Search Results (7)

Chronic total occlusion (CTO) is one of the most severe and sophisticated vascular stenosis because of complete blockage, greater operation difficulty, and lower procedural success rate. This study proposes a hydraulic-driven soft robot imitating the earthworm’s locomotion to assist doctors or operators in actively opening thrombi in coronary or peripheral artery vessels. Firstly, a three-actuator bionic soft robot is developed based on earthworms’ physiological structure. The soft robot’s locomotion gait inspired by the earthworm’s mechanism is designed. Secondly, the influence of structure parameters on actuator deformation, stress, and strain is explored, which can help us determine the soft actuators’ optimal structure parameters. Thirdly, the relationship between hydraulic pressure and actuator deformation is investigated by performing finite element analysis using the bidirectional fluid–structure interaction (FSI) method. The kinematic models of the soft actuators are established to provide a valuable reference for the soft actuators’ motion control. Full article

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios. Full article

Gait analysis is important in a variety of applications such as animation, healthcare, and virtual reality. So far, high-cost experimental setups employing special cameras, markers, and multiple wearable sensors have been used for indoor human pose-tracking and gait-analysis purposes. Since locomotive activities such as walking are rhythmic and exhibit a kinematically constrained motion, fewer wearable sensors can be employed for gait and pose analysis. One of the core parts of gait analysis and pose-tracking is lower-limb-joint angle estimation. Therefore, this study proposes a neural network-based lower-limb-joint angle-estimation method from a single inertial sensor unit. As proof of concept, four different neural-network models were investigated, including bidirectional long short-term memory (BLSTM), convolutional neural network, wavelet neural network, and unidirectional LSTM. Not only could the selected network affect the estimation results, but also the sensor placement. Hence, the waist, thigh, shank, and foot were selected as candidate inertial sensor positions. From these inertial sensors, two sets of lower-limb-joint angles were estimated. One set contains only four sagittal-plane leg-joint angles, while the second includes six sagittal-plane leg-joint angles and two coronal-plane leg-joint angles. After the assessment of different combinations of networks and datasets, the BLSTM network with either shank or thigh inertial datasets performed well for both joint-angle sets. Hence, the shank and thigh parts are the better candidates for a single inertial sensor-based leg-joint estimation. Consequently, a mean absolute error (MAE) of 3.65° and 5.32° for the four-joint-angle set and the eight-joint-angle set were obtained, respectively. Additionally, the actual leg motion was compared to a computer-generated simulation of the predicted leg joints, which proved the possibility of estimating leg-joint angles during walking with a single inertial sensor unit. Full article

Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively. Full article

The optimization of multisystem neurorehabilitation protocols including electrical spinal cord stimulation and multi-directional tasks training require understanding of underlying circuits mechanisms and distribution of the neuronal network over the spinal cord. In this study we compared the locomotor activity during forward and backward stepping in eighteen adult decerebrated cats. Interneuronal spinal networks responsible for forward and backward stepping were visualized using the C-Fos technique. A bi-modal rostrocaudal distribution of C-Fos-immunopositive neurons over the lumbosacral spinal cord (peaks in the L4/L5 and L6/S1 segments) was revealed. These patterns were compared with motoneuronal pools using Vanderhorst and Holstege scheme; the location of the first peak was correspondent to the motoneurons of the hip flexors and knee extensors, an inter-peak drop was presumably attributed to the motoneurons controlling the adductor muscles. Both were better expressed in cats stepping forward and in parallel, electromyographic (EMG) activity of the hip flexor and knee extensors was higher, while EMG activity of the adductor was lower, during this locomotor mode. On the basis of the present data, which showed greater activity of the adductor muscles and the attributed interneuronal spinal network during backward stepping and according with data about greater demands on postural control systems during backward locomotion, we suppose that the locomotor networks for movements in opposite directions are at least partially different. Full article

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist. Full article

A new bi-directional, simple-structured solenoid actuator for active locomotion capsule robots (CRs) is investigated in this paper. This active actuator consists of two permanent magnets (PMs) attached to the two ends of the capsule body and a vibration inner mass formed by a solenoidal coil with an iron core. The proposed CR, designed as a sealed structure without external legs, wheels, or caterpillars, can achieve both forward and backward motions driven by the internal collision force. This new design concept has been successfully confirmed on a capsule prototype. The measured displacements show that its movement can be easily controlled by changing the supplied current amplitude and frequency of the solenoid actuator. To validate the new bi-directional CR prototype, various experimental as well as finite element analysis results are presented in this paper. Full article