A Phase Variable Approach for IMU-Based Locomotion Activity Recognition

Bartlett, H.; Goldfarb, Michael

doi:10.1109/tbme.2017.2750139

Cited by 63 publications

(31 citation statements)

References 19 publications

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“…Although the controller facilitates a wider range of tasks than walking, it does not encapsulate tasks such as stair ascent and descent. However, the structure provided is flexible for embodying new sets of kinematics in task-recognition frameworks [52], [53]. Similarly, a paradigm that unifies different speeds and inclines (as in [54]) can be incorporated to expand the range of tasks that the proposed controller can manage, especially for walking on non-flat surfaces.…”

Section: Discussionmentioning

confidence: 99%

A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis

et al. 2019

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Although there has been recent progress in control of multi-joint prosthetic legs for rhythmic tasks such as walking, control of these systems for non-rhythmic motions and general real-world maneuvers is still an open problem. In this article, we develop a new controller that is capable of both rhythmic (constant-speed) walking, transitions between speeds and/or tasks, and some common volitional leg motions. We introduce a new piecewise holonomic phase variable, which, through a finite state machine, forms the basis of our controller. The phase variable is constructed by measuring the thigh angle, and the transitions in the finite state machine are formulated through sensing foot contact along with attributes of a nominal reference gait trajectory. The controller was implemented on a powered knee-ankle prosthesis and tested with a transfemoral amputee subject, who successfully performed a wide range of rhythmic and non-rhythmic tasks, including slow and fast walking, quick start and stop, backward walking, walking over obstacles, and kicking a soccer ball. Use of the powered leg resulted in clinically significant reductions in amputee compensations for rhythmic tasks (including vaulting and hip circumduction) when compared to use of the take-home passive leg. In addition, considerable improvements were also observed in the performance for nonrhythmic tasks. The proposed approach is expected to provide a better understanding of rhythmic and non-rhythmic motions in a unified framework, which in turn can lead to more reliable control of multi-joint prostheses for a wider range of real-world tasks.

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Section: Discussionmentioning

confidence: 99%

A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis

et al. 2019

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“…A mechanical sensor is also widely used in locomotion mode recognition. For example, the Inertial Measurement Unit (IMU) can provide position information (Ahmad et al, 2013;Young et al, 2014b;Bartlett and Goldfarb, 2018;Martinez-Hernandez and Dehghani-Sanij, 2018). Besides, a mechanical sensor is easily integrated with prosthetics.…”

Section: Introductionmentioning

confidence: 99%

On-board Training Strategy for IMU-Based Real-Time Locomotion Recognition of Transtibial Amputees With Robotic Prostheses

Wang

2020

Front. Neurorobot.

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The paper puts forward an on-board strategy for a training model and develops a real-time human locomotion mode recognition study based on a trained model utilizing two inertial measurement units (IMUs) of robotic transtibial prosthesis. Three transtibial amputees were recruited as subjects in this study to finish five locomotion modes (level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending) with robotic prostheses. An interaction interface was designed to collect sensors' data and instruct to train model and recognition. In this study, the analysis of variance ratio (no more than 0.05) reflects the good repeatability of gait. The on-board training time for SVM (Support Vector Machines), QDA (Quadratic Discriminant Analysis), and LDA (Linear discriminant analysis) are 89, 25, and 10 s based on a 10,000 × 80 training data set, respectively. It costs about 13.4, 5.36, and 0.067 ms for SVM, QDA, and LDA for each recognition process. Taking the recognition accuracy of some previous studies and time consumption into consideration, we choose QDA for real-time recognition study. The real-time recognition accuracies are 97.19 ± 0.36% based on QDA, and we can achieve more than 95% recognition accuracy for each locomotion mode. The receiver operating characteristic also shows the good quality of QDA classifiers. This study provides a preliminary interaction design for human-machine prosthetics in future clinical application. This study just adopts two IMUs not multi-type sensors fusion to improve the integration and wearing convenience, and it maintains comparable recognition accuracy with multi-type sensors fusion at the same time.

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“…Related range sensor and home-video based systems, which cost about £700, such as [9], [10] and [11], that build on the work of [12], with Pro-Trainer motion analysis software (Sports Motion, Inc., Cardiff, CA), offer gait analysis outside the gait laboratory, e.g., in local clinics and at homes. Similar to other range sensor and home-video based gait analysis systems [2,[13][14][15][16][17][18][19][20][21] and Inertial Measurement Unit (IMU) based gait analysis systems [22][23][24][25][26], the gait parameters obtained after data processing can be sent to physiatrists for clinical consultation, indicating the potential for tele-rehabilitation [27][28][29][30][31]. It is shown in [32] that a 2D video tracker software provides similar accuracy to VICON 3D system for knee angle measurement but not for measurement of the ankle angle over time.…”

Section: Introductionmentioning

confidence: 99%

“…Our system addresses some of the drawbacks of related range sensor and home-video based systems [2,9,11,[13][14][15][16][17][18][19]41] and IMU systems [22][23][24][25][26] namely: (1) Unlike [9], there are no colour restrictions on the background or the participant's clothing; (2) In contrast to Soda et al [9], which is validated on only one healthy volunteer with one walking trial with no gold standard benchmark, we validate our proposed system's knee angle against the gold standard VICON MX Giganet 6xT40 and 6xT160 (VICON Motion Systems Ltd., Oxford, UK, approximately £250,000) optical motion analysis system (the same gold standard as used by [11]). (3) Unlike systems of [11] and Pro-Trainer and Siliconcoach (Siliconcoach Ltd., Dunedin, New Zealand) as used by [42] and [43] that require significant manual effort, our system autonomously tracks the markers attached to the joints and calculates the knee angle; the only operational effort required is for marker-template selection for tracking initialization which is done via a user-friendly graphical user interface (GUI).…”

Section: Introductionmentioning

confidence: 99%

“…(5) 3D Kinect range sensor-based systems [13-18, 20, 21] cannot reliably capture relatively fast body motion, since Kinect operates at only 30 frames per second (fps), whereas our system operates at 210 fps. (6) Like other range sensor and home-video based systems, our system is non-intrusive to the participants, which is in contrast to state-of-the-art IMU gait analysis systems [22][23][24][25][26]. However, with only a 2D camera in our gait analysis system, its drawback lies in the following two aspects: (1) Estimation of the human joint locations using our system is less accurate compared to 3D Kinect-based range sensor systems, and (2) The gait parameters derived from the 2D images in our system are less reliable than those derived from the inertial data in IMU systems.…”

Section: Introductionmentioning

confidence: 99%

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Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

Yang

Ugbolue

McNicol

et al. 2019

IET Science, Measurement & Technology

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While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibration, autonomous detection of frames-ofinterest, Kalman-filter+Structural-Similarity-based marker tracking, and autonomous knee angle calculation. The proposed system is tested using 15 participants, including 10 stroke patients and 5 healthy volunteers. The evaluation of autonomous frames-ofinterest detection shows only 0.2% difference between the frame number of the detected frame compared to the frame number of the manually labelled ground truth frame, and thus can replace manual labelling. The system is validated against a gold standard optical motion analysis system, using knee angle accuracy as metric of assessment. The accuracy investigation between the RGBand the grayscale-video marker tracking schemes shows that the grayscale system suffers from negligible accuracy loss with a significant processing speed advantage. Experimental results demonstrate that the proposed system can automatically estimate the knee angle, with R-squared value larger than 0.95 and Bland-Altman plot results smaller than 3.0127 degrees mean error. r row centre coordinate of the marker t duration of one frame a candidate block b marker-template ∈ set membership,

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A Phase Variable Approach for IMU-Based Locomotion Activity Recognition

Abstract: This paper offers a new method of gait classification based on a phase variable description. The method is shown to provide improved classification accuracy relative to an LDA pattern recognition framework when trained with nonsubject-specific data.

Cited by 63 publications

References 19 publications

A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis

A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis

On-board Training Strategy for IMU-Based Real-Time Locomotion Recognition of Transtibial Amputees With Robotic Prostheses

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

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