A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

Torricelli, Diego; Cortés, Camilo; Lete, Nerea; Bertelsen, Álvaro; González-Vargas, José; del-Ama, Antonio J.; Dimbwadyo, I.; Moreno, Juan C.; Flórez, Jesús; Pons, José L.

doi:10.3389/fnbot.2018.00018

Cited by 32 publications

(25 citation statements)

References 22 publications

(24 reference statements)

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“…Although these mechanisms will add inertia and bulk to the device, undesired interaction forces and torques can be significantly reduced, with an impact on perceived comfort [18]. Overall, relative motion of interfaces should be minimized as to optimize comfort, efficiency, and reliability of exoskeletons [24].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Effects of Strapping Pressure on Human-Robot Interface Dynamics Using a Soft Robotic Cuff

Langlois

Rodriguez-Cianca

Serrien

et al. 2021

IEEE Trans. Med. Robot. Bionics

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Physical human-robot interfaces are a challenging aspect of exoskeleton design, mainly due to the fact that interfaces tend to migrate relatively to the body leading to discomfort and power losses. Therefore, the key is to develop interfaces that optimize attachment stiffness, i.e. reduce relative motion, without compromising comfort. To that end, we propose a method to obtain the optimal attachment pressure in terms of connection stiffness and comfort. The method is based on a soft robotic interface capable of actively controlling strapping pressure which is coupled to a cobot. Hereby the effects of strapping pressure on energetic losses, connection stiffness, and perceived comfort are analyzed. Results indicate a trade-off between connection stiffness and perceived comfort for this type of interface. An optimal strapping pressure was found in the 50 to 80 mmHg range. Connection stiffness was found to increase linearly over a pressure range from 0 mmHg (stiffness of 1139 N/m) to 100 mmHg (stiffness of 2232 N/m). And energetic losses were reduced by 42% by increasing connection stiffness. This research highlights the importance of strapping pressure when attaching an exoskeleton to a human and introduces a new adaptive interface to improve the coupling from an exoskeleton to an individual.

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Section: Introductionmentioning

confidence: 99%

Investigating the Effects of Strapping Pressure on Human-Robot Interface Dynamics Using a Soft Robotic Cuff

Langlois

Rodriguez-Cianca

Serrien

et al. 2021

IEEE Trans. Med. Robot. Bionics

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show abstract

“…A known future gait trajectory adds a feedforward term to powered exoskeleton devices instead of predominantly relying on feedback sensors [ 1 , 27 , 29 , 69 ]. This would improve device performance by narrowing down the nonlinear kinematic differences between the user and the device and therefore avoid altering the user’s natural gait trajectories [ 70 , 71 ]. Prediction of future gait trajectory could substantially improve the design of prosthetics by adapting the device controlling parameters according to the user’s movement [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of gait trajectories based on the Long Short Term Memory neural networks

et al. 2021

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The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82–5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

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“…As expected, the intra-subject implementation shows better performance in both gait trajectory and gait phase prediction than inter-subject implementation, which suggests that subject-specific training is preferable if the gait trajectory and gait phase are to be used for an exoskeleton framework. Although a generic model can reduce the time required in the training process, some studies have shown that a generic model may not be suitable for specific individuals, and that subject-specific musculoskeletal geometry is essential for high prediction accuracy [ 44 , 45 ]. One advantage of our approach is that no anthropometric geometry measurements are needed in either implementation.…”

Section: Discussionmentioning

confidence: 99%

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Gutierrez-Farewik

2020

Sensors

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Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.

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A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

Cited by 32 publications

References 22 publications

Investigating the Effects of Strapping Pressure on Human-Robot Interface Dynamics Using a Soft Robotic Cuff

Investigating the Effects of Strapping Pressure on Human-Robot Interface Dynamics Using a Soft Robotic Cuff

Prediction of gait trajectories based on the Long Short Term Memory neural networks

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

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