Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network.

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

Electrical stimulation accelerates neurite regeneration in axotomized dorsal root ganglion neurons by increasing MMP-2 expression.

Electrical stimulation (ES) can be useful for promoting the regeneration of injured axons, but the mechanism underlying its positive effects is largely unknown. The current study aimed to investigate whether ES could enhance the regeneration of injured neurites in dorsal root ganglion explants and regulate the MMP-2 expression level, which is correlated with regeneration. Significantly increased neurite regeneration and MMP-2 expression was observed in the ES group compared with the sham group. However, an MMP inhibitor significantly decreased this ES-induced neurite regeneration. Our data suggest that the positive effect of ES on neurite regeneration could likely be mediated by an increase in MMP-2 expression, thereby promoting the regeneration of injured neurites.

Classification of gait phases based on a machine learning approach using muscle synergy.

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

A Simplified Estimation of Abnormal Reflex Torque due to Elbow Spasticity Using Neuro-musculoskeletal Model.

This paper is to develop a simplified estimation method of internal torque for clinical use, such as spasticity assessment. Compared with many parameters to be tuned, the proposed estimation method only has a single tuning parameter by simplifying the neuro-musculoskeletal model. Moreover, based on forward dynamics, the proposed method uses EMG signals as the input, and uses muscle activation dynamics and musculotendon dynamics to calculate internal torque. A biomechanical method based on dynamometer was applied to determine the tuning parameter and to validate the estimation result of the proposed model. Through a pilot study with healthy subjects and stroke patients, we found that the proposed estimation method would be helpful for spasticity assessment.