The knee has gradually become an important research target for the lower extremity exoskeleton. However, the issue that whether the flexion-assisted profile based on the contractile element (CE) is effective throughout the gait is still a research gap. In this study, we first analyze the effective flexion-assisted method through the passive element’s (PE) energy storage and release mechanism. Specifically, ensuring assisting within an entire joint power period and the human’s active movement is a prerequisite for the CE-based flexion-assisted method. Second, we design the enhanced adaptive oscillator (EAO) to ensure the human’s active movement and the integrity of the assistance profile. Third, a fundamental frequency estimation based on discrete Fourier transform (DFT) is proposed to shorten the convergence time of EAO significantly. The finite state machine (FSM) is designed to improve the stability and practicality of EAO. Finally, we demonstrate the effectiveness of the prerequisite condition for the CE-based flexion-assisted method by using electromyography (EMG) and metabolic indicators in experiments. In particular, for the knee joint, CE-based flexion assistance should be within an entire joint power period rather than just in the negative power phase. Ensuring the human’s active movement will also significantly reduce the activation of antagonistic muscles. This study will aid in designing assistive methods from the perspective of natural human actuation and apply the EAO to the human-exoskeleton system.

Inertial measurement units (IMUs) increasingly function as a basic component of a wearable sensor network (WSN). The IMU-based joint angle estimation (JAE) is a typical usage of IMUs for WSN, with extensive applications. However, the issue that IMUs move with respect to their original placement during JAE is still a research gap and limits the robustness of deploying the technique in real-world application scenarios. In this study, we propose to detect and correct the IMU movement online in a computationally lightweight manner. In particular, we first experimentally investigate the influence of IMU movements. Second, we design the metrics for detecting IMU movements by mathematically formulating how the IMU movement affects the IMU’s measurements. Third, we determine the optimal thresholds of metrics by synthetic IMU data from a significantly amended simulation model. Finally, a correction method is proposed to correct the effects of IMU movements. We demonstrate our method on both synthetic data and real-user data. Our proposed method achieves a detection rate of 94.8%, a misdetection rate of 9.8%, and the calculation time of 2.15 ms for detecting IMU movements and can restore the accuracy of JAE. All of the results significantly outperform the intuitive method. The results demonstrate our method is a promising solution to detect and correct IMU movements during JAE. This study will aid the deployment of IMU-based JAE in real-world applications.

Inertial measurement unit (IMU)-based lower-limb joint angle estimation (JAE) that enables wearable and real-time monitoring of kinematic and biomechanical states has been studied for decades. However, 3-degree-of-freedom (DoF) JAE of lower-limb joints still requires cumbersome calibration procedures. In this article, we propose a self-aligned method by formulating a universal 3-DoF kinematic constraint and solving it by dividing it into three subproblems. Particularly, we divide the issue of optimizing the 3-DoF kinematic constraint by: 1) estimating the main axis following a widely adopted hinge-joint assumption; 2) unifying the reference frames according to a comprehensive metric; and 3) estimating the rest two joint axes. By estimating the coordinates of the three rotational axes of the 3-DoF joint, we further estimate the 3-DoF joint angles by decoupling the joint’s rotations into rotations around the axes. We evaluate our method by accuracy comparison with other methods, test–retest reliability, and ablation experiments. The performance demonstrates that the RMSEs of our method are below 2.9° for hip and knee, below 2.6° for ankle, which are comparable with other methods and able to estimate 3-DoF angles in a self-aligned manner. The agreement between test and retest is good, and the ablation experiments demonstrate the effectiveness of sequentially solving the three subproblems. The proposed method is feasible for self-aligned JAE of 3-DoF lower-limb joints. This study may aid the wider application of IMU-based JAE in field conditions and provide a powerful component for wearable human intent decoding.

Both the discrete motion states and continuous joint kinematics are essential for controlling assistive robots under changeable environmental conditions. However, few studies investigate both the discrete and continuous motion intents. This paper is the first work to propose an end-to-end motion intent decoding method that integrates recognition of discrete locomotion modes and prediction of continuous joint kinematics and gait events. First, we propose a data-driven approach to segment the transitional periods of the adjacent locomotion modes and determine their boundaries. Second, we build a CNN-LSTM network that utilizes locomotion mode classification as the prior of joint kinematics and gait events prediction and thus decodes discrete and continuous motion intents. Finally, we evaluate our method through extensive experiments and comparisons. The proposed method for locomotion mode recognition achieves an accuracy of 98.73% for steady-state and 97.53% for transitional periods. For knee angle prediction, the method achieves the ahead-of-time prediction with NRMSE lower than 8.41%, R-value higher than 0.93, and R-square higher than 0.88 for various prediction time. For gait event prediction, it predicts the future timing of events with an error of 8.46 ms for initial contact, 32.76 ms for heel off, and 8.91 ms for toe off. This study reveals the feasibility of predicting locomotion-based kinematics and gait events on various terrains. The rich motion intents studied in this work highlight the potential of a more detailed lower-limb rehabilitation system.

Deep learning (DL)-based human activity recognition (HAR) methods have shown promise in the applications of health Internet of Things (IoT) and wireless body sensor networks (BSNs). However, adapting these methods to new users in real-world scenarios is challenging due to the cross-subject issue. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning’s benefit of sparsely acquiring informative samples with actual labels and self-training’s benefit of effectively utilizing unlabeled data to adapt the HAR model to the target domain, i.e., the new users. ActiveSelfHAR consists of several key steps. First, we utilize the model from the source domain to select and label the domain invariant samples, forming a self-training set. Second, we leverage the distribution information of the self-training set to identify and annotate samples located around the class boundaries, forming a core set. Third, we augment the core set by considering the spatiotemporal relationships among the samples in the nonself-training set. Finally, we combine the self-training set and augmented core set to construct a diverse training set in the target domain and fine-tune the HAR model. Through leave-one-subject-out validation on three IMU-based data sets and one EMG-based data set, our method achieves mean HAR accuracies of 95.20%, 82.06%, 89.52%, and 92.82%, respectively. Our method demonstrates similar HAR accuracies to the upper bound, i.e., fine-tuning framework with approximately 1% labeled data of the target data set, while significantly improving data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into health IoT and BSN.

Prolonged awkward arm postures and repetitive tasks in industrial environments can easily lead to worker injuries. The occupational shoulder exoskeleton is a promising solution to reducing the incidence of work-related musculoskeletal disorders, especially in the shoulder. In general, the device has to be compatible with the human shoulder, which is one of the most complex joints, thus posing significant challenges in existing efforts to design occupational shoulder exoskeletons. The purpose of this paper is to present a comprehensive review of exoskeletons designed to support human shoulders and arms in industrial applications. Recent developments and advancements in mechanism design, actuators, and control aspects are discussed in detail. A brief overview of the evaluation (i.e., participants, task design, objective, and subjective assessment) is also reported, and the experiment protocol to assess performance is summarized. Finally, existing challenges and possible future research directions for occupational shoulder exoskeletons are provided.