Real-Time On-Board Recognition of Locomotion Modes for an Active Pelvis Orthosis

Cheng, Gong; Xu, Dongfang; Zhou, Zhihao; Vitiello, Nicola; Wang, Qining

doi:10.1109/humanoids.2018.8625044

Cited by 10 publications

(39 citation statements)

References 17 publications

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“…A possible explanation for this is that, in some particular cases, like accidentally stepping out with an unbalanced stride, the human body will adjust naturally to avoid falling down, resulting in unusual changes in the EMG and GRF patterns and possible misjudgment of the classifier. This phenomenon (i.e., the error rate of recognizing RD into LW) is higher than the overall error rate and is also found in relevant works [27][28][29] (a comparison is given in Table II). A potential solution is to enlarge the volume of the training dataset.…”

Section: Resultssupporting

confidence: 59%

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Fang¹,

Wang²,

Gao³

2020

Preprint

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In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

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Section: Resultssupporting

confidence: 59%

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Fang¹,

Wang²,

Gao³

2020

Preprint

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“…Ai et al 2017 [9] 70.8% TT (4)/Healthy (1) Ankle Prosthesis Beil et al 2018 [10] 90.9% Healthy (10) Exoskeleton Chen et al 2013 [11] 72.7% TT (5)/Healthy (8) Ankle Prosthesis Chen et al 2014 [12] 79.2% TT (1)/Healthy (7) Ankle Prosthesis Chen et al 2015 [13] 77.3% TT (1)/Healthy (5) Ankle Prosthesis Du et al 2012 [14] 75.0% TF (9) Ankle Knee Prosthesis Du et al 2013 [15] 45.8% TF (4) Ankle Knee Prosthesis Feng et al 2019 [16] 77.3% TT (3) Ankle Prosthesis Godiyal et al 2018 [17] 86.4% TF (2)/Healthy (8) Ankle Knee Prosthesis Gong et al 2018 [18] 86.4% Healthy (1) Orthosis Gong et al 2020 [19] 86.4% Healthy (3) Orthosis Hernandez et al 2012 [20] 37.5% TF (1) Ankle Knee Prosthesis Hernandez et al 2013 [21] 54.2% Healthy (1) Ankle Knee Prosthesis Huang et al 2009 [22] 81.8% TF (2)/Healthy (8) Ankle Knee Prosthesis Huang et al 2010 [23] 79.2% TF (1)/Healthy (5) Ankle Knee Prosthesis Huang et al 2011 [24] 83.3% TF (5) Ankle Knee Prosthesis Kim et al 2017 [25] 63.6% Healthy (8) Exoskeleton Liu et al 2016 [26] 70.8% TF (1)/Healthy (6) Ankle Knee Prosthesis…”

Section: Quality Score Groups (N) Locomotion Assistive Devicementioning

confidence: 99%

“…Finally, several feature reduction techniques were sometimes used to find the minimal feature set necessary for successful classification and to avoid overfitting (N = 14): Wrapper techniques such as Sequential Forward Selection (SFS) and Selection Backward Selection (SBS) were used to pick the features having the highest impact on the classification accuracy [39] (N = 8). Such methods are time consuming [18]. Zhang et al [57] compared the processing time taken by two wrapper methods and a filter method.…”

Section: Influence Of Featuresmentioning

confidence: 99%

“…An obvious thing to add on the list would be the inclusion of more daily life conditions for testing the devices. Examples of these would be different angles of approaches towards stairs or slope [9,28], different staircases [18] or load bearing changes [51]. A good extension for many of the studies included in this review would be a test of the algorithms outside the laboratory.…”

Section: Recommendations For Generalization To Daily Life Conditionsmentioning

confidence: 99%

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Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Labarrière

Thomas

Calistri³

et al. 2020

Sensors

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Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

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“…The method only relied on the inertial sensors integrated into the exoskeleton, and no additional sensors were required on the human body. Compared with the previous works using the same exoskeleton [14,20], one improvement of our cur-rent study was that we simplified the setups of the sensing approaches in both training and testing procedures. The simplification in sensors can reduce the time needed to calibrate the recognition procedure in practical applications.…”

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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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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Real-Time On-Board Recognition of Locomotion Modes for an Active Pelvis Orthosis

Cited by 10 publications

References 17 publications

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Flexible and Wearable GRF and EMG Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation

Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Locomotion Mode Recognition with Inertial Signals for Hip Joint Exoskeleton

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