Exoskeleton: The Friend of Mankind in context of Rehabilitation and Enhancement

Agrawal, Aaditya; Dube, Abhishek N.; Kansara, Devanshi; Shah, Saloni; Sheth, Saurin

doi:10.17485/ijst/2016/v9is1/100889

Cited by 16 publications

(13 citation statements)

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“…Smart clothing includes thin flex batteries and sensors which read body movements to understand current events in the surrounding in the war [13]. Exo skeleton is a wearable device of an external selection that supports and protects the human body [14]. Table 1 presents the products of the wearable device for wellness in the military.…”

Section: B Wellnessmentioning

confidence: 99%

Wearable Computing for Defence Automation: Opportunities and Challenges in 5G Network

Sharma

Park

et al. 2020

IEEE Access

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Recently, wearable technologies have evolved in the most unexpected field like a military force outside of healthcare, fitness, lifestyle and similar areas. The growing need for soldiers' coordination, training and health, the increase in asymmetric warfare, suspected geopolitical conflicts and soldiers' modernization programs, among others, are some of the factors that fuel the growth of the military wearables market. Wearable computation plays an important role in improving the capabilities of the soldier. Further, the 5G network promises a solution to the many network and performance challenges in order to adopt more sophisticated wearable technologies in defense automation. In this paper, we conduct a study to identify the role of wearable computing for the defence automation system. We present the taxonomy of wearable computing in defence automation system and explain the relationship of each attribute. In addition, we identify the raise issues and challenges in communication and cybersecurity when deploying the 5G network in defense automation. Furthermore, we propose the design of the wearable smartwatch architecture as a use case of healthcare transformation in defense automation in the 5G environment. INDEX TERMS Wearable computing, 5G network, security and privacy, edge computing.

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Section: B Wellnessmentioning

confidence: 99%

Wearable Computing for Defence Automation: Opportunities and Challenges in 5G Network

Sharma

Park

et al. 2020

IEEE Access

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“…2019, 9, x FOR PEER REVIEW 4 of 10 "Controller" process contains the control algorithm of the lower limb exoskeleton as shown in Equation ( 4). The "Self-adaption" process is the adaptive parameter learning module, as shown in Equation (5). In the block diagram, "Phi" is obtained by the adaptive parameter learning process.…”

Section: Validation Experimentsmentioning

confidence: 99%

“…Lower limb exoskeleton robot is a mechanical device to improve a human's "physical strength" [1,2]. It can assist aged and disabled citizens, rehabilitate injured patients, and extend the capacity of military and engineering forces [3][4][5][6]. As the lower limb exoskeleton should move synchronously with human legs, accurate tracking of human gait is particularly important for its performance [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

2019

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The lower limb exoskeleton is a wearable human–robot interactive equipment, which is tied to human legs and moves synchronously with the human gait. Gait tracking accuracy greatly affects the performance and safety of the lower limb exoskeletons. As the human–robot coupling systems are usually nonlinear and generate unpredictive errors, a conventional iterative controller is regarded as not suitable for safe implementation. Therefore, this study proposed an adaptive control mechanism based on the iterative learning model to track the single leg gait for lower limb exoskeleton control. To assess the performance of the proposed method, this study implemented the real lower limb gait trajectory that was acquired with an optical motion capturing system as the control inputs and assessment benchmark. Then the impact of the human–robot interaction torque on the tracking error was investigated. The results show that the interaction torque has an inevitable impact on the tracking error and the proposed adaptive iterative learning control (AILC) method can effectively reduce such error without sacrificing the iteration efficiency.

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“…Another milestone in the field of assistive technology is the introduction of hoist therapeutic device which aims at providing comprehensive therapy for the muscles and hence, proceeded with supervised and balance training. Therefore, it helps in augmenting the control of this system manually [22]. On considering the concerns raised on the availability of such assistive devices that focus mainly on the physical movements, the most effective solution to all the problems imposed by various assistive technologies is the smart exoskeleton, designed to help in rehabilitation and empower the paralyzed for a better lifestyle [23].…”

Section: Introductionmentioning

confidence: 99%

AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

et al. 2021

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In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities.

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Exoskeleton: The Friend of Mankind in context of Rehabilitation and Enhancement

Cited by 16 publications

References 0 publications

Wearable Computing for Defence Automation: Opportunities and Challenges in 5G Network

Wearable Computing for Defence Automation: Opportunities and Challenges in 5G Network

Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

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