Description:FIELD OF INVENTION

[001] The present invention relates to a support system to be worn by a user for assisting in their body movements. More specifically, the present invention relates to an Internet of things based smart exoskeleton that measure physiological body parameters to decide fitness for heavy lifting tasks and keep track of work performed by the user, thereby enabling equitable distribution of work or load or weight.

BACKGROUND OF THE INVENTION

[002] An exoskeleton is a wearable device that augments, enhances, or replicates the wearer’s body movement. It is typically designed to provide support, strength, and endurance, particularly to the limbs and lower back. In the context of warehouse and industrial environments, exoskeleton are deployed to assist workers in performing physically demanding task, such as lifting heavy load, repetitive motion task, and maintaining ergonomic postures. By distributing the weight of lifted heavy objects and augmenting the wearer’s muscle power, exoskeleton aims to reduce the risk of musculoskeletal injuries among workers. In large scale warehouse operations, where efficacy, attrition rate and safety are paramount, exoskeletons have become increasingly important.

[003] Despite their manifold benefits, currently available exoskeleton presents several disadvantages that hinder their effectiveness in industrial settings. One major limitation is their lack of integrated system for real time monitoring of physiological parameters such as heart rate, pulse rate, body temperature etc. This deficiency leads to non-adaption to user’s physical state and prevention of workers from overexertion and potential injuries. Additionally, these devices do not track comprehensive usage metric, such as the duration of use, load weight lifted, making it difficult to ensure equitable distribution of load or weight lifted among workers. This oversight can lead to uneven workloads and increase risk of injury to some workers. Furthermore, the absence of integrated safety feature and load management capabilities means that exoskeleton is not able to prevent unsafe usage scenario, such as continued lifting despite fatigue, thus failing to fully protect workers’ health and safety.

[004] There are several patent applications that disclose an exoskeleton. One such United States patent application US10561564B2 discloses a rehabilitation or mobility enhancing device in the form of an exoskeleton that provides mobility assistance or enhancement to a user within the exoskeleton. The exoskeleton may include a torso support and two leg supports coupled to the torso support. Each leg support may include a hip joint, a knee joint, a foot module, and panels connecting the torso support to the hip joints, the hip joints to the knee joints, and the knee joints to the foot modules. Actuators such as motors may be positioned in a discrete location on exoskeleton away from the relatively bulky knee and hip joints to provide for a low profile of the exoskeleton, which may allow the exoskeleton to be worn inconspicuously under a user's clothing. However, the cited document lacks integrated safety feature and load management capabilities means that exoskeleton is not able to prevent unsafe usage scenario, such as continued lifting despite fatigue, thus failing to fully protect workers’ health and safety.

[005] In order to overcome the problem associated with state of arts, there is a need for the development of an efficient IOT based smart exoskeleton suit that can overcome the aforesaid limitations in a more efficient manner.

OBJECTIVE OF THE INVENTION

[006] The primary objective of the present invention is to provide an IOT based smart exoskeleton suit.

[007] Another objective of the present invention is to provide an exoskeleton suit with workers’ safety feature.

[008] Another objective of the present invention is to ensure equitable sharing of lifted load.

[009] Another objective of the present invention is to provide an exoskeleton suit that is low cost and easy to use.

[0010] Yet another objective of the present invention is to measure physiological parameters of users’ body and raise an alarm upon detecting any of the physiological parameter is in unsafe range.

[0011] Yet another objective of the present invention is to provide an exoskeleton suit that prevents users from musculoskeletal diseases.

[0012] Yet another objective of the present invention is to monitor or capture health status of associates or workers or heavy lifters, which results in increased productivity.

[0013] Yet another objective of the present invention is to measure productivity of worker through measuring weight lifted and frequency of lifting.

[0014] Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.

BRIEF DESCRIPTION OF DRAWINGS
[0015] The present invention will be better understood after reading the following detailed description of the presently preferred aspects thereof with reference to the appended drawings, in which the features, other aspects and advantages of certain exemplary embodiments of the invention will be more apparent from the accompanying drawing in which:

[0016] Figure 1 illustrates a schematic diagram of an IOT based smart exoskeleton suit.

[0017] Figure 2 illustrates block diagram of IOT based smart exoskeleton suit.

[0018] Figure 3 illustrates screen of a handheld device.

[0019] Figure 4 illustrates screen of the handheld device depicting duration of use of exoskeleton suit.

[0020] Figure 5 illustrates screen of the handheld device depicting temperature and pulse rate of user.

[0021] Figure 6 illustrates screen of the handheld device depicting rating on a pre-set scale of users’ health.

[0022] Figure 7 illustrates screen of the handheld device depicting gripping strength of worker measures through a torque sensor.

[0023] Figure 8 illustrates screen of the handheld device depicting weight lifted by user.

[0024] Figure 9 illustrates screen of the handheld device depicting number of times of weight lifting.

[0025] Figure 10 illustrates screen of the handheld device depicting steps count and calories burnt of user.

[0026] Figure 11 illustrates screen of the handheld device depicting oxygen saturation level of user.

[0027] Figure 12 illustrates screen of the handheld device depicting biometric authentication of user for tracking record of each worker.

SUMMARY OF THE INVENTION
[0028] The present invention relates to an IOT based smart exoskeleton suit. The exoskeleton suit comprises of a first set of sensors and a second set of sensors for measuring the pre-defined parameters such as weight of load lifted, heart rate, pulse rate, oxygen saturation level (SPO2), number of lift of load, time duration of use of exoskeleton suit, calorie consumed, step count, gripping strength, and body temperature. The exoskeleton suit further comprises of a microcontroller configured to provide fitness rating based on the pre-defined parameters. The rating is categorized into three ranges: first range indicates fit for work, second range indicates moderate level of fitness, and third range indicates unfit for work. Therefore, the exoskeleton suit measure physiological body parameters to decide fitness for lifting task and keep track of work performed by the user, thereby enabling equitable distribution of heavy load.

[0029] The present invention also provides a method for operation of the IOT based smart exoskeleton suit. The method comprising steps of: authenticating the user through the biometric sensor for storing data of each user separately; fastening of the adjustable shoulder strap and the body strap; measuring, heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature through the first set of sensors and the second set of sensors; comparing the heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature with pre-set threshold values; raising the alarm by transmitting the alert signal to the plurality of handheld devices, upon detecting heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature in the unsafe range; providing a rating in the first range, the second range, and the third range on the pre-set scale through the microcontroller measuring weight of load lifted, number of lift, time duration of use of exoskeleton suit, calorie consumed, step count, gripping strength through the first set of sensors and the second set of sensors; and displaying the weight of load lifted, number of lift, time duration of use of exoskeleton suit, calorie consumed, step count, gripping strength on screen of each of the plurality of the handheld devices.

DETAILED DESCRIPTION OF INVENTION
[0030] The following detailed description and embodiments set forth herein below are merely exemplary out of the wide variety and arrangement of instructions which can be employed with the present invention. The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. All the features disclosed in this specification may be replaced by similar other or alternative features performing similar or same or equivalent purposes. Thus, unless expressly stated otherwise, they all are within the scope of the present invention.

[0031] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0032] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention.

[0033] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0034] It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, integers, steps, or components but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.

[0035] Accordingly, the present invention relates to a support system to be worn by a user for assisting in their body movements. More specifically, the present invention relates to an Internet of things based smart exoskeleton that measure physiological body parameters to decide fitness for lifting task and keep track of work performed by the user, thereby enabling equitable distribution of load.

[0036] In a preferred embodiment of the present invention, as shown in Figure 1, An IOT based smart exoskeleton suit (100), comprises of a shoulder sub-assembly (102) with adjustable shoulder strap (134), a torso sub-assembly (108) coupled to center of the shoulder subassembly (102), and two leg sub-assembly (122) coupled to the torso sub-assembly (108) via height adjustable rods (136).

[0037] The shoulder sub-assembly (102) further comprises of an alarm (106) for alerting users, upon detecting any of the pre-defined parameters in unsafe range. Additionally, the adjustable shoulder strap (134) of the shoulder sub-assembly (102) fasten the exoskeleton suit (100) to the user body.

[0038] Further, the torso sub-assembly (108) is coupled to the center of the shoulder sub-assembly. The torso sub-assembly comprises of a back support strap (110); a body strap for fastening the exoskeleton suit (100) to the user’s body; a first set of sensors (114) integrated with microcontroller on the back support strap; a microcontroller (116) connected to the first set of sensors (114); a counting module (118) operated by the microcontroller (116) for counting the frequency of lift of weight; and a machine learning module (120) operated by the microcontroller (116).

[0039] The back support strap houses the microcontroller (116), the first set of sensor (114), and a communication unit (144). The communication unit (144) wirelessly connects the exoskeleton suit (100) to the plurality of handheld devices. The communication unit (144) comprises of: a Wi-Fi module, a Bluetooth module, an Ethernet module, and a GPRS module. The communication unit (144) is configured for transmitting pre-defined parameters to a plurality of handheld devices , wherein the pre-defined parameters are: weight of load lifted, heart rate, pulse rate, oxygen saturation level (SPO2), number of lift, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength, and body temperature.

[0040] The body strap fasten the exoskeleton suit (100) to the user body. The body strap further activates a time sensor to measure duration of use of the exoskeleton suit (100) by the user, upon fastening of the body strap. The timer is operated by the microcontroller (116). As shown in Figure 4, the time sensor is displayed on a screen of the handheld device.

[0041] The first set of sensors (114) comprises of a pulse oximeter, a torque sensor, a biometric sensor, a temperature sensor, a load cell, a pulse sensor. The pulse oximeter measures oxygen saturation level of blood. Further, the torque sensor is configured to measure gripping strength of user, upon apply force by hand of user. In an exemplary embodiment, the torque sensor may be such as, but not limited to, a dynamometer gripping sensor, strain gauge torque sensor, optical torque sensor, and the alike.

[0042] The biometric sensor is configured to authenticate user for record tracking purpose. The biometric sensor recognize users from a pre-stored set of user through a unique identity/biometric. In an exemplary embodiment, the biometric sensor may be such as, but not limited to, a finger print sensor, face detector, iris detector, gait biometric sensor, and the alike.

[0043] The temperature sensor is configured to measure body temperature of user and the pulse sensor is configured to measure pulse rate. Further, the motion sensor is configured to detect orientation rate on a preset scale in a first range, second range, and third range, wherein the first range indicates user is unfit for work, the second range indicates moderate level of fitness, and the third range indicates user is fit for work. In an exemplary embodiment, the motion sensor may be such as, but not limited to, an inertial measurement unit (IMU), tomographic motion sensors, and the alike.

[0044] The microcontroller (116) is housed on the back support strap (110). Further, the microcontroller (116) is connected to the first set of sensors (114). Additionally, the microcontroller (116) is configured to execute pre-fed instructions stored on a memory housed on the back support strap (110). Further, the grip dynamometer (112) is configured to operate the counting module (118), and the machine learning module (120), wherein the counting module (118), and the machine learning module (120) are pre-fed instructions installed on the memory and executed by the microcontroller (116) based on pre-fed instructions.

[0045] The microcontroller (116) is configured to measure pre-defined parameters through processing of data received through the first set of sensors (114). The microcontroller (116) is further configured to transmit an alert signal to the plurality of handheld devices through the communication unit (144) (128), upon detecting the pre-defined health parameters outside the safe range.

[0046] The counting module (118) is configured to count the number of objects lifted by user. Each time the user picks up a load, the user presses the button on the counting module (118) and the counting module (118) increases the count of lifted object by one. Further, the counting module (118) stores the number of load lifted in the memory. Moreover, the counting module (118) is configured to display the number of load lifted by users on the screen of the handheld devices. The counting module (118) detects the lifting of load through the load cell (104).

[0047] The machine learning module (120) is configured to detect musculoskeletal disorders by analyzing data of the first set of sensors (114) and the second set of sensors (126). The machine learning module (120) pre-process the data, extract features from the data, and trained model using the data. Moreover, the machine learning module (120) is continually retrained in order to improve accuracy.

[0048] Furthermore, each of the leg sub-assembly (122) comprises of a a load cell (104) for measuring weight of load picked up by a user, thigh cuff (124) connected to the height adjustable rods through a spring, clutch and knob assembly; a second set of sensors (126) integrated on the waist belt and connected to the microcontroller (116).

[0049] The load cell (104) is configured to measure the weight of object being lifted. The load cell (104) have a first end and a second end. The first end is secured to a frame or base. When weight of object is applied to body of the load cell (104), the body of the load cell (104) may flex slightly under the strain. To measure the deformation, strain gauges may be tightly bonded to the body of the load cell (104) at predetermined points, causing strain gauges to deform in unison with the body of the load cell (104). The resulting movement alters the electrical resistance of the strain gauges in proportion to the amount of deformation caused by the applied load of shipment. The electrical resistance of the strain gauges may be measured with the resulting signal being output as a weight reading.

[0050] In an exemplary embodiment, the load cell (104) may be selected from such as, but not limited to a pneumatic load cell, a miniature load cell, an s-beam load cell, a compression load cell, a canister load cell, and the like.

[0051] The Thigh cuff (124) is configured to secure the exoskeleton to the user’s thigh. It ensures proper alignment and stability of the exoskeleton suit’s (100) mechanical structure with the user’s body. The thigh cuff (124) is connected to the height adjustable rods through a spring, clutch and knob assembly.

[0052] The spring based system converts Kinetic energy into elastic potential energy & stores in expanders while movement & elastic potential energy back to kinetic energy which supports lifting the upper body & external object or load.

[0053] The clutch is a mechanical clutch that automatically detects whether the lifter is walking or lifting, accordingly turns spring based support mechanism off or on, respectively.

[0054] The knob assembly is used for adjusting the load, by the angle between the load carried by the lifter & waist belt depending upon the working of the tasks & the users preference, the users perceive a supporting force while bending forward.

[0055] The second set of sensors (126) comprising: an accelerometer sensor (128), a PPG sensor (130), and a motion sensor (132). Further, the accelerometer sensor (128) and PPG sensor (130) are configured to measure calorie consumed and walking step of the user. Further, the motion sensor (132) is configured to detect orientation rate on a preset scale in a first range, second range, and third range, wherein the first range indicates user is unfit for work, the second range indicates moderate level of fitness, and the third range indicates user is fit for work. In an exemplary embodiment, the motion sensor may be such as, but not limited to, an inertial measurement unit (IMU), tomographic motion sensors, and the alike.

[0056] The handheld device is configured to receive the pre-defined parameters form the microcontroller (116) through the communication unit (144). Further, as shown in Figure 3 to Figure 12, the handheld device displays the pre-defined parameters on screen. The pre-defined parameters are selected from, weight of load lifted, heart rate, pulse rate, oxygen saturation level (SPO2), number of lifted objects, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength, and body temperature.

[0057] In an embodiment, the present invention also provides a method for operation of the IOT based smart exoskeleton suit, comprises the following steps:-
• authenticating the user through the biometric sensor for storing data of each user separately;
• fastening of the adjustable shoulder strap and the body strap;
• measuring heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature through the first set of sensors (114) and the second set of sensors (126);
• comparing the heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature with pre-set threshold values;
• raising the alarm by transmitting the alert signal to the plurality of handheld devices , upon detecting heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature in the unsafe range;
• providing a rating in the first range, the second range, and the third range on the pre-set scale through the microcontroller (116);
• measuring weight of load lifted, number of lift, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength through the first set of sensors (114) and the second set of sensors (126); and
• displaying the weight of load lifted, number of lift, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength on screen of each of the plurality of the handheld devices.

[0058] In an embodiment the advantages of the present invention are enlisted herein:
• The present invention provides an exoskeleton suit with workers’ safety feature.
• The present invention ensures equitable sharing of work.
• The present provides an exoskeleton suit that is low cost and easy to use.
• The present invention measures physiological parameters of user body and raise an alarm upon detecting any of the physiological parameter in unsafe range.
• The present invention provides an exoskeleton suit that prevents users from musculoskeletal diseases.
• The present invention monitors or capture health status of associates or workers or heavy lifters, which results in increased productivity.
• The present invention measures productivity of worker through measuring weight lifted and frequency of lifting.

[0059] While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
, Claims:WE CLAIM:

1. An IOT based smart exoskeleton suit (100), comprising:
(a) a shoulder sub-assembly (102) with adjustable shoulder strap (134), comprising: , an alarm (106);
(b) a torso sub-assembly (108) coupled to center of the shoulder subassembly (102), the torso sub-assembly (108) comprising:
o a back support strap (110);
o a waist belt connected to the bottom of the torso sub assembly (108);
o a body strap for fastening the exoskeleton suit (112) to the user’s body;
o a first set of sensors (114) integrated on the body strap, comprising: a pulse oximeter, a torque sensor, a biometric sensor, a temperature sensor, a load cell, a pulse sensor;
o a microcontroller (116) connected to the first set of sensors (114);
o a second set of sensors (126) integrated on the waist belt and connected to the microcontroller (116), comprising: an accelerometer sensor (128), a PPG sensor (130), and an motion sensor (132);
o a counting module (118) operated by the microcontroller (116) for counting the frequency of lift;
o a machine learning module (120) operated by the microcontroller (116);
(c) two leg sub-assembly (122) coupled to the torso sub-assembly (108) via height adjustable rods, each of the leg sub-assembly (122) comprising:
o a thigh cuff (124) connected to the height adjustable rods (136) through a spring (138), clutch (140) and knob assembly (142);
o a load cell (104) integrated on the knob assembly, for measuring weight of load picked up by a user;
2. The exoskeleton suit (100) as claimed in claim 1, wherein the exoskeleton suit (100) comprising a communication unit (144) wirelessly connected to a plurality of handheld devices for transmitting pre-defined parameters to the plurality of handheld devices.
3. The exoskeleton suit (100) as claimed in claim 1, wherein the pre-defined parameters are: weight of load lifted, heart rate, pulse rate, oxygen saturation level (SPO2), number of lift, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength, and body temperature.
4. The exoskeleton suit (100) as claimed in claim 2, wherein the communication unit (144) comprises of: a Wi-Fi module, a Bluetooth module, an Ethernet module, and a GPRS module.
5. The exoskeleton suit (100) as claimed in claim 1, wherein the microcontroller (116) is configured to transmit pre-defined health parameters to the plurality of handheld device through the communication unit (144).
6. The exoskeleton suit (100) as claimed in claim 1, wherein the motion sensor is inertial measurement unit (IMU), configured to detect orientation rate on a preset scale in a first range, second range, and third range.
7. The exoskeleton suit (100) as claimed in claim 1, wherein the first range indicates user is unfit for work, the second range indicates moderate level of fitness, and the third range indicates user is fit for work.
8. The exoskeleton suit (100) as claimed in claim 1, wherein the torque sensor is a dynamometer gripping sensor.
9. The exoskeleton suit (100) as claimed in claim 1, wherein the microcontroller (116) is configured to transmit an alert signal to the plurality of handheld devices through the communication unit (144) , upon detecting the pre-defined health parameters outside the safe range.
10. The exoskeleton suit (100) as claimed in claim 1, wherein the machine learning module (120) is configured to detect musculoskeletal disorders through the training on data of the first set of sensors (114) and the second set of sensors (126).
11. The exoskeleton suit (100) as claimed in claim 1, wherein the exoskeleton suit (100) comprises a timer configured to measure duration of use of the exoskeleton suit (100) by the user, upon fastening of the adjustable shoulder strap and the body strap.
12. The exoskeleton suit (100) as claimed in claim 1, wherein the microcontroller (116) is configured to measure calorie consumed and walking step of the user through the accelerometer sensor and pulse sensor.
13. The exoskeleton suit (100) as claimed in claim 1, wherein the biometric sensor is a finger print sensor.
14. A method for operation of the IOT based smart exoskeleton suit, comprising steps of:
a. authenticating the user through the biometric sensor for storing and tracking data of each user separately;
b. fastening of the adjustable shoulder strap and the body strap;
c. measuring heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature through the first set of sensors (114) and the second set of sensors (126);
d. comparing the heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature with pre-set threshold values;
e. raising the alarm by transmitting the alert signal to the plurality of handheld devices , upon detecting heart rate, pulse rate, oxygen saturation level (SPO2), and body temperature in the unsafe range;
f. providing a rating in the first range, the second range, and the third range on the pre-set scale through the microcontroller (116);
g. measuring weight of load lifted, number of lift, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength through the first set of sensors (114) and the second set of sensors (126); and
h. displaying the weight of load lifted, number of lift, time duration of use of exoskeleton suit (100), calorie consumed, step count, gripping strength on screen of each of the plurality of the handheld devices .
15. The method as claimed in claim 14, wherein the method comprise a step of detecting Musculoskeletal disorders through the training of the machine learning module (120) on data of the first set of sensors (114) and the second set of sensors (126).