Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

Kolaghassi, Rania; Al-Hares, Mohamad Kenan; Sirlantzis, Konstantinos

doi:10.1109/access.2021.3104464

Cited by 28 publications

(19 citation statements)

References 108 publications

(134 reference statements)

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“…After obtaining the data of the position of the exoskeleton links for slow, normal, and fast speeds, in consideration of study [20], this study was conducted with healthy patients and 20 gait cycles were obtained for each speed and were then analyzed using Pearson's correlation coefficient and the Lin concordance coefficient.…”

Section: Resultsmentioning

confidence: 99%

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Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton

Calle-Siguencia

Callejas-Cuervo

García-Reino

2022

Sensors

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Motion assistance exoskeletons are designed to support the joint movement of people who perform repetitive tasks that cause damage to their health. To guarantee motion accompaniment, the integration between sensors and actuators should ensure a near-zero delay between the signal acquisition and the actuator response. This study presents the integration of a platform based on Imocap-GIS inertial sensors, with a motion assistance exoskeleton that generates joint movement by means of Maxon motors and Harmonic drive reducers, where a near zero-lag is required for the gait accompaniment to be correct. The Imocap-GIS sensors acquire positional data from the user’s lower limbs and send the information through the UDP protocol to the CompactRio system, which constitutes a high-performance controller. These data are processed by the card and subsequently a control signal is sent to the motors that move the exoskeleton joints. Simulations of the proposed controller performance were conducted. The experimental results show that the motion accompaniment exhibits a delay of between 20 and 30 ms, and consequently, it may be stated that the integration between the exoskeleton and the sensors achieves a high efficiency. In this work, the integration between inertial sensors and an exoskeleton prototype has been proposed, where it is evident that the integration met the initial objective. In addition, the integration between the exoskeleton and IMOCAP is among the highest efficiency ranges of similar systems that are currently being developed, and the response lag that was obtained could be improved by means of the incorporation of complementary systems.

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Section: Resultsmentioning

confidence: 99%

“…All of these sensors offer the capacity for combination with artificial intelligence (AI) and to be able to predict the movements that the exoskeleton wearer will perform, thus training the controller [20].…”

Section: Introductionmentioning

confidence: 99%

Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton

Calle-Siguencia

Callejas-Cuervo

García-Reino

2022

Sensors

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“…Human walking is based on significant events that occur during walking, which form a cycle of gait. The duration of the different phases is measured as a percentage of the cycle duration [14,[17][18][19]. A gait cycle consists of two steps: double support (DS) and simple support (SS), as shown in Fig.…”

Section: Duration Of Different Phasesmentioning

confidence: 99%

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

Wajiansyah

Malani

Supriadi

et al. 2022

Journal of Robotics and Control

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The Indonesian Robot Dance Contest (KRSTI) is a branch of the Indonesian Robot Contest (KRI) with the theme of dance. The robot used is a humanoid robot that can dance. Every year at the event, the provisions for robots constantly change, both the type of dance being demonstrated and the requirements for the robot's height. The taller the robot, the more difficult it is to control its walking movements because of the load it carries. This study uses a suitable algorithm to make the walking movement more natural and minimize the robot's falling. Human ROM data is used as a parameter for the range of motion of the servos that act as joints in the robot's legs. The algorithm created serves to determine the initial position of the angle on the servo to avoid the wrong initial movement position between one servo and another. The robot used is the Bioloid Robot’s leg Type A and uses OpenCM 9.04-A as the controller. The results showed that ROM on human feet could not be fully implemented on robot legs due to the robot's structure and the need for a robot that only relies on an algorithm to find the correct fulcrum to maintain balance. The comparison results show that the movement when walking on the ankle (ID servo 15) ranges from 749-567, while the ROM range is only between 580-512. When walking (servo ID 16), movement ranges from 460-291, while the ROM range ranges from 580-512.

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“…Some datasets do exist but are quite limited, such as only using one accelerometer to measure trajectories [23], solely measuring force data from a balance board [24], or using unnatural or 'perturbed' motions [25]. Also uncommon are datasets containing data from users with pathological conditions [26]. Another limitation of many existing studies is the focus on sit-to-stand without including stand-to-sit, an equally important transfer.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these recorded data, a trajectory prediction algorithm can be applied to retrieve the estimated motion of a user. However, though trajectory prediction has been widely documented for walking [26], it is very scarce for STSTS movements. Previous works such as [28], have used cost functions to generate trajectories for different groups of individuals and even applied them to an assistive device achieving high success rates [29].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

Bennett

Kumar

Garate

2022

Sensors

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Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perform these motions can be severely impaired by different factors, such as the occurrence of a stroke, limiting the ability to engage in other daily activities. This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression models were then used on two different sets of key participant parameters (height and weight, and BMI and age), to produce a predicted trajectory. Resulting trajectories matched the true ones for non-stroke subjects with an average R2 score of 0.864±0.134 using k = 3 and 100% seat height when using height and weight parameters. Even among a small sample of stroke patients, balance and motion trends were noticed along with a large within-class variation, showing that larger scale trials need to be run to obtain significant results. The full dataset of sit-to-stand-to-sit actions for each user is made publicly available for further research.

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Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

Cited by 28 publications

References 108 publications

Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton

Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

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