arXiv subjects

Yo Kobayashi

Publications and source records attributed to Yo Kobayashi.

6 recordsLinked to original sources

arXiv2024

Measurement of changes in muscle viscoelasticity during static stretching using stress-relaxation data

This study investigates how the viscoelasticity of the muscle changes during static stretching by measuring the state of the muscle during stretching using continuous time-series data. We used a device that applied a force to the muscle during stretching and measured the reaction force. The device was attached to the participants, and time-series data of the reaction force (stress-relaxation data) during stretching were obtained. A model using fractional calculus (spring-pot model) was selected as the viscoelastic model for the muscle, in which the data for stress relaxation were fitted on a straight line on a both logarithmic plot. The experimental stress-relaxation results showed that viscoelasticity tended to change abruptly at a particular time during static stretching because the stress-relaxation data were represented by a broken line comprising two segments on the both logarithmic plot. Considering two states of viscoelasticity, before and after the change, the stress-relaxation curve was fitted to the spring-pot model with high accuracy using segment regression (R2 = 0.99). We compared the parameters of the spring-pot model before and after the change in muscle viscoelasticity. By examining these continuous time-series data, we also investigated the time taken for the effects of stretching to become apparent. Furthermore, by measuring the changes in muscle viscoelasticity during static stretching before and after a short-term exercise load of running on a treadmill, we examined the effects of short-term exercise load on the changes in viscoelasticity during static stretching.

physics.bio-ph↗

arXiv2024

Estimation of posture and joint angle of human body using foot pressure distribution: Morphological computation with human foot

This paper proposes a novel contact and wearable sensing system for estimating the upper body posture and joint angles (ankle, knee, and hip) of the human body using foot pressure distribution information obtained from a sensor attached to the plantar region. In the proposed estimation method, sensors are installed only on the plantar region, which is the end of the human body and the point of contact with the environment. The posture and joint angles of other parts of the body are estimated using only this information. As a contact and wearable sensor, the proposed system differs from previous measurement systems in the sense that the sensor does not need to be placed near the target joint or body. The estimation was carried out using a multivariate linear regression model with the foot pressure distribution as the input and the joint angle or posture as the output. The results reveal that it is possible to estimate the posture and joint angles of the human body from foot pressure distribution information (R2$\fallingdotseq$0.9). The proposed estimation method was validated by morphological computation to confirm that it is enabled by foot morphology. The validation approach compared the estimation accuracy achieved when an object was interposed between the foot pressure distribution sensor and the plantar region and the morphological relationship of the plantar region to the environment varied. The results reveal that there is a significant difference in the estimation accuracy between cases with and without an intervening object, suggesting that the morphology of the plantar region contributes to the estimation. Furthermore, the proposed estimation method is considered as physical reservoir computing, wherein the human foot is used as a computational resource.

cs.HC↗

arXiv2024

Estimating continuous data of wrist joint angles using ultrasound images

Ultrasound imaging has recently been introduced as a sensing interface for joint motion estimation. The use of ultrasound images as an estimation method is expected to improve the control performance of assistive devices and human--machine interfaces. This study aimed to estimate continuous wrist joint angles using ultrasound images. Specifically, in an experiment, joint angle information was obtained during extension--flexion movements, and ultrasound images of the associated muscles were acquired. Using the features obtained from ultrasound images, a multivariate linear regression model was used to estimate the joint angles. The coordinates of the feature points obtained using optical flow from the ultrasound images were used as explanatory variables of the multivariate linear regression model. The model was trained and tested for each trial by each participant to verify the estimation accuracy. The results show that the mean and standard deviation of the estimation accuracy for all trials were root mean square error (RMSE)=1.82 $\pm$ 0.54 deg and coefficient of determination (R2)=0.985 $\pm$ 0.009. Our method achieves a highly accurate estimation of joint angles compared with previous studies using other signals, such as surface electromyography, while the multivariate linear regression model is simple and both computational and model training costs are low.

cs.HC↗

arXiv2023

Information processing via human soft tissue

This study demonstrates that the soft biological tissues of humans can be used as a type of soft body in physical reservoir computing. Soft biological tissues possess characteristics such as stress-strain nonlinearity and viscoelasticity that satisfy the requirements for physical reservoir computing, including nonlinearity and memory. The aim of this study was to utilize the dynamics of human soft tissues as a physical reservoir for the emulation of nonlinear dynamical systems. To demonstrate this concept, joint angle data during motion in the flexion-extension direction of the wrist joint, and ultrasound images of the muscles associated with that motion, were acquired from human participants. The input to the system was the angle of the wrist joint, while the deformation field within the muscle (obtained from ultrasound images) represented the state of the reservoir. The results indicate that the dynamics of soft tissue have a positive impact on the computational task of emulating nonlinear dynamical systems. This research suggests that the soft tissue of humans can be used as a potential computational resource.

q-bio.NC↗

arXiv2018

Non-minimum phase viscoelastic properties of soft biological tissues

Understanding the visocoelastic properties of soft biological tissues is important for progress in the field of human healthcare. This study analyzes the viscoelastic properties of soft biological tissues using a fractional dynamics model. We conducted a dynamic viscoelastic test on several porcine samples, namely liver, breast, and skeletal muscle tissues, using a plate--plate rheometer. We found that some soft biological tissues have non-minimum phase properties; that is, the relationship between compliance and phase delay is not uniquely related to the non-integer derivative order in the fractional dynamics model. The experimental results show that the actual phase delay is larger than that estimated from compliance. We propose a fractional dynamics model with the fractional Hilbert transform to represent these non-minimum phase properties. The model and experimental results were highly correlated in terms of compliance and phase diagrams and complex mechanical impedance. We also show that the amount of additional phase delay, defined as the increase in actual phase delay compared to that estimated from compliance, differs with tissue type.

physics.bio-ph↗

arXiv2015

Simple Empirical Model for Identifying Rheological Properties of Soft Biological Tissues

Understanding the rheological properties of soft biological tissue is a key issue for mechanical systems used in the healthcare field. We propose a simple empirical model using Fractional Dynamics and Exponential Nonlinearity (FDEN) to identify the rheological properties of soft biological tissue. The model is derived from detailed material measurements using samples isolated from porcine liver. We conducted dynamic viscoelastic and creep tests on liver samples using a rheometer. The experimental results indicated that biological tissue has specific properties: i) power law increases in storage elastic modulus and loss elastic modulus with the same slope; ii) power law gain decrease and constant phase delay in the frequency domain over two decades; iii) log-log scale linearity between time and strain relationships under constant force; and iv) linear and log scale linearity between strain and stress relationships. Our simple FDEN model uses only three dependent parameters and represents the specific properties of soft biological tissue.

physics.bio-ph↗