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One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R2 scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.

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.

Finger‐joint‐angle (FJA) estimation based on surface electromyographic (sEMG) signals plays an important role in the control of prosthetics and exoskeletons. However, most of the existing FJA‐estimation methods are unexplainable and subject‐specific, and accurate FJA estimation remains a challenge. This study pioneeringly modeled the relationship between the forearm muscles and hand movements for FJA estimation by adding a multihead self‐attention (MHSA) block between long short‐term memory networks. Additionally, an intersubject transfer‐learning method based on the K‐nearest‐neighbors clustering‐based pretraining strategy is proposed to ensure similarity between the data used for pretraining and that of a new user. The model performance is evaluated on the Ninapro DB2 dataset and collected data. Results show that the proposed method significantly outperformed the state‐of‐the‐art methods in terms of root‐mean‐square error (Ninapro DB2: 6.37 ± 0.16 vs. 7.26 ± 0.18, p < 0.01; collected data: 5.64 ± 0.21 vs. 5.08 ± 0.15, p < 0.01) and correlation coefficient (Ninapro DB2: 0.87 ± 0.01 vs. 0.85 ± 0.01, p < 0.01; collected data: 0.92 ± 0.01 vs. 0.88 ± 0.01, p < 0.01). Moreover, the proposed model is based on an explainable prediction mechanism wherein the importance of information from the different muscles is quantified by the MHSA. Schematic of the proposed method. A K‐nearest‐neighbors clustering‐based pretraining strategy was adopted to ensure similarity between the data used for pretraining and that of a new user. Besides, the proposed method pioneeringly utilized an attention mechanism to learn the relationship between muscles and hand movements for finger‐joint‐angle estimation and enhanced model explainability by using attention weights to quantify feature importance.

Joint estimation of the random impairments, phase noise (PHN) and channel, in orthogonal frequency division multiplexing (OFDM) system is investigated in this study. Bayesian Cramér-Rao lower bounds (BCRLBs) for the joint estimation of PHN and channel are derived, and are compared with the corresponding standard CRLB, which shows the significance of joint estimator in a Bayesian framework. The authors propose maximum a posteriori algorithms for the estimation of PHN and channel, utilising their statistical knowledge which is known a priori. The performance of the estimation methods is studied through simulations and numerical results show that the performance of the proposed algorithms is better than existing algorithms and is closer to BCRLB.

Intensive research in the field of mathematical modeling of hydraulic servo systems has shown that their mathematical models have many important details which cannot be included in the model. Due to impossibility of direct measurement or calculation of dimensions of certain components, leakage coefficients or friction coefficients, it was supposed that parameters of the hydraulic servo system are random. On the other side, it has been well known that the hydraulic servo system can be approximated by a linear model with time-varying parameters. An estimation of states and time-varying parameters of linear state-space models is of practical importance for fault diagnosis and fault-tolerant control. Previous works on this topic consider estimation in Gaussian noise environment, but not in the presence of outliers. The known fact is that the measurements have inconsistent observations with the largest part of the observation population (outliers). They can significantly make worse the properties of linearly recursive algorithms which are designed to work in the presence of Gaussian noises. This paper proposes the strategy of parameter–state robust estimation of linear state-space models in the presence of all possible faults and non-Gaussian noises. Because of its good features in robust filtering, Masreliez–Martin filter represents a cornerstone for realization of the robust algorithm. The good features of the proposed robust algorithm to identification of the hydraulic servo system are illustrated by intensive simulations.

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.

Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

Channel state information (CSI) collected during WiFi packet transmissions can be used for localization of commodity WiFi devices in indoor environments with multipath propagation. To this end, the angle of arrival (AoA) and time of flight (ToF) for all dominant multipath components need to be estimated. A two-dimensional (2D) version of the multiple signal classification (MUSIC) algorithm has been shown to solve this problem using 2D grid search, which is computationally expensive and is therefore not suited for real-time localisation. In this paper, we propose using a modified matrix pencil (MMP) algorithm instead. Specifically, we show that the AoA and ToF estimates can be found independently of each other using the one-dimensional (1D) MMP algorithm and the results can be accurately paired to obtain the AoA–ToF pairs for all multipath components. Thus, the 2D estimation problem reduces to running 1D estimation multiple times, substantially reducing the computational complexity. We identify and resolve the problem of degenerate performance when two or more multipath components have the same AoA. In addition, we propose a packet aggregation model that uses the CSI data from multiple packets to improve the performance under noisy conditions. Simulation results show that our algorithm achieves two orders of magnitude reduction in the computational time over the 2D MUSIC algorithm while achieving similar accuracy. High accuracy and low computation complexity of our approach make it suitable for applications that require location estimation to run on resource-constrained embedded devices in real time.

Lithium-ion batteries (LIBs) are widely used in electrical vehicles (EVs), but safety issues with LIBs still occur frequently. State of charge (SOC) and state of health (SOH) are two crucial parameters for describing the state of LIBs. However, due to inconsistencies that may occur among hundreds to thousands of battery cells connected in series and parallel in the battery pack, these parameters can be difficult to estimate accurately. To address this problem, this paper proposes a joint SOC and SOH estimation method based on the nonlinear state space reconstruction (NSSR) and long short-term memory (LSTM) neural network. An experiment testbed was set up to measure the SOC and SOH of battery packs under different criteria and configurations, and thousands of charging/discharging cycles were recorded. The joint estimation algorithms were validated using testbed data, and the errors for SOC and SOH estimation were found to be within 2.5% and 1.3%, respectively, which is smaller than the errors obtained using traditional Ah-Integral and LSTM-only algorithms.

An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.