Lower Limb Exercise Rehabilitation Assessment Based on Artificial Intelligence and Medical Big Data

Ling, Wenjie; Yu, Guoxing; Li, Zhaofeng

doi:10.1109/access.2019.2939006

Cited by 13 publications

(4 citation statements)

References 36 publications

(22 reference statements)

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“…Step A: Exercises: In this step, the user performs exercises in an environment monitored by a standard camera. Patients with stroke may have poor mobility of the lower or upper limbs, depending on the severity [12]. If it is high severity, passive rehabilitative exercises are prescribed for the patient; that is, there is the help of the physical therapist.…”

Section: A General Explanation Of Methodologymentioning

confidence: 99%

Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities

Francisco

Rodrigues

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Physical rehabilitation techniques during the treatment of clinical pathology are one of the most challenging areas for the medical structure, patients, and families. In large and continental countries, remote monitoring of this treatment is essential. However, equipment and medical follow-up during exercises still have high costs. With the improvement of computer vision and machine learning techniques, some computational, less expensive alternatives have been proposed in the literature. However, monitoring patients during physical rehabilitation exercises with the help of artificial intelligence by a health professional, especially from the capture of visual signals, is still a challenge and poorly explored in the scientific-technological literature. This work aims to propose a new methodology based on computer vision and machine learning for remote tracking of the body joints of patients during physiotherapy rehabilitation exercises. As a new contribution, this work presents a modular neural network architecture composed of two modules: one for detecting physical exercises and another for measuring how much is correct. Another contribution is a strategy for expanding databases, considering that generic databases for this type of exercise are rare on the internet. The results showed that both modules obtained more than 90% of accuracy in recognition and their respective validation.

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Section: A General Explanation Of Methodologymentioning

confidence: 99%

Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities

Francisco

Rodrigues

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Human lower limb rehabilitation training is generally divided into two parts. In the case of complete loss of motor function, external assistance is usually used to realize passive rehabilitation [12] of gait simulation. In this rehabilitation process, the joints carry out a repetitive round-trip motion, and the round-trip cycle is determined by the rehabilitation gait trajectory set in advance.…”

Section: Tracking Control Problem Descriptionmentioning

confidence: 99%

Periodic Disturbance Compensation Control of a Rope-Driven Lower Limb Rehabilitation Robot

Wang

Zhang

2023

Actuators

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In order to solve the external periodic disturbance and unknown dynamics influence in the passive rehabilitation process of a rope-driven lower limb rehabilitation robot, a control method with periodic repeated learning was designed. In this control method, the closed-loop dynamics are divided into a periodic disturbance term, an unknown dynamics term, and a basic term, and the shape function is designed by using the Stone–Weirstrass theorem. In the process of periodic operation, the estimated value of the shape function coefficient is repeatedly learned to obtain the periodic disturbance term approximation and to realize the compensation in advance. Through the design of the impedance learning rate, the unknown dynamic term is periodically learned, and the unknown dynamics approximation is obtained. By combining the two approximations with the basic terms which can be solved directly, the external periodic disturbance is compensated for in advance and the control precision is improved. The control algorithm was verified by simulation, and the error fluctuation of the system gradually decreases and reaches the ideal value within several cycles. The performance of the control system is stable, and the problem of limb impedance caused by different patients is well solved.

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“…Ling et al, [19] compared to the first time where the traditional method of development of technology for virtual reality, it develops a realistic system of lower limb rehabilitation, creates a more accurate human lower limb rehabilitation model, studies the lower limb rehabilitation movement with passive mode and simulates the improvements to human musculoskeletal enhancements in the second stage of rehabilitation. Second, analysis is being carried out on a robust omnidirectional mobile based on a low limb rehabilitation control system based on medical big data and artificial intelligence.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Cui

Chang²

2020

IEEE Access

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Presently, A wide range of unlabeled and minimal style data significantly decreases the current motion sequence's reuse ability. An important method of data reuse has a successful classification and fragment separation, which has been discussed in this research. This paper focuses on these particular problems and the tremendous progress of deep learning in design and symbolic fields. A Limited Boltzmann Model (LBM) theory is based on the Advanced Spatio-Temporal Extraction Model (ASTEM), which has been used for analyzing the physiological motion of human skeletons. There are primarily three aspects to the results of the study. (1) For constructing a semi-combination model, the stack factor decomposition is used as a spatiotemporal model function and LBM discrimination. (2), Optimized algorithm used to create the three-channel generative LBM model using the weight decomposition idea and then extract the time and space-based abstract properties of the original motion series. (3) The unsupervised related model of frame detection is built using the perception of human interaction through 3D convolution LBM. A significant research direction of the medical analysis and extraction of sports data is used appropriately to interpret and gain valuable information and knowledge from motion analyses. Experimental outcomes show that this technique offers technical assistance and guidance for implementing a real cloud-based fusion system.

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Lower Limb Exercise Rehabilitation Assessment Based on Artificial Intelligence and Medical Big Data

Cited by 13 publications

References 36 publications

Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities

Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities

Periodic Disturbance Compensation Control of a Rope-Driven Lower Limb Rehabilitation Robot

Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

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