Predicting Seat-Off and Detecting Start-of-Assistance Events for Assisting Sit-to-Stand With an Exoskeleton

Tanghe, Kevin; Harutyunyan, Anna; Aertbeliën, Erwin; Groote, Friedl De; Schutter, Joris De; Vrancx, Peter; Nowé, Ann

doi:10.1109/lra.2016.2530165

Cited by 20 publications

(19 citation statements)

References 17 publications

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“…The models are adopted depending on the type of sensors that are installed for recording the signals of the gait. Nowadays, wearable sensors are widely used for gait phase recognition systems: Wearable force-based measurements [ 9 , 21 , 26 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ], Electromyographic (EMG) sensors [ 55 , 56 ], Inertial Measurement Units (IMUs) [ 9 , 19 , 29 , 41 , 57 , 58 ], and joint angular sensors [ 24 , 59 , 60 , 61 , 62 ] are used specifically for the detection of the gait. The studies showed that the methods that used force-based measurements such as Force Sensing Resistors (FSRs), footswitches, and foot pressure insoles yield the highest precision for detection [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Dong

Cao

et al. 2020

Sensors

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Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

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Section: Introductionmentioning

confidence: 99%

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Dong

Cao

et al. 2020

Sensors

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“…Kinematic and dynamic data of a healthy 29 year-old subject (gender: male, mass: 70 kg) wearing a bilateral exoskeleton were recorded during sit-to-stand movements. The bilateral exoskeleton was actuated at the ankle, knee, and hip joints with the purpose of assisting subjects with muscle deficiency during sit-to-stand movements [13], [14]. Contact pressures were measured at the contact zones between the subject and exoskeleton.…”

Section: A Experimental Measurementsmentioning

confidence: 99%

Subject-Exoskeleton Contact Model Calibration Leads to Accurate Interaction Force Predictions

Serrancolí

Falisse

Dembia

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Knowledge of human-exoskeleton interaction 1 forces is crucial to assess user comfort and effectiveness 2 of the interaction. The subject-exoskeleton collaborative 3 movement and its interaction forces can be predicted in 4 silico using computational modeling techniques. We devel-5 oped an optimal control framework that consisted of three 6 phases. First, the foot-ground (Phase A) and the subject-7 exoskeleton (Phase B) contact models were calibrated 8 using three experimental sit-to-stand trials. Then, the collab-9 orative movement and the subject-exoskeleton interaction 10 forces, of six different sit-to-stand trials were predicted 11 (Phase C). The results show that the contact models were 12 able to reproduce experimental kinematics of calibration 13 trials (mean root mean square differences (RMSD) coor-14 dinates ≤ 1.1°and velocities ≤ 6.8°/s), ground reaction 15 forces (mean RMSD≤ 22.9 N), as well as the interaction 16 forces at the pelvis, thigh, and shank (mean RMSD ≤ 5.4 N). 17 Phase C could predict the collaborative movements of pre-18 diction trials (mean RMSD coordinates ≤ 3.5°and veloc-19 ities ≤ 15.0°/s), and their subject-exoskeleton interaction 20 forces (mean RMSD ≤ 13.1 N). In conclusion, this optimal 21 control framework could be used while designing exoskele-22 tons to have in silico knowledge of new optimal movements 23 and their interaction forces.

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“…Other events are explained in Table 3. The seat-off event is predicted using statistical methods and is described in more detail in the work of Tanghe et al [54]. Briefly explained, a statistical model is constructed relying on measurements of hip, knee and ankle angles during STS motions of non-disable persons without exoskeleton.…”

Section: Sit-to-stand Exoskeletonmentioning

confidence: 99%

Model-based control for exoskeletons with series elastic actuators evaluated on sit-to-stand movements

Vantilt

Tanghe

Afschrift

et al. 2019

J NeuroEngineering Rehabil

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Background Currently, control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Furthermore, assistance is often provided by imposing these desired trajectories using impedance controllers. However, lower-limb exoskeletons are also a promising solution for mobility problems of individuals in daily life. To develop an assistive exoskeleton which allows the user to be autonomous, i.e. in control of his motions, remains a challenge. This paper presents a model-based control method to tackle this challenge. Methods The model-based control method utilizes a dynamic model of the exoskeleton to compensate for its own dynamics. After this compensation of the exoskeleton dynamics, the exoskeleton can provide a desired assistance to the user. While dynamic models of exoskeletons used in the literature focus on gravity compensation only, the need for modelling and monitoring of the ground contact impedes their widespread use. The control strategy proposed here relies on modelling of the full exoskeleton dynamics and of the contacts with the environment. A modelling strategy and general control scheme are introduced. Results Validation of the control method on 15 non-disabled adults performing sit-to-stand motions shows that muscle effort and joint torques are similar in the conditions with dynamically compensated exoskeleton and without exoskeleton. The condition with exoskeleton in which the compensating controller was not active showed a significant increase in human joint torques and muscle effort at the knee and hip. Motor saturation occurred during the assisted condition, which limited the assistance the exoskeleton could deliver. Conclusions This work presents the modelling steps and controller design to compensate the exoskeleton dynamics. The validation seems to indicate that the presented model-based controller is able to compensate the exoskeleton.

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Predicting Seat-Off and Detecting Start-of-Assistance Events for Assisting Sit-to-Stand With an Exoskeleton

Cited by 20 publications

References 17 publications

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Subject-Exoskeleton Contact Model Calibration Leads to Accurate Interaction Force Predictions

Model-based control for exoskeletons with series elastic actuators evaluated on sit-to-stand movements

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