Voluntary gait speed adaptation for robot-assisted treadmill training

Koenig,; Binder,; Zitzewitz,; Omlin, Ximena; Bolliger, Marc; Riener,

doi:10.1109/icorr.2009.5209591

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…von Zitzewitz et al [16] developed a voluntary speed adaptation controller of a treadmill with robotic gait orthosis, Lokomat, which can allow patient-cooperative control by measuring horizontal interaction forces through a mechanical tether connected to the trunk, similar to the Sarcos Tread port [17]. Koenig et al [18] updated the algorithm to include speed adaptation for severe patients using swing leg forces. Even though mechanical tethers [16,17] can increase safety during patient training with fixed positioning, they may limit natural variability of walking in highly ambulatory patients due to motion constraints by the mechanical characteristics [19] of a tether or exoskeleton robot.…”

Section: Introductionmentioning

confidence: 99%

A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

Yoon

Park

Damiano

2012

J NeuroEngineering Rehabil

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BackgroundVirtual reality (VR) technology along with treadmill training (TT) can effectively provide goal-oriented practice and promote improved motor learning in patients with neurological disorders. Moreover, the VR + TT scheme may enhance cognitive engagement for more effective gait rehabilitation and greater transfer to over ground walking. For this purpose, we developed an individualized treadmill controller with a novel speed estimation scheme using swing foot velocity, which can enable user-driven treadmill walking (UDW) to more closely simulate over ground walking (OGW) during treadmill training. OGW involves a cyclic acceleration-deceleration profile of pelvic velocity that contrasts with typical treadmill-driven walking (TDW), which constrains a person to walk at a preset constant speed. In this study, we investigated the effects of the proposed speed adaptation controller by analyzing the gait kinematics of UDW and TDW, which were compared to those of OGW at three pre-determined velocities.MethodsTen healthy subjects were asked to walk in each mode (TDW, UDW, and OGW) at three pre-determined speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s) with real time feedback provided through visual displays. Temporal-spatial gait data and 3D pelvic kinematics were analyzed and comparisons were made between UDW on a treadmill, TDW, and OGW.ResultsThe observed step length, cadence, and walk ratio defined as the ratio of stride length to cadence were not significantly different between UDW and TDW. Additionally, the average magnitude of pelvic acceleration peak values along the anterior-posterior direction for each step and the associated standard deviations (variability) were not significantly different between the two modalities. The differences between OGW and UDW and TDW were mainly in swing time and cadence, as have been reported previously. Also, step lengths between OGW and TDW were different for 0.5 m/s and 1.5 m/s gait velocities, and walk ratio between OGS and UDW was different for 1.0 m/s gait velocities.ConclusionsOur treadmill control scheme implements similar gait biomechanics of TDW, which has been used for repetitive gait training in a small and constrained space as well as controlled and safe environments. These results reveal that users can walk as stably during UDW as TDW and employ similar strategies to maintain walking speed in both UDW and TDW. Furthermore, since UDW can allow a user to actively participate in the virtual reality (VR) applications with variable walking velocity, it can induce more cognitive activities during the training with VR, which may enhance motor learning effects.

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Section: Introductionmentioning

confidence: 99%

A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

Yoon

Park

Damiano

2012

J NeuroEngineering Rehabil

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“…Von Zitzewitz et al [6] and Christensen et al [7] implemented treadmill velocity adaptation using anterior-posterior force component of the ground reaction forces between the subject's feet and the ground. Koenig et al [8] used swing leg forces of an exoskeleton to accelerate and decelerate the treadmill. De Luca et al [9] and Souman et al [10] designed control algorithms for an omni-directional (2DOF) treadmill.…”

Section: Introductionmentioning

confidence: 99%

“…Some other approaches [6][7][8] require additional mechanisms such as a mechanical tether or an exoskeleton in order to acquire kinetic data during locomotion. Very recently, De Luca et al [9] and Souman et al [10] offered an advanced control algorithm for an omni-directional treadmill with a variable reference position and a velocity observer.…”

Section: Introductionmentioning

confidence: 99%

Speed adaptation control of a small-sized treadmill with state feedback controller

Manurung

Yoon

Park³

2010

2010 3rd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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Variable velocity in treadmill training may play an important role in gait rehabilitation where a patient can practice variable walking velocity while the treadmill adjusts to it. In order to adjust the treadmill velocity to the subject's variable walking velocity, we have developed an automatic gait velocity adaptation algorithm implemented on a small-sized commercial treadmill (belt length of 1.2 m and width of 0.5 m) which is widely used at home and health centers. The control objective is to automatically adjust the treadmill velocity so that the subject's position is maintained within the track when the subject walks at a variable velocity. The subject's position with respect to a reference point is measured by a low-cost sonar sensor located on the back of the subject. Based on a encoder sensor measurement at the treadmill motor, a state feedback control algorithm with Kalman filter was implemented to determine the velocity of the treadmill. In order to reduce the unnatural inertia force felt by the subject, a predefined acceleration limit was applied, which generated smooth velocity trajectories. The experimental results demonstrate the effectiveness of the proposed method in providing successful velocity changes in response to variable velocity walking including starting (accelerating) and stopping (decelerating) periods of treadmill exercise without causing significant inertia force to the subject. In the pilot study with three subjects, users could change their walking velocity easily and naturally with small deviations during slow, medium, and fast walking. The proposed automatic velocity adaptation algorithm can potentially be applied to any locomotion interface in an economical way without having to use sophisticated and expensive sensors and larger treadmills.

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“…The patients with disability desire to have the ability to control their walking speed with assistance from a rehabilitative robotic system. This can encourage a patient to actively engage in rehabilitation training (Koenig et al, 2009;Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Processing Surface EMG Signals for Exoskeleton Motion Control

et al. 2020

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The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.

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Voluntary gait speed adaptation for robot-assisted treadmill training

Cited by 14 publications

References 13 publications

A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

Speed adaptation control of a small-sized treadmill with state feedback controller

Processing Surface EMG Signals for Exoskeleton Motion Control

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