, evaluate the collective cognitive response of the batch of trainees to identify an effectiveness value of the training session for the batch of trainees and determine an efficiency value of the trainer based on the identified effectiveness value of the training session.

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