Online gait task recognition algorithm for hip exoskeleton

Jang, Junwon; Kim, Kyungrock; Lee, Jusuk; Lim, Byeonghwa; Shim, Youngbo

doi:10.1109/iros.2015.7354129

Cited by 53 publications

(46 citation statements)

References 20 publications

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“…The kinematics inter-subject variability can indeed affect the LMR performance, with thresholding being inherently sensitive to minor variations of the recorded kinematics. A work similar to ours was presented in the literature for an active pelvis orthosis [26]. Our controller demonstrated a higher performance.…”

Section: B Performance Of the Lmr Processsupporting

confidence: 65%

“…Therefore, commercial lower-limb orthoses for patients with spinal cord injury are often controlled monitoring mechanical feature of the human-exoskeleton system, such as the tilting of the trunk [24], [25]. The most advanced approach for the LMR with lower-limb active orthoses is presented in a recent study [26], to identify locomotion-related activities of daily living in healthy or mildly impaired people with residual movement capabilities. The algorithm is based on a fuzzy-logic classifier operating on signals acquired from the onboard mechanical sensors (hip joint potentiometers) and an IMU (for foot contact detection) integrated in the backpack of a fully portable hip exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Parri

Yuan

Marconi

et al. 2017

IEEE/ASME Trans. Mechatron.

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Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for realtime locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a timebased approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subjectindependent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities.

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Section: B Performance Of the Lmr Processsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Parri

Yuan

Marconi

et al. 2017

IEEE/ASME Trans. Mechatron.

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show abstract

“…To assist lower limb motion and overcome the mentioned limitations, we developed various kinds of compact and lightweight exoskeletons for elderly people in our previously reported studies [ 13 , 25 , 26 , 27 ]. The exoskeletons included partially assisting devices for only the hip and ankle and a fully assisting device for all of the joints in the lower limbs.…”

Section: Introductionmentioning

confidence: 99%

Compact Hip-Force Sensor for a Gait-Assistance Exoskeleton System

Choi

Seo

Hyung

et al. 2018

Sensors

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In this paper, we propose a compact force sensor system for a hip-mounted exoskeleton for seniors with difficulties in walking due to muscle weakness. It senses and monitors the delivered force and power of the exoskeleton for motion control and taking urgent safety action. Two FSR (force-sensitive resistors) sensors are used to measure the assistance force when the user is walking. The sensor system directly measures the interaction force between the exoskeleton and the lower limb of the user instead of a previously reported force-sensing method, which estimated the hip assistance force from the current of the motor and lookup tables. Furthermore, the sensor system has the advantage of generating torque in the walking-assistant actuator based on directly measuring the hip-assistance force. Thus, the gait-assistance exoskeleton system can control the delivered power and torque to the user. The force sensing structure is designed to decouple the force caused by hip motion from other directional forces to the sensor so as to only measure that force. We confirmed that the hip-assistance force could be measured with the proposed prototype compact force sensor attached to a thigh frame through an experiment with a real system.

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“…Being a noncausal algorithm, this procedure could not be implemented in real-time and had to be implemented during postprocessing of the data since future input is needed for the algorithm. Jang et al [18] measured hip joint angles of both legs and signals from IMUs at the moment of foot contact to recognize level ground, stair ascent, or stair descent by using a hip exoskeleton. This algorithm had a one-step delay, such that the first step transitioning into a new mode was always unrecognized.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, existing gait mode recognition schemes have shortcomings of not being autonomous [4, 5], delay in detection of a new mode until the next step [16, 18], difficulty of implementation in real-time [6–10, 17], dependency on trained stair heights [16], or need for a large number of sensors [12–15, 19, 20]. There are currently no reliable gait mode recognition algorithms available which can detect all of the modes without long delays and without the use of a large number of sensors.…”

Section: Introductionmentioning

confidence: 99%

Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis

Islam

Hsiao-Wecksler

2016

Journal of Biophysics

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This paper presents an algorithm, for use with a Portable Powered Ankle-Foot Orthosis (i.e., PPAFO) that can automatically detect changes in gait modes (level ground, ascent and descent of stairs or ramps), thus allowing for appropriate ankle actuation control during swing phase. An artificial neural network (ANN) algorithm used input signals from an inertial measurement unit and foot switches, that is, vertical velocity and segment angle of the foot. Output from the ANN was filtered and adjusted to generate a final data set used to classify different gait modes. Five healthy male subjects walked with the PPAFO on the right leg for two test scenarios (walking over level ground and up and down stairs or a ramp; three trials per scenario). Success rate was quantified by the number of correctly classified steps with respect to the total number of steps. The results indicated that the proposed algorithm's success rate was high (99.3%, 100%, and 98.3% for level, ascent, and descent modes in the stairs scenario, respectively; 98.9%, 97.8%, and 100% in the ramp scenario). The proposed algorithm continuously detected each step's gait mode with faster timing and higher accuracy compared to a previous algorithm that used a decision tree based on maximizing the reliability of the mode recognition.

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Online gait task recognition algorithm for hip exoskeleton

Cited by 53 publications

References 20 publications

Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Compact Hip-Force Sensor for a Gait-Assistance Exoskeleton System

Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis

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