Switching Assistance for Exoskeletons During Cyclic Motions

Tagliamonte, Nevio Luigi; Valentini, Simona; Sudano, Angelo; Portaccio, Iacopo; Leonardis, Chiara De; Formica, Domenico; Accoto, Dino

doi:10.3389/fnbot.2019.00041

Cited by 6 publications

(5 citation statements)

References 27 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies that actuated the knee joint provided additional passive supports at either ankle or hip. [171][172][173][174][175][176][177][178] The results showed reductions when walking with the powered mode as compared with the minimal impedance mode for the soleus in stance phase, 173 the biceps femoris in swing phase, 171 and rectus femoris, tibialis anterior, gastrocnemius lateralis, and semitendinosus 172 during stance and swing phase. In minimal impedance mode, also called "free" or "transparent" mode, the exo attempts to be transparent to the user and minimally impact walking biomechanics either by providing no torque or actively controlling for minimal interaction torque.…”

Section: Active Actuation Of Knee or Hip Jointmentioning

confidence: 95%

“…176 Studies also evaluated knee exoskeletons in other tasks, such as squatting 177 or knee flexion-extension. 175,178 A knee exoskeleton also reduced knee extensor muscle activity when using a controller that was capable of injecting the minimal amount of energy needed to support oscillations of the knee. 175 Passive springs placed anteriorly on the hip stored and released energy, thereby reducing plantar flexor activity during walking.…”

Section: Active Actuation Of Knee or Hip Jointmentioning

confidence: 99%

“…175,178 A knee exoskeleton also reduced knee extensor muscle activity when using a controller that was capable of injecting the minimal amount of energy needed to support oscillations of the knee. 175 Passive springs placed anteriorly on the hip stored and released energy, thereby reducing plantar flexor activity during walking. 179 Actuated hip exoskeletons increased muscle activity of tibialis anterior, rectus femoris, and gastrocnemius medialis in the minimal impedance mode, whereas that of the semitendinosus reduced.…”

Section: Active Actuation Of Knee or Hip Jointmentioning

confidence: 99%

See 2 more Smart Citations

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Mahdian,

Wang,

Refai

et al. 2023

Journal of Applied Biomechanics

View full text Add to dashboard Cite

Lower limb exoskeletons and exosuits (“exos”) are traditionally designed with a strong focus on mechatronics and actuation, whereas the “human side” is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human–exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human–machine system that optimizes the biological and electromechanical performance of the combined system.

show abstract

Section: Active Actuation Of Knee or Hip Jointmentioning

confidence: 95%

Section: Active Actuation Of Knee or Hip Jointmentioning

confidence: 99%

Section: Active Actuation Of Knee or Hip Jointmentioning

confidence: 99%

See 1 more Smart Citation

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Mahdian,

Wang,

Refai

et al. 2023

Journal of Applied Biomechanics

View full text Add to dashboard Cite

show abstract

“…Extracting the gait motion characteristics helps in detecting gait deviations which can be an indication of a possibility of tripping, slipping, or balance loss [ 19 , 20 , 21 , 22 , 23 ]. Alternatively, it can compensate for the delay of the response time of the control system [ 24 , 25 , 26 ]. Moreover, the lower limb’s future trajectory prediction can be used to solve numerous problems facing robotic lower limb prothesis/orthosis.…”

Section: Introductionmentioning

confidence: 99%

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

Karakish

Fouz

Elsawaf

2022

Sensors

View full text Add to dashboard Cite

Achieving a normal gait trajectory for an amputee’s active prosthesis is challenging due to its kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase segmentation are essential parameters in controlling an active prosthesis. Recently, the most practiced algorithm in gait trajectory generation is the neural network. Deploying such a complex Artificial Neural Network (ANN) algorithm on an embedded system requires performing the calculations on an external computational device; however, this approach lacks mobility and reliability. In this paper, more simple and reliable ANNs are investigated to be deployed on a single low-cost Microcontroller (MC) and hence provide system mobility. Two neural network configurations were studied: Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN); the models were trained on shank and foot IMU data. The data were collected from four subjects and tested on a fifth to predict the trajectory of 200 ms ahead. The prediction was made for two cases: with and without providing the current phase of the gait. Then, the models were deployed on a low-cost microcontroller (ESP32). It was found that with fewer data (excluding the current gait phase), CNN achieved a better correlation coefficient of 0.973 when compared to 0.945 for MLP; when including the current phase, both network configurations achieved better correlation coefficients of nearly 0.98. However, when comparing the execution time required for the prediction on the intended MC, MLP was much faster than CNN, with an execution time of 2.4 ms and 142 ms, respectively. In summary, it was found that when training data are scarce, CNN is more efficient within the acceptable execution time, while MLP achieves relative accuracy with low execution time with enough data.

show abstract

“…Thus, a vital function for controlling LLEs during walking is to generate a gait trajectory, i.e., movement of the lower limb joints and segments. If a gait trajectory can be predicted and incorporated into the control algorithm, it may be beneficial to add a feed-forward component to compensate for the delay that may result in the control’s response time [ 2 , 3 , 4 ]. Both model-based and machine learning methods are widely studied approaches for gait trajectory prediction.…”

Section: Introductionmentioning

confidence: 99%

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Gutierrez-Farewik

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Switching Assistance for Exoskeletons During Cyclic Motions

Cited by 6 publications

References 27 publications

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Contact Info

Product

Resources

About