ESTIMATION OF KNEE JOINT TORQUE DURING SIT–STAND MOVEMENT BASED ON sEMG SIGNALS USING NEURAL NETWORKS

Akhil, V.M.; Ashmi, M.; Jobin, V.; Rajendrakumar, P. K.; Sivanandan, K. S.

doi:10.1142/s0219519422500245

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Neural network control systems have been explored for activities like cycling and knee joint movement [18]. These systems offer the potential to adapt and learn from data, making them suitable for personalized control.…”

Section: Neural Network Controlmentioning

confidence: 99%

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Ahmad,

Shamsudin,

Soomro

et al. 2023

Sinergi

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Functional Electrical Stimulation (FES) cycling, or FES-Cycling, holds great therapeutic potential for individuals with paralysis, such as those with Spinal Cord Injury (SCI), traumatic brain injury, or stroke, aiming to restore mobility. However, the nonlinear nature of the musculoskeletal system poses a significant challenge in controlling FES-Cycling. To address this, an integrated closed-loop phase angle fuzzy-based system was developed. This system offers real-time control by adjusting stimulation intensity (pulse width) within the range of 50 to 200μs while maintaining a constant frequency of 35Hz, thereby ensuring precise pedaling trajectory and cadence patterns. An experimental study involved three healthy individuals (Cases A, B, and C) and one individual with hemiplegia stroke (Case D). Results showed that the proposed system consistently reduced average angle trajectory errors for Cases A, B, and C, with values of 2.6945, 3.2958, and 2.9922 degrees, respectively. Case D, affected by hemiplegia stroke, faced greater challenges and exhibited a higher error of 3.4562 degrees. Fatigue resistance, evaluated through fatigue indices, showed promising results for Cases A, B, and C with values of 0.10778, 0.06866, and 0.04603, respectively. However, Case D experienced higher fatigue (0.2304) due to the unique challenges of hemiplegia stroke. These findings highlight the effectiveness of the proposed control system in optimizing FES-Cycling, particularly for healthy individuals. For individuals with paralysis, like Case D, further research is needed to adapt the system to their specific conditions and cycling patterns. This system holds the potential for enhancing FES-Cycling as a therapeutic strategy and warrants additional investigation and customization for different patient populations.

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Section: Neural Network Controlmentioning

confidence: 99%

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Ahmad,

Shamsudin,

Soomro

et al. 2023

Sinergi

View full text Add to dashboard Cite

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Review of Methods of Finding Muscle Torque in Lower Limb Rehabilitation

Gusi,

Perez,

Ablan

et al. 2023

2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environ

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Prediction of Collagen Content Through Biomechanical Parameters in Mice Skin Wound: A Comparison of Ann and Anfis Models

Ebrahiminia

Radman

Samimi

2023

J. Mech. Med. Biol.

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Pathological analysis as well as biomechanical methods are powerful approaches for collagen assessment, which plays an important role in understanding the wound healing process and choosing a treatment method in clinical situations. Due to the limitations of preparing and evaluating pathological images, this study was designed to establish a machine learning technique to predict the wound collagen content through its biomechanical parameters. For this purpose, the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were compared. The wound was created with an incision on the back of 30 male BALB/c mice. On the 7th and 14th days, animals were sacrificed and 60 wound tissue samples were evaluated using histopathological and biomechanical methods to quantify the amount of collagen and wound tensile strength to feed the ANN and ANFIS developed models. Based on the results, both models have appropriate performance to predict the wound collagen content. However, the comparison of coefficient of determination ([Formula: see text]) and root mean square error (RMSE) for testing dataset revealed that ANN ([Formula: see text], [Formula: see text]) had more prediction capability than ANFIS ([Formula: see text], [Formula: see text]). As a decision support system, ANN model could assist in the evaluation of wound healing process with collagen values prediction.

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ESTIMATION OF KNEE JOINT TORQUE DURING SIT–STAND MOVEMENT BASED ON sEMG SIGNALS USING NEURAL NETWORKS

Cited by 3 publications

References 27 publications

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Review of Methods of Finding Muscle Torque in Lower Limb Rehabilitation

Prediction of Collagen Content Through Biomechanical Parameters in Mice Skin Wound: A Comparison of Ann and Anfis Models

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