Assisted Gait Phase Estimation Through an Embedded Depth Camera Using Modified Random Forest Algorithm Classification

Pasinetti, Simone; Fornaser, Alberto; Lancini, Matteo; Cecco, M. De; Sansoni, Giovanna

doi:10.1109/jsen.2019.2957667

Cited by 19 publications

(8 citation statements)

References 56 publications

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“…In [53], a quadratic discriminant analysis allows to get the gait phase from capacitive sensors measuring the thigh muscles contraction. Finally, in [161], a computer vision classifier can estimate the gait phase from the data of depth cameras located on the crutches.…”

Section: Machine Learning Phase (Mlp)mentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

164

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Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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Section: Machine Learning Phase (Mlp)mentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

164

View full text Add to dashboard Cite

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“…On the one hand, the development of individual counselors' professional abilities is uneven in all aspects. As the five types of vocational abilities that distinguish the position of counselor from other positions, whether it is the document requirements of the education authorities at all levels or the actual needs of student work, higher requirements are put forward for the five types of vocational abilities such as the ability of education and guidance of counselors [25].…”

Section: Problems In the Teaching Ability Of Counselorsmentioning

confidence: 99%

The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

Sun

et al. 2022

Wireless Communications and Mobile Computing

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The good development of industry-specific universities (hereinafter referred to as characteristic universities) promotes the optimal allocation of national university resources and moreover promotes the development of China’s education, while counselors, as important teaching and research staff, have a very close connection with the development level of the school, directly reflecting the quality and level of education of the school and determining the height of its educational development. Talent is the first productive forces, counselors as teaching talents, and is essential to the construction of industry specialty universities. The evaluation index system of teaching ability of counselors can effectively guide counselors toward the path of specialization, scientifically realize career planning, and maximize the achievement of the general goal of the characteristic school they are in. This article aims to assess the teaching capacity of counselors and targeted training capabilities. The counselor’s ability is reflected in the teaching of teaching, completing teaching objectives and cultivates student’s hobbies. In this paper, through investigating the cultivation methods of different industries, the cultivation of counselor’s ability is to assess consensus and establish multilevel teachers’ teaching capacity cultivation system. The results show that the system can effectively improve the teaching capacity of young teachers and increase 20% of student’s satisfaction; however, it also shows that the professional competence of counselors still needs to be improved.

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“…The authors of [30] employed a decision tree estimating the gait phrase based on the feet loads and segmented IMU data. The authors of [31] presented a novel method for gait phase classification. They modified a Random Forest algorithm to foresee an initial phase of gait data, classifying the gait phase (stance or swing).…”

Section: Related Workmentioning

confidence: 99%

Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism

Wang

et al. 2021

Machines

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To date, several alterations in the gait pattern can be treated through rehabilitative approaches and robot assisted therapy (RAT). Gait data and gait trajectories are essential in specific exoskeleton control strategies. Nevertheless, the scarcity of human gait data due to the high cost of data collection or privacy concerns can hinder the performance of controllers or models. This paper thus first creates a GANs-based (Generative Adversarial Networks) data augmentation method to generate synthetic human gait data while still retaining the dynamics of the real gait data. Then, both the real collected and the synthesized gait data are fed to our constructed two-stage attention model for gait trajectories prediction. The real human gait data are collected with the five healthy subjects recruited from an optical motion capture platform. Experimental results indicate that the created GANs-based data augmentation model can synthesize realistic-looking multi-dimensional human gait data. Also, the two-stage attention model performs better compared with the LSTM model; the attention mechanism shows a higher capacity of learning dependencies between the historical gait data to accurately predict the current values of the hip joint angles and knee joint angles in the gait trajectory. The predicted gait trajectories depending on the historical gait data can be further used for gait trajectory tracking strategies.

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Assisted Gait Phase Estimation Through an Embedded Depth Camera Using Modified Random Forest Algorithm Classification

Cited by 19 publications

References 56 publications

Review of control strategies for lower-limb exoskeletons to assist gait

Review of control strategies for lower-limb exoskeletons to assist gait

The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism

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