106 may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for the Creating dynamic image hotspots. In an embodiment, the modules 106 includes color component image extraction module (shown in FIG. 2), a segmentation module (shown in FIG. 2), a masked image generation module (shown in FIG. 2), a clustered mask image generation module (shown in FIG. 2), the sample region mask creation module (shown in FIG. 2), a color coded activity map generation module (shown in FIG. 2), cluster contrast value computation module (shown in FIG. 2), optimal color coded regions of interest selection module (shown in FIG. 2) and an activity map generation module (shown in FIG. 2). In an embodiment, FIG. 2 illustrates a functional architecture of the system of FIG. 1A, for activity prediction using portable multispectral laser speckle imager, in accordance with some embodiments of the present disclosure.

The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106.

Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1A) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). Working of the components of the system 100 are explained with reference to the method steps depicted in FIG. 3.

FIGS. 1B and 1C are exemplary block diagrams of the system shown in FIG. 1A for activity prediction using portable multispectral laser speckle imager, in accordance with some embodiments of the present disclosure. FIG. 1B is the front view of the portable multispectral laser speckle imager 114 attached to a device such as mobile device and the FIG. 1C is the orthogonal view.

FIG. 1D is an exemplary circuit diagram of a portable multispectral laser speckle imager system shown in FIG. 1A, in accordance with some embodiments of the present disclosure. Now referring to FIG. 1D, the USB port is connected to a USB controller which continuously read input byte stream of data through the USB Communication Device Class (CDC) communication protocol. The USB controller check for valid 1 byte commands received from the computing device 101. Based on the valid commands the respective transistor/switch turns ON/OFF. The four switches (130A through 130D) control the power line for light sources. Based on application the three laser sources and led sources can be powered in any sequence possible. The computing device 101 includes at least one camera/imaging device such as the mobile device depicted in FIGS. 1B and 1C. In another embodiment, respective image sequence of the sample can also be captured using the camera/imaging device of the computing device 101.

FIG. 3 is an exemplary flow diagram illustrating a method 300 for activity prediction using portable multispectral laser speckle imager implemented by the system of FIG. 1A according to some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more data storage devices or the memory 104 operatively coupled to the one or more hardware processor(s) 102 and is configured to store instructions for execution of steps of the method 300 by the one or more hardware processors 102. The steps of the method 300 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1A and the steps of flow diagram as depicted in FIG. 3. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300, or an alternative method. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof.

At step 302 of the method 300, the one or more hardware processors 102 of the computing device 101 are configured by the programmed instructions to receive a plurality of multispectral image frames and a Red Green Blue (RGB) image from an imaging device. Each the plurality of multispectral image frames comprises a plurality of samples for activity prediction. The plurality of multispectral image frames are captured by illuminating red, blue and green lasers of the imaging device, wherein the RGB image is captured by illuminating Light Emitting Diode (LED) flash of the imaging device. In an embodiment, the imaging device is the portable multispectral imager shown in FIG. 1A through FIG. 1C.

FIG. 4A is an example input image for the processor implemented method for activity prediction using portable multispectral laser speckle imager implemented by the system of FIG. 1A according to some embodiments of the present disclosure. Now referring to FIG. 4A, 402 is an image of a Bengal gram or brown gram,