IMU-Based Locomotion Mode Identification for Transtibial Prostheses, Orthoses, and Exoskeletons

Gao, Fei; Liu, Gaoyu; Liang, Feng-Yan; Liao, Wei-Hsin

doi:10.1109/tnsre.2020.2987155

Cited by 44 publications

(36 citation statements)

References 60 publications

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“…The evaluation method also influenced the numeric recognition results. For instance, the recent studies on IMU-based locomotion mode recognition achieved >95% average recognition accuracies with intrasubject crossvalidation [21,22]. The sensor setups are quite different from ours.…”

Section: Discussionmentioning

confidence: 65%

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Locomotion Mode Recognition with Inertial Signals for Hip Joint Exoskeleton

Zeng

Cheng

et al. 2021

Applied Bionics and Biomechanics

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Recognizing locomotion modes is a crucial step in controlling lower-limb exoskeletons/orthoses. Our study proposed a fuzzy-logic-based locomotion mode/transition recognition approach that uses the onrobot inertial sensors for a hip joint exoskeleton (active pelvic orthosis). The method outputs the recognition decisions at each extreme point of the hip joint angles purely relying on the integrated inertial sensors. Compared with the related studies, our approach enables calibrations and recognition without additional sensors on the feet. We validated the method by measuring four locomotion modes and eight locomotion transitions on three able-bodied subjects wearing an active pelvic orthosis (APO). The average recognition accuracy was 92.46% for intrasubject crossvalidation and 93.16% for intersubject crossvalidation. The average time delay during the transitions was 1897.9 ms (28.95% one gait cycle). The results were at the same level as the related studies. On the other side, the study is limited in the small sample size of the subjects, and the results are preliminary. Future efforts will be paid on more extensive evaluations in practical applications.

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Section: Discussionmentioning

confidence: 65%

“…The sensor setups are quite different from ours. The study mounted an IMU board on the amputated foot for terrain identification [21], while the study fixed the IMU boards on the shanks, the waist, and wrists [22].…”

Section: Discussionmentioning

confidence: 99%

Locomotion Mode Recognition with Inertial Signals for Hip Joint Exoskeleton

Zeng

Cheng

et al. 2021

Applied Bionics and Biomechanics

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“…This method was more effective than linear discriminant analysis (LDA) when using nonsubject-specific training data of three locomotion modes from an IMU wore on the thigh. Gao [28] proposed a terrain geometry-based algorithm with an elliptical boundary as trigger condition using the inclination grade of the ground to classify five locomotion modes with an IMU estimating the foot trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…However, using too many sensors on wearable robotic system, especially power lower limb prosthesis, will impose extra burden on the user's body and affect the effectiveness of movement. In addition, when using a small amount of sensors like an IMU, the number of locomotion modes increases and similar locomotion modes occur, e.g., level walking at different speeds [27] and ramp motions at small angles [28] may lead to significant errors and reduced model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Han

Liu

Yan

et al. 2021

Sensors

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Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.

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“…In recent years, a branch of the research activity in this field focused on the gait identification problem, managed through the use of different sensors like, for example, Inertial Measurement Unit (IMU) and force-sensitive resistor. On the basis of the acquired data the Authors of [2], [3] were able to determine if the exoskeleton is walking on a straight ground, on a ramp, or on a staircase. In [4], the actual gait phase was identified.…”

Section: Introductionmentioning

confidence: 99%