Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of Invention:
A SYSTEM AND METHOD FOR GUIDING ON REPEATED DEXTEROUS HUMAN WORKMANSHIP

APPLICANT:
LVL ALPHA PVT LTD
An Indian entity having address as:
Off No. 202, 2nd Floor, Yashshree Complex, BT Kawade, Ghorpadi, Pune -01

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application does not claim priority from the Indian patent application.
TECHNICAL FIELD
The present subject matter described herein, in general, relates to a field of dexterous human workmanship. More specifically, the present invention relates to a system for guiding on repeated dexterous human workmanship.
BACKGROUND
This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
In the dynamic tapestry of industrial and artisanal work environments, a rich tapestry of dexterous and repetitive tasks may define the daily routines of the individuals engaged in these realms. These tasks, diverse in nature and complexity, traverse a spectrum ranging from seemingly mundane like polishing, grinding, washing, and bending, to the intricacies of tasks requiring advanced skills, such as weaving, wiring, embroidery, and welding. In the hands of skilled craftsmen and workers, each motion and maneuver, whether subtle or intricate, contributes to the creation of products that form the backbone of industries and the heartbeat of artisanal craftsmanship.

Operating handheld instruments or machinery for specific purposes may require less intricate skills but demands precision and repetition for proficiency. On the other end of the spectrum lie significantly complex tasks that demand not only a wealth of experience but also an extended period of repetitive learning for a worker to attain mastery. Weaving through this spectrum, workers encounter a plethora of challenges in understanding and executing their assigned activities.
For newcomers, these challenges manifest in the form of a demanding learning curve. New workers face the necessity of time and repetitive iterations to internalize the intricacies of their tasks, progressively building confidence with each repetition. The complexity of certain tasks may lead to frustration and inefficiency as the learning process unfolds. Such intricacies often require a nuanced approach to training and guidance that is tailored to the specific demands of each task.
Conversely, experienced craftsmen and workers, despite their proficiency, confront persistent issues that stem from the very nature of their expertise. Prolonged engagement in repetitive tasks contributes to issues of fatigue, safety concerns, and cumulative strain on their arms and bodies. These challenges, left unaddressed, can lead to long-term health issues and a decline in overall productivity.
Remarkably, in the current landscape, there is a conspicuous absence of comprehensive solutions that effectively optimize the intricate movements of the human arms and other related body parts during these tasks. Equally lacking are systems that provide quasi real-time feedback on operational efficiency. This gap is particularly pronounced when considering the need for adaptive solutions that cater to the diverse range of tasks and skill levels within work environments.
The urgency for innovation in this space is underscored by the imperative to enhance not only operational efficiency but also the overall well-being of workers. Bridging this gap requires an integrated and intelligent system that can monitor, analyze, and provide timely feedback on dexterous and repetitive tasks. By doing so, such a system has the potential to transform the learning experience for newcomers and alleviate the challenges faced by seasoned craftsmen, paving the way for a more sustainable and harmonious integration of human labor and technological advancement in industrial and artisanal landscapes alike.
In both industrial and artisanal work environments, a diverse array of dexterous and repetitive tasks is undertaken by workers, ranging from various less complex. The execution of these tasks involves repeated hand positions, movements, and mechanical stress, posing challenges for both novice and experienced craftsmen.
For less complex tasks, new workers often face a learning curve that involves repetitive practice to gain proficiency and confidence in the activity. Conversely, experienced craftsmen contend with prolonged issues of fatigue, safety concerns, and strain on their arms and bodies due to the nature of repeated work. The absence of existing solutions capable of optimizing arm movements and providing quasi real-time feedback on operational efficiency exacerbates these challenges.
Conventional approaches typically offer feedback to workers only after the completion of their tasks. This delayed feedback fails to address the immediate needs of the worker during the execution of the task, hindering the potential for real-time improvement in gestures, postures, and overall operational efficiency.
Thus, there is a need of solution which is capable of optimizing arm movements and providing quasi real-time haptic feedback on operational efficiency to reduce these challenges.
SUMMARY
This summary is provided to introduce concepts related to a system and a method for guiding on repeated dexterous human workmanship, and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in classifying or limiting the scope of the claimed subject matter.
In one implementation, a system for guiding on repetitive dexterous human workmanship is disclosed. The system may comprise one or more wearing devices. The one or more wearing devices may comprise one or more sensors to monitor one or more repeated dexterous human workmanship. The one or more wearable devices may be worn by one or more workers on their body. The one or more wearable devices may comprise a memory. Further, the one or more wearable devices may comprise a processor coupled with the memory. The processor may be configured to execute programmed instructions stored in the memory. Further, the programmed instructions may be executed by the processor for receiving one or more biomechanical measurements related to the one or more repeated dexterous human workmanship. Further, the programmed instructions may be executed by the processor for analysing the one or more biomechanical measurements using an activity optimization model. The analysis may be performed to identify one or more anomalies in the repeated dexterous human workmanship. Further, the programmed instructions may be executed by the processor for identifying, by the activity optimization module, an optimal way of performing the one or more repeated dexterous human workmanship based on the one or more anomalies. Further, the programmed instructions may be executed by the processor for providing multimodal feedback to the one or more workers via the one or more wearable devices for guiding the one or more workers based on the optimal way of performing the one or more repeated dexterous human workmanship.
In another implementation of the present disclosure, a method for guiding on repetitive dexterous human workmanship is disclosed. The method may comprise one or more steps for monitoring one or more repeated dexterous human workmanship using one or more sensors of one or more wearable devices. Further, the method may comprise one or more steps for receiving one or more biomechanical measurements related to the one or more repeated dexterous human workmanship. The method may comprise one or more steps for analysing the one or more biomechanical measurements using the activity optimization model. The analysing may be performed to identify one or more anomalies in the repeated dexterous human workmanship. Further, the method comprises one or more steps for identifying, by the activity optimization model, an optimal way of performing the one or more repeated dexterous human based on the one or more anomalies. The method may comprise one or more steps for providing multimodal feedback to the one or more workers via the one or more wearable devices for guiding on the one or more workers based on the optimal way of performing the one or more repeated dexterous human workmanship.
BRIEF DESCRIPTION OF DRAWINGS
The detailed description is described with reference to the accompanying figures. In the Figures, the left-most digit(s) of a reference number identifies the Figure in which the reference number first appears. The same numbers are used throughout the drawings to refer to the like features and components.
Figure 1 illustrates a block diagram of a system (100) of a wrist-worn wearable device, in accordance with an embodiment of a present subject matter;
Figure 2 illustrates an anatomy and component layout (200) of the one or more wearable devices (101,102,103), in accordance with an embodiment of the present subject matter;
Figure 3 illustrates components (300) connected to the processor (404), in accordance with an embodiment of the present subject matter;
Figure 4 illustrates a decision-making process (400) within the system (100) for determining the optimal arm movement, in accordance with an embodiment of the present subject matter;
Figure 5 illustrates a block diagram (500) showing an overview of a server (501) for guiding on repeated dexterous human workmanship, in accordance with an embodiment of the present subject matter; and
Figure 6 illustrates a flowchart describing a method (600) for guiding repeated dexterous human workmanship, in accordance with an embodiment of the present subject matter.

DETAILED DESCRIPTION
Before the present system and method are described, it is to be understood that this disclosure is not limited to the system and its arrangement as described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the versions or embodiments only and is not intended to limit the scope of the present application.
The terms “comprise”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus preceded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
Reference throughout the specification to “various embodiments,” “some embodiments,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Referring to Figure 1, a block diagram of a system (100) of a wrist-worn wearable device is illustrated, in accordance with an embodiment of the present subject matter. The system (100) comprises one or more wearable devises (101,102,103). Further the one or more wearable devices (101,102,103) may comprise one or more sensors to monitor one or more repeated dexterous human workmanship. Further, the system (100) may comprise an APP (104), a cloud server (105), local server (106). A short-range communication (120) and a GSM (121). The one or more sensors can be worn on different parts of the human arm including, but not limited to, wrist, forearms, biceps, an intelligent repetitive dexterous movements tracking of workmanship of human hands. The one or more wearable devices (101,102,103) may show an approximate position where the device may be situated for monitoring and measurement. The one or more wearable devices (101,102,103) may measure all forms of physiological and vital information including, but not limited to, health parameters, stress and strain, linear motion, gyroscopic motion and limits of human kinetics.
In one implementation, the system (100) can be implemented using hardware, software, or a combination of both, which includes using where suitable, one or more computer programs, mobile applications, or “apps” by deploying either on-premises over the corresponding computing terminals or virtually over cloud infrastructure. The system (100) may include various micro-services or groups of independent computer programs which can act independently in collaboration with other micro-services. The system (100) may also interact with a third-party or external computer system. Internally, the system (100) may be the central processor of all requests for transactions by the various actors or users of the system. a critical attribute of the system (100) is that it can concurrently and instantly complete an online transaction by a system user in collaboration with other systems.
In another implementation, the system (100) comprises multimodal feedback to one or more workers wearing the one or more wearable devices (101,102,103). Further, the modes of feedback to the one or more workers may be in the form of, and not limited to, Haptic, multi-frequency and intensity vibrations in two planar dimensions, contact with the human arm, audio feedback in vocal and alert noises and visual in the form of displayed texts patterns, icons and flashing signs. Furthermore, the feedback may allow the one or more workers to optimize their movements by reducing any wasted actions as well as redistributing or improving support for requiring weight bearing or stress and strain to the human arm during their workmanship. The feedback may also alert the worker regarding potential risky movements or actions which can cause injury due to hyper-extension, overbearing of weight by the arms, improper gripping technique, and repeated movements and motions that can be tiring and detrimental after an extended period of repetitions.
In one implementation, a system (100) comprises a wearable movement tracking device, that can be worn on different parts of the human arm including, but not limited to, the forearm, bicep and wrist, a repetitive dexterous movement tracking of workmanship of human hands.
In another implementation, the system (100) may comprise the one or more wearable devices (101,102,103) that may use a user interface and feedback systems consisting of, but not limited to, an inbuilt haptic touch display (202), audio input through one or more microphones, audio output through inbuilt speakers and multi-function push-buttons. Further, the one or more wearable devices (101,102,103) may also have motion and mechanical parameter measurement sensors including, but not limited to, inertial motion sensor, change of orientation and angular motion sensor, magnetic field sensor, shock or impact monitoring sensor (304), mechanical stress monitoring sensor and mechanical strain monitoring sensor. The one or more wearable device devices (101,102,103) may also have an environmental monitoring sensor (206) which can record raw ambient information and calculated parameters that are not limited to, but also include ambient temperature, pressure, humidity, air resistance and calculated parameters such as Air Quality Index, Volatile Organic Compound composition, Altitude, Dew point and Carbon Monoxide composition. The device also has a type of short or long-range wireless data communication module and location estimating system module like GPS, GLONASS, BAIDU, NAVIC, GCM etc. In one implementation, the one or more wearable devices (101,102,103) may have various health monitoring sensors such as photoplethysmogram, an oxygen saturation level detection sensor, an electrocardiogram, a pulse rate sensor, a heart rate sensor, a body temperature sensor, a galvanic skin response sensor, an electrodermal activity detection sensor, and the like. Further, the one or more wearable devices (1011,102,103) may be ruggedly built with water-resistant exteriors, shock and scratch-resistant body and display cover. The one or more wearable devices (101,102,103) may be battery-operated with an inbuilt rechargeable battery.
In another implementation, the system (100) may consist of one or more wearable devices (101,102,103) having an intelligent onboard processing and memory unit, that processes and stores all the raw information related to motion, mechanical parameters, health vitals, environment and location and sends it over the wireless network to the nearest terminal device containing the software application. Further, in another implementation, the system (100) may provide a statistical self-learning model which may provide the worker with quasi-real-time results that best suit the optimised workflow of the given dexterous workmanship.
In another implementation, the system (100) may also comprise a software platform to illustrate the human dexterous workmanship in 3D visualization and as per human biometrics visualize all the parameters in an easily understandable form allowing the supervisory level personnel to understand and record the repetitive actions and workmanship process and the related time and efficiency.
In an implementation, the system (100) may send data (508) form one or more wearable devices (101,102,103) to processor (404) for processing. The App (104) may be connected to the local server (106). Further, the data (508) may be sent to the remote central server (106). The connection between the app (104) and the remote central server (106) may established periodically through cellular or satellite internet network (121). The cellular or satellite internet network (121) may include, any one of the following: a GSM, a cable network, the wireless network, a telephone network (e.g., Analog, Digital, POTS, PSTN, ISDN, xDSL), a cellular communication network, a mobile telephone network (e.g., CDMA, GSM, NDAC, TDMA, E-TDMA, NAMPS, WCDMA, CDMA-2000, UMTS, 3G, 4G, 5G, 6G), a radio network, a television network, the Internet, the intranet, the local area network (LAN), the wide area network (WAN), an electronic positioning network, an X.25 network, an optical network (e.g., PON), a satellite network (e.g., VSAT), a packet-switched network, a circuit-switched network, a public network, a private network, and/or other wired or wireless communications network configured to carry data.
In yet another embodiment, the server (104) and the one or more wearable devices (101,102,103) may communicate with each other via the network (121). In one implementation, the network (121) may be a wireless network, a wired network, or a combination thereof. The network (121) can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network (121) may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), short-range communication (120) and the like, to communicate with one another. Further, the network (121) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
Referring to figure. 2, a component layout (200) of the one or more wearable devices (101,102,103) is illustrated in accordance with an embodiment of present subject matter. The component layout (200) may comprise a power button (201), touch display (202), a stress strain sensor array (203), a health vital sensor (204), a haptic vibration array (205), an environmental sensor (206) and a speaker (207). The hardware component layout of the one or more wearable devices (101,102,103) may be a major hardware component used for the detection of repetitive dexterous workmanship threshold. The touch display (202) may use the functions such as visual and tactile medium for the user interface. Further, the touch display (202) may represent the visual information and may allow touch-based feedback as a user input. Further, the power button (201) may be configured to turn the one or more wearable devices (101,102,103) on and off. The one or more wearable devices (101,102,103) may be capable of talking multiple user inputs such as short press, double press, and customized user input.
In one implementation, the portable movement of the stress strain sensor array (203), the health vital sensor (204), and the haptic vibration array (205) from the one or more sensors may collect the information of individual’s movements and vitals. Further, the feedback may be provided to the aforementioned dexterous workmanship. Furthermore, the stress strain sensor array (203) may measure the mechanical force exerted to the human arm while the workers working on their workmanship. The health vital sensor (204) may be configured to measure the multitude of human vitals to understand the active status of the worker during their work. The haptic vibration array (205) may be responsible for the haptic feedback (402). The haptic feedback may be in the form of varied frequency vibrations to alert the one or more workers as directional instructions for movement.
Referring to Figure 3, components (300) connected to the processor (404) is illustrated, in accordance with an embodiment of the present subject matter. The components (300) comprise the processor (404) that may corresponds to a sensor part (314) and an output part (315). The sensor part (314) may correspond to an angular rotation axis (301), a linear displacement (302), an inertial motion sensor (303), a shock or impact sensor (304), a force monitor (305), a torque sensor (306), a stress strain sensor (307), and an environment sensor (206). Further, the output part (316) of the processor (404) may correspond to a non-volatile memory (503), a display driver (309), a touch display (202). Furthermore, the output part (316) may correspond to a haptic vibration feedback UP (311 a), haptic vibration feedback DOWN (311 b), haptic vibration feedback LEFT (311 c), and haptic feedback vibration feedback RIGHT (311 d) and a wireless radio (312), an audio speaker (313).
In one embodiment, the angular rotation (301) and the linear displacement may be connected to the inertial motion sensor (303) for implementation of ingress six axis motion data. Further, the shock or impact monitor (304) may detect and process the impact received by the one or more workers during dexterous workmanship. Further, the force monitor (305) and the torque monitor (306) may be coupled to the force monitor (305). Furthermore, the force monitor (305) and torque monitor (306) may be configured to monitor the force and the torque sustained by the human arm during the execution of dexterous human workmanship. The torque monitor (306) may assess based on one or more biomechanical measurements of the one or more workers. The one or more biomechanical measurements may correspond to measurements related to a biomechanical parameter, exterior mechanical parameters, stress and strain on human arms, linear displacement, angular rotation of wrist, forearm, bicep during the repeated dexterous human workmanship.
In one implementation, the one or more sensors may detect and process the impact received by the one or more workers during dexterous workmanship. Furthermore, the data may be ingressed and processed information may be used to perform operations that corresponds to giving audio-visual feedback to the one or more workers. The touch display (202) may act as a feedback system every time an individual performs an irregular motion outside of the threshold. The wireless radio (313) may be used for transporting information between the App (104) and the wireless device. Furthermore, the processor (401) may monitor the movements and the corresponding effects on the workers body to keep the track of their efficiency in finishing the tasks. Furthermore, the one or more sensors may be used to collect homogeneous data corresponding to the one or more repeated dexterous human workmanship. In an exemplary embodiment, the one or more sensors corresponds to a stress strain sensor (307), physiological sensor, health vital sensor, environment sensor (312), motion and mechanical parameter measurement sensor, inertial motion sensor (303), change of orientation and angular motion sensor (301), magnetic field sensor, shock or impact monitoring sensor (304), mechanical stress monitoring sensor, mechanical strain monitoring sensor, photoplethysmogram sensor, an oxygen saturation level detection sensor, an electrocardiogram sensor, a pulse rate sensor, a heart rate sensor, a body temperature sensor, a galvanic skin response sensor, an electrodermal activity detection sensor, water-resistant exteriors are configured to measure a mechanical force exerted to the human arm, while working on their. The one or more wearable devices (101,102,103) may have environmental monitoring sensors (206) which may record raw ambient information and calculated parameters that are not limited to, but also include ambient temperature, pressure, humidity, air resistance and calculated parameters such as Air Quality Index, Volatile Organic Compound composition, Altitude, Dew point and Carbon Monoxide composition. The device also has a type of short or long-range wireless data communication module and location estimating system module like GPS, GLONASS, BAIDU, NAVIC, etc.
In one embodiment, the directional instructions for movement may be achieved by an array of upto four individual haptic vibration motors at 90 degrees each. Moreover, the individual haptic vibration motors may form a complete circular array of top, down, left, right haptic responses. In each iteration, the course correction or direction information may be given to the worker when the relevant haptic motor is turned on. Further, the haptic vibration array (205) may be a set of vibration motors to enable multi-modal linear motion, rotation motion, varied combination of frequency and periodicity of vibrations, or a combination thereof. The frequency and periodicity of the vibration may inform the worker regarding the requirement of course correction, severe alert, higher frequency, low periodicity vibration feedback (401). The haptic feedback may corresponds to a varied combination of frequency and periodicity of vibrations that may be indicating precision of trajectory correction to obtain optimal way of performing the one or more repeated dexterous human workmanship. Furthermore, the combination may explain the movements like left, right arm linear motion, forward linear motion, backward linear motion. The same sequence can also be used to request forward extension and contraction of the forearm and wrist, all of this object to the limitation of human arm kinematics.
In another embodiment, the haptic feedback vibration up (311 a) and the haptic vibration feedback down (311 b) may help the one or more workers to understand the forward and backward movement of the arm required to each optimum user hand movement form. Further, the haptic feedback vibration (311) may be of top-left, top-right, bottom-left, bottom-right and a combination thereof. Further, the haptic feedback vibration (311) may be used to explain dexterous rotational movements. Moreover, the haptic feedback (402) may correspond to the one or more multi-modal haptic vibration feedback (401) for guiding using one of left linear motion, right linear motion, up linear motion, down linear motion, forward linear motion, backward linear motion, clockwise rotation, anticlockwise rotation or a combination thereof. The one or more wearable devices (101,102,103) may comprise the haptic vibration array (205). The parallel top-left vibrations may be the rotation of the arm to be towards the users left until the optimal angular form. Moreover, the same sequence can also be used to request forward extension and contraction of the forearm and wrist. The environment sensor (206) may help to gauge the ambient atmosphere environment of the one or more workers. The information may monitor the system (100) to determine the healthy conditions for carrying out repetitive dexterous human workmanship.
Referring to Figure 4, a decision-making process (400) within the system (100) for determining the optimal arm movement is illustrated, in accordance with an embodiment of the present subject matter. The process (400) may comprise the haptic vibration-based feedback (401), a haptic feedback (402), activity optimization (403), the processor (404), linear displacement (405), and an audio feedback alarm. The system (100) may be capable of providing the decision of the arm movement. The lines near human hand as shown in the figure. 4 which may define the threshold. The processor (404) may detect the motion and may apply optimization (403). Further, the system (100) may send the feedback to the one or more workers. The haptic vibration-based feedback (401) and the haptic feedback (402) may determine the direction of the sensory feedback sent to the one or more workers. The lines around the hands may determine the threshold.
In general, if the hand moves about the threshold, it may detect the motion and apply. Once the task is analyzed the system sends feedback which may sent to the one or more workers send the real-time haptic feedback (402) to the one or more workers. Once that is analyzed the system (300) may determine in which direction the sensor feedback be sent.
Now, referring to figure 5, a block diagram (500) showing an overview of the server (106) for guiding on repeated dexterous human workmanship, is illustrated in accordance with an embodiment of a present subject matter. The server (106) includes a processor (404), an input/output (I/O) interface (502), and the memory (503). The processor (404) is coupled with the memory (503). The processor (404) is configured to execute programmed instructions stored in the memory (503). Further, the processor (404) of the one or more wearable devices (101,102,103) may be configured to predict a trajectory of human appendages based on the one or more biomechanical measurements. The processor (404) may be configured to generate the feedback based on the repeated dexterous human workmanship. The processor (404), in one embodiment, may comprise a standard microprocessor, microcontroller, central processing unit (CPU), distributed or cloud processing unit, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions and/or other processing logic that accommodates the requirements of the present invention.
Further, the I/O interface (502) is an interface to other components of the server (106) and the system (100). The I/O interface (502) may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface (502) may allow the system (100) to interact with the user directly or through the one or more wearable devices (101,102,103). Further, the I/O interface (502) may enable the system (100) to communicate with other wearable devices, such as web servers and external data servers (not shown). The I/O interface (502) can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface (502) may include one or more ports for connecting a number of devices to one another or to another server. In one embodiment, the I/O interface (502) allows the server (106) to be logically coupled to other one or more wearable devices (101,102,103) some of which may be built in. Illustrative components include tablets, mobile phones, scanner, printer, wireless device, etc. Further, the processors (404) can read data from various entities such as memory (503) or I/O interface (502). The processor’s (404) primary functions encompass data acquisition, wherein it gathers repetitive dexterous human workmanship.
The memory (503) may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, Solid State Disks (SSD), optical disks, magnetic tapes, memory cards, virtual memory and distributed cloud storage. The memory (503) may be removable, non-removable, or a combination thereof. The memory (503) may include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The memory (503) may include programs or coded instructions that supplement applications and functions of the system (100). In one embodiment, the memory (503), amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the programs or the coded instructions. In yet another embodiment, the memory (503) may be managed under a federated structure that enables adaptability and responsiveness of the server (106). The memory further may include various modules (504) namely a biomechanical measurement module (505), an activity optimization model (506), and a feedback module (507). In one embodiment, the processor (404) is configured to train the activity optimization model (506) based on the one or more biomechanical measurements received from the one or more sensors monitoring repeated dexterous human workmanship, wherein the training of the activity optimization model (506) is performed using the biomechanical measurement corresponding to the optimal way of performing the one or more repeated dexterous human workmanship, wherein the activity optimization model (506) corresponds to a statistical self-learning model. In one embodiment, the server (106) utilizes the processor (404) for executing the various modules (504) stored in the memory (503). Moreover, the activity optimization model (506), in addition to data gathering may be configured to combine human ergonomic understanding and empirical data. The activity optimization model (506) may further configure to use quasi-real-time data collected during the repetitive dexterous human workmanship execution to make dynamic changes to the trajectory of workmanship.
In one implementation, the biomechanical measurement module (505) may be configured to receive one or more biomechanical measurements related to the one or more repeated dexterous human workmanship. Further, the one or more biomechanical measurements may correspond to measurements related to the one or more biomechanical parameter, exterior mechanical parameters, stress and strain on human arms, linear displacement, angular rotation of wrist, forearm, bicep during the repeated dexterous human workmanship. The activity optimization model (506) may be configured to analyse the one or more biomechanical measurements. Further, the activity optimization model (506) may provide an optimal way of performing the one or more repeated dexterous human workmanship. Furthermore, the activity optimization model (506) may be configured to provide multimodal feedback to the one or more workers via the one or more wearable devices (101,102,103) for guiding the one or more workers based on the optimal way of performing the one or more repeated dexterous human workmanship. In one exemplary embodiment, the feedback module (507) may be configured to receive the real-time haptic feedback (402). The multimodal feedback may correspond to one of the audio alarm, visual images, vector animation, haptic feedback (402), video feedback, text feedback, quasi-real-time feedback, on-device inference or a combination thereof. etc. Furthermore, the real-time feedback may be provided to the one or more workers while performing the repetitive dexterous human workmanship. The real-time haptic feedback (402) may improve the workers posture and may help the worker to reduce the load on the one or more workers’ body. The data collection module (508) may be configured to collect data corresponding to the repeated dexterous human workmanship. In an exemplary embodiment, the input may correspond to a movement of one or more worker body part.
In another implementation, the system (500) may ensure that the model can be sanitizing all data points and maintaining an acceptable range, eliminating rogue values that may have crept in due to the one or more sensors or external impulse responses, which may represent the limitations of human movement, strength and dexterity.
In another implementation, the system (100) may be configured to use a statistical self-learning model that provides the user with quasi-real-time feedback and on-device inference that may best suit the optimized workflow of the repetitive dexterous human workmanship. The system (100) may enable training a novice worker on the optimal way of performing repeated dexterous human workmanship. This system (100) may provide the alert to the one or more workers about any deviation from the normal procedure. The alert can be sent using any of the previously mentioned modes of interaction with the one or more workers. Further, the activity optimization model (506) may perform analysis to identify the one or more anomalies in the repeated dexterous human workmanship may be performed by comparing the one or more biomechanical measurements with threshold value of optimal dexterous repeated workmanship. Furthermore, the workmanship generation model (506) may perform an anomaly detection using various parameters that may classify the type of workmanship, required ergonomy and the repeated movement of the hands. Moreover, these parameters may be analyzed by the one or more wearable devices (101,102,103) during the repeated tasks and may be used for comparison and enhancement of the repeated dexterous human workmanship. Moreover, the anomaly detection may use functions on semi-supervised data with previous knowledge about the known anomalies of the one or more workers.
Referring to figure 6, a flowchart describing a method (600) for guiding repeated dexterous human workmanship, in accordance with an embodiment of the present subject matter. The method (600) is structured as a step-by-step process.
At step (601), the method (600) may be monitoring one or more repeated dexterous human workmanship using one or more sensors of one or more wearable devices (101,102,103).
At step (602), the method (600) may be receiving one or more biomechanical measurements related to the one or more repeated dexterous human workmanship.
At step (603), the method (600) may be analysing the one or more biomechanical measurements using an activity optimization model (506). The analysing (503) is performed to identify one or more anomalies in the repeated dexterous human workmanship.
At step (604), the method (600) may be identifying using the activity optimization model (506), the optimal way of performing the one or more repeated dexterous human based on the one or more anomalies.
At step (605), the method (600) may provide multimodal feedback to the one or more workers, via the one or more wearable devices (101,102,103) for guiding the one or more workers based on the optimal way of performing the one or more repeated dexterous human workmanship.
Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
The foregoing description shall be interpreted as illustrative and not in any limiting sense. A person of ordinary skill in art would understand that certain modifications could come within the scope of this disclosure.
The embodiments, examples and alternatives of the preceding paragraphs or the description and drawings, including any of their various aspects or respective individual features, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments unless such features are incompatible.
, Claims:WE CLAIM:
1. A system (100) for guiding on repetitive dexterous human workmanship, characterized in that, the system (100) comprising:
one or more wearable devices (101,102,103) comprises one or more sensors to monitor one or more repeated dexterous human workmanship, wherein the one or more wearable devices (101,102,103) are worn by one or more workers on their body, wherein the one or more wearable devices (101,102,103) comprise
a memory (503);
a processor (404) coupled with the memory (503), wherein the processor (404) is configured to execute programmed instructions stored in the memory (503), wherein the programmed instructions comprise:
receiving one or more biomechanical measurements related to the one or more repeated dexterous human workmanship;
analysing the one or more biomechanical measurements using an activity optimization model (506), wherein the analysing is performed to identify one or more anomalies in the repeated dexterous human workmanship;
identifying, using the activity optimization model (506), an optimal way of performing the one or more repeated dexterous human workmanship based on the one or more anomalies; and
providing a multimodal feedback to the one or more workers, via the one or more wearable devices (101,102,103), for guiding the one or more workers based on the optimal way of performing the one or more repeated dexterous human workmanship.
2. The system (100) as claimed in claim 1, wherein the one or more biomechanical measurements correspond to measurements related to a biomechanical parameter, exterior mechanical parameters, stress and strain on human arms, linear displacement, angular rotation of wrist, forearm, bicep during the repeated dexterous human workmanship.
3. The system (100) as claimed in claim 1, wherein the processor (404) is configured to train the activity optimization model (506) based on the one or more biomechanical measurements received from the one or more sensors monitoring repeated dexterous human workmanship, wherein the training of the activity optimization model (506) is performed using the biomechanical measurement corresponding to the optimal way of performing the one or more repeated dexterous human workmanship, wherein the activity optimization model (506) corresponds to a statistical self-learning model.
4. The system (100) as claimed in claim 1, wherein the processor (404) of the one or more wearable devices (101,102,103) is configured to predict a trajectory of a human appendages based on the one or more biomechanical measurements, and wherein the processor (404) is configured to generate the feedback based on the predicted trajectory and human inverse kinematics to correct the repeated dexterous human workmanship.
5. The system (100) as claimed in claim 1, wherein the processor (404) is configured to provide real-time haptic feedback (402) for correcting movements during the time of performing the one or more repeated dexterous human workmanship,
wherein the haptic feedback (402) corresponds to one or more multi-directional haptic vibration feedback (401) for guiding using one of left linear motion, right linear motion, up linear motion, down linear motion, forward linear motion, backward linear motion, clockwise rotation, anticlockwise rotation or a combination thereof; wherein one or more wearable devices (101,102,103) comprise a haptic vibration array (205);
wherein the haptic vibration array (205) comprises a set of vibration motors to enable multi-directional linear motion, rotational motion, varied combination of frequency and periodicity of vibrations, or a combination thereof,
wherein the haptic feedback corresponds to a varied combination of frequency and periodicity of vibrations indicating precision of trajectory correction to obtain optimal way of performing the one or more repeated dexterous human workmanship.
6. The system (100) as claimed in claim 1, wherein identifying one or more anomalies in the repeated dexterous human workmanship is performed by comparing the one or more biomechanical measurements with threshold values of optimal dexterous human workmanship.
7. The system (100) as claimed in claim 1, wherein the one or more sensors are used to collect homogeneous data corresponding to one or more repeated dexterous human workmanship.
8. The system (100) as claimed in claim 1, wherein system (100) enables training a novice worker on optimal way of performing repeated dexterous human workmanship, by providing the multimodal feedback, quasi-real-time feedback, and on-device inference using the activity optimization model (506).
9. The system (100) as claimed in claim 1, wherein the multimodal feedback corresponds to one of audio alarm, visual images, vector animation, haptic feedback (402), or a combination thereof.
10. The system (100) as claimed in claim 1, wherein the one or more sensors corresponds to a stress strain sensor (307), physiological sensor, health vital sensor, environment sensor (206), motion and mechanical parameter measurement sensor, inertial motion sensor (303), change of orientation and angular motion sensor, magnetic field sensor, shock monitoring sensor, mechanical stress monitoring sensor, mechanical strain monitoring sensor, photoplethysmogram sensor, an oxygen saturation level detection sensor, an electrocardiogram sensor, a pulse rate sensor, a heart rate sensor, a body temperature sensor, a galvanic skin response sensor, an electrodermal activity detection sensor, water-resistant exteriors are configured to measure a mechanical force exerted to the human arm, while working on their workmanship.
11.The system (100) as claimed in claim 1, wherein the activity optimization model (506), in addition to data gathering is configured to combine human ergonomic understanding and empirical data, wherein the activity optimization model (506) is configured to use quasi-real-time data collected during the repetitive dexterous human workmanship execution to make dynamic changes to the trajectory of workmanship.
12. A method (600) for guiding on repetitive dexterous human workmanship, characterized in that, the method (500) comprising:
monitoring (601) one or more repeated dexterous human workmanship using one or more sensors of one or more wearable devices (101,102,103);
receiving (602) one or more biomechanical measurements related to the one or more repeated dexterous human workmanship;
analysing (603) the one or more biomechanical measurements using an activity optimization model (506), wherein the analysing (503) is performed to identify one or more anomalies in the repeated dexterous human workmanship;
identifying (604), using the activity optimization model (506), optimal way of performing the one or more repeated dexterous human based on the one or more anomalies; and
providing (605) multimodal feedback to the one or more workmanship workers, via the one or more wearable devices (101,102,103), for guiding the one or more workers based on the optimal way of performing the one or more repeated dexterous human workmanship.
13. The method (600) as claimed in claim 12, wherein one or more biomechanical measurements correspond to measurements related to biomechanical parameter, exterior mechanical parameters, stress and strain on human arms, linear displacement, angular rotation of wrist, forearm, bicep during the repeated dexterous human workmanship.
14. The method (600) as claimed in claim 12, wherein the method (600) comprises training the activity optimization model (506) based on one or more biomechanical measurements received from the one or more sensors monitoring repeated dexterous human workmanship; wherein the training of the activity optimization model (506) is performed using biomechanical measurement corresponding to optimal way of performing the one or more repeated dexterous human workmanship.
15. The method (600) as claimed in claim 12, wherein the method (600) comprises identifying one or more anomalies in the repeated dexterous human workmanship is performed by comparing the one or more biomechanical measurements with threshold values of optimal dexterous human.
16. The method (600) as claimed in claim 12, wherein the method (600) comprises training a novice worker on optimal way of performing repeated dexterous human workmanship, by providing the multimodal feedback, quasi-real-time feedback, and on-device inference, using the activity optimization model (506).
17. The method (600) as claimed in claim 12, wherein the method (600) comprises providing real-time haptic feedback (402) for correcting movements during the time of performing one or more repeated dexterous human workmanship;
wherein the haptic feedback (402) corresponds to one or more multi-directional haptic vibration feedback (401) for guiding using one of left linear motion, right linear motion, up linear motion, down linear motion, forward linear motion, backward linear motion, clockwise rotation, anticlockwise rotation or a combination thereof, wherein the one or more wearable devices (101,102,103) comprise a haptic vibration array (205),
wherein the haptic vibration array (205) comprises a set of vibration motors to enable multi-directional linear motion, rotational motion, varied combination of frequency and periodicity of vibrations, or a combination thereof,
wherein the haptic feedback (402) corresponds to a varied combination of frequency and periodicity of vibrations indicating precision of trajectory correction to obtain optimal way of performing the one or more repeated dexterous human workmanship.
18. The method (600) as claimed in claim 12, wherein multimodal feedback corresponds to one of audio alarm, visual images, vector animation, haptic feedback (402), or a combination thereof.
19.The method (100) as claimed in claim 12, wherein the activity optimization model (506), in addition to data gathering is configured to combine human ergonomic understanding and empirical data, wherein the activity optimization model (506) is configured to use quasi-real-time data collected during the repetitive dexterous human workmanship execution to make dynamic changes to the trajectory of workmanship.
Dated this 29th Day of December 2023

Priyank Gupta
Agent for the Applicant
IN/PA-1454