Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

Lu, Yun; Cao, Ye; Chen, Yi; Li, Hui; Li, Weihua; Du, Haiping; Sun, Shuaishuai

doi:10.1088/1361-665x/accf6f

Cited by 7 publications

(2 citation statements)

References 36 publications

(38 reference statements)

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“…In addition, there is a braking delay in the electric motor, which is a potential safety threat prone to causing secondary injuries to the patient. The use of smart materials in the robot transmission structure [ 15 , 16 ] can achieve better control during use [ 17 , 18 ]. Magnetorheological fluid (MRF) is a smart material consisting of soft magnetic material particles, carrier fluid, and surfactant, and its fluid properties can be changed reversibly according to the external magnetic field [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper

Zhu,

Hu,

Zhao

et al. 2024

Micromachines

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In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human–machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

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Section: Introductionmentioning

confidence: 99%

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper

Zhu,

Hu,

Zhao

et al. 2024

Micromachines

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“…Brognara investigated the fall risk among patients with diabetic foot neuropathy using wearable inertial sensors [28]. Yun achieved new dynamic muscle force estimation by utilizing a wearable ultrasound transducer and an IMU sensor [29]. However, few studies have combined neural network models with wearable inertial sensors to estimate muscle forces.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System

Liu,

et al. 2024

Sensors

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Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN–LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN–LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN–LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

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Surface‐Level Muscle Deformation as a Correlate for Joint Torque

Alvarez,

de Marcillac,

Jin

et al. 2024

Adv Materials Technologies

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Wearable technology excels in estimating kinematic and physiological data, but estimating biological torques remains an open challenge. Deformation of the skin above contracting muscles—surface‐level muscle deformation—has emerged as a promising signal for joint torque estimation. However, a lack of ground‐truth measures of surface‐level muscle deformation has complicated the evaluation of wearable sensors designed to measure surface‐level muscle deformation. A non‐contact methodology is proposed for ground‐truth measurement of surface‐level muscle deformation using a 2D laser profilometer. It shows how three metrics of surface‐level muscle deformation—peak radial displacement: r = 0.94 ± 0.05, surface curvature: r = 0.78 ± 0.10, surface strain: r = 0.83 ± 0.12—correlate strongly to changes in volitional elbow torque, further exploring the impact of measurement location or joint angle on these relationships. A nonlinear, lead‐lag relationship between surface‐level muscle deformation and torque is also found. The findings suggest that surface‐level muscle deformation is a promising signal for non‐invasive, real‐time estimates of torque. By standardizing measurement, the methodology can help inform the design of future wearable sensors.

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Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

Cited by 7 publications

References 36 publications

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper

Estimation of Muscle Forces of Lower Limbs Based on CNN–LSTM Neural Network and Wearable Sensor System

Surface‐Level Muscle Deformation as a Correlate for Joint Torque

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