Drones are widely used in fields such as aerial photography and security inspection, owing to their high mobility and wide field of view. Compared to a single drone, multi-drone systems can capture complementary information from different viewpoints, effectively addressing complex tracking scenarios such as occlusion, viewpoint changes, and target disappearance. However, existing multi-drone tracking methods often suffer from inadequate feature representation and inefficient inter-drone information fusion. To tackle these challenges, we propose FAEMTrack, a novel multi-drone tracking framework that integrates a feature-augmented embedding module (FAEM) with an advanced cross-drone fusion mechanism. FAEM enhances spatial-semantic feature encoding by replacing conventional convolutional layers with a multi-scale depthwise convolution block (MSDB) and spatial attention mechanism (SA), significantly improving the discriminative power of target features across different drone perspectives. Furthermore, to better utilize complementary information from different viewpoints, we introduce a cross-drone fusion strategy based on response confidence and entropy-weighted fusion, dynamically integrating information from multi-drone and promoting adaptive collaboration among them. Extensive experiments on the MDOT benchmark demonstrate that FAEMTrack outperforms existing state-of-the-art single-drone and multi-drone tracking methods in both tracking accuracy and robustness, showcasing its superior performance in complex scenarios. The source code and trained models will be available at https://github.com/wjh-scut/FAEMTrack.

This study proposes a novel Transformer-based framework for identifying terrain transition states and recognizing steady-state terrains using data from inertial measurement units. Compared to traditional time series classification methods for transition states, our approach reframes the problem as a time series fitting and terrain change-point detection task, capturing the dynamic nature of human locomotion across varying terrains. Outdoor experiments demonstrate the model’s superior performance in both steady-state and transition detection, with enhanced interpretability. Specifically, steady-state identification achieves accuracies of 99.63% on normal terrain and 98.06% on complex terrain. Compared to traditional convolutional neural network-based approaches, our method improves terrain classification accuracy by 12.30% –37.67% under normal conditions and 12.34% –39.90% under complex conditions. Moreover, the normalized root mean square error for transition curve fitting is significantly reduced to 0.016 and 0.032 for normal and complex terrains, outperforming other models.

By accurately recognizing the wearer’s motion, the underwater exoskeleton enables more efficient human-machine collaboration and provides enhanced assistance in complex and dynamic underwater environments. In this study, we propose a soft underwater exosuit motion mode recognizer based on a long short-term memory network and convolutional neural networks, referred to as LSTM-CNN. This model is designed to perform two tasks: motion mode classification and state transition label recognition. First, the LSTM network extracts features from the time-series data, followed by further feature extraction and classification using the convolutional and fully connected networks. The recognition of motion modes relies on three IMU sensors placed on the left and right legs and the back of the torso of the soft underwater exosuit. On the dataset containing four classes, including non-assist, breaststroke, flutter kick, and underwater walking, LSTM-CNN achieved an overall accuracy of 99.943±0.006% in motion mode classification and 92.101±0.054% in state transition label recognition. The experimental results indicate that the LSTM-CNN achieves better accuracy and performs optimally across various evaluation metrics compared to the other methods.

Underwater assistance is crucial for individuals who depend on diving for their livelihood. In this paper, we propose a novel underwater exosuit actuator designed to assist with flutter kicking during diving, thereby decreasing the effort the diver has to exert. The actuator can provide bidirectional assistance to the up and downbeats when the diver kicks underwater, and has no restriction on leg movements when it is deactivated. Both the benchtop experiment and human subject tests were conducted to verify its performance. The benchtop experiment verified its kinematic features, while tests with five participants validated its assistive performance. The results indicate that the actuator delivers a peak torque of 0.0947 Nm/kg and a peak force of 100 N in both directions, while allowing free leg movement during walking or kicking when not powered, thus ensuring safety during diving.

The authors hope that this overview and suggestions will stimulate and contribute to further ongoing discussions and interesting research work and industrial applications in some fields of Systems, Man and Cybernetics.

Exosuits play an essential role in facilitating rehabilitation training for patients with functional impairments. However, the safety of human-exosuit interactions and their adaptability to varied environments are crucial challenges that hinder their transition from laboratory prototypes to practical real-world applications. This study presents an innovative active exosuit designed to correct foot drop, characterized by its safe interactive performance and terrain daptability. Regarding safe human-exosuit interaction, meticulous design of the dimensions of hinge module ensures that, upon contact between the hinges, the driving rope reaches its maximum contraction length, thereby mechanically safeguarding against excessive dorsiflexion of the ankle joint by preventing further stretch. In terms of adaptability to various terrains, a visual system capable of terrain recognition has been integrated, enabling a seamless transition between control modes based on the detected terrain, achieving a recognition accuracy of 99.1% through the Transformer in Transformer (TNT) algorithm. To quantify the effect of the exosuit in correcting drop-foot, five healthy participants with artificially induced impairment were recruited to participate in the experiment. The experimental results revealed that the peak of the plantarflexion angle was decreased by 21.3%, 26.0%, and 46.5%, respectively, with the assistance of an exosuit when walking at the terrains of level ground (LG), upramps (UR), and upstairs (US). The root mean square (RMS) of the electromyography (EMG) signals of the tibialis anterior (TA) muscle was reduced by 26.9%, 32.5%, and 20.2%, respectively. These initial results demonstrate that the exosuit can effectively assist dorsiflexion at different terrains.Note to Practitioners—This paper has presented the design and evaluation of a soft exosuit equipped with multiple hinges and a vision system, which offers dorsiflexion assistance to improve ground clearance across various terrains. The multi-hinge structure facilitates effective assistance force transmission and acts as a mechanical limiter, preventing damage to the ankle joint from overstretching the Bowden cable due to control errors. The vision subsystem enables precise terrain recognition, facilitating tailored assistance based on the current terrain conditions. Moreover, a position-based control strategy is adopted based on the reference trajectory measured from the healthy side, enabling the exosuit to adapt to three terrains: LG, UR, and US. This work has the potential to advance the development of exosuits for practical applications significantly.

How to optimally synchronize exoskeleton powered assistance remains a problem that limits the broad application of such devices. Kinematic change during frequent switching between go and stop, a common and representative activity of daily living (ADL), makes it challenging to predict the gait phase and deliver assistance due to unpredictable movements. Conventional adaptive oscillators (AO) have been verified to be effective in gait phase prediction in steady-state walking. When walking cadences are changed, it usually requires multiple walking strides to synchronize the assistance, causing inaccurate or even unwanted force disturbances that can be dangerous in some cases. To solve this problem, a gait-based AO is proposed in this paper. It has two-level AO systems. The high-level AO learns from last stride and updates the low-level AO, which is designed to estimate the gait phase of the current stride in real time. This approach significantly decreases the time required to learn walking kinematics, while simultaneously improving the accuracy of predictions. Experiments were conducted on seven participants, and the results showed that the proposed gait-based AO can predict the gait phase faster, more accurate, and more stable than the conventional one during non-steady-state walking. Note to Practitioners—This article developed a new gait phase prediction method called gait-based adaptive oscillator. It aims to provide fast and reliable gait phase prediction in situations characterized by frequent transitioning between stop and go (non-steady-state walking). This method has the potential to be an alternative to existing phase prediction methods for robotic exoskeleton assisted walking in daily life, as it requires no model training before use while can synchronize exoskeleton assistance within two strides.

Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. We presented a fusion network with stacked denoise autoencoder and meta learning (SDA-NN-ML) to recognize gait phase and predict gait percentage from IMU signals. Experiments were conducted to detect the four lateral walking gait phases and predict their percentage across different speeds. The performance of SDA-NN-ML and Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) and Long Short Term Memory (LSTM) were evaluated. The cross-subject recognition accuracy of SDA-NN-ML (89.94%) decreased by 4.62% compared to the training accuracy, which outperformed SVM (8.60%), AdaBoost (5.61%), and LSTM (7.12%). For real-time and cross-subject prediction of gait phase percentage, the RMSE of SDA-NN-ML (0.2043) outperformed that of a single regression network (0.2426). With a signal noise ratio of 100:30, the cross-subject recognition accuracy decreased by a mere 5.70%, while the prediction result (RMSE) of SDA-NN-ML increased by 0.0167 when compared to the noise-free results. SDA-NN-ML demonstrates a stable multi-step-ahead prediction ability with an accuracy higher than 82.50% and an RMSE of less than 0.23 when the ahead time is less than 200 ms. The results demonstrated that the proposed method has high accuracy and robust performance in lateral walking gait recognition and prediction.

Assisting underwater movements improves divers' efficiency and reduces the risk of decompression sickness from physical activity. Although exoskeletons have been developed for numerous land-based scenarios, their application in underwater diving remains unexplored. This article proposes a soft underwater lower-limb exosuit designed to assist three aquatic movements: flutter kick, breaststroke kick, and underwater walk. We presented the mechanical design of the exosuit that is capable of assisting bidirectional leg movements in full kicking/gait cycle, while ensuring natural leg mobility without impeding normal leg function. A cascade force integral controller is also designed to resolve issues related to uncontrollable states and stiffness variations within the system. To verify the assistive performance of the system, experiments were conducted with nine participants to assess how the proposed exosuit aids in reducing metabolic cost across various motion patterns and frequencies. The findings indicate that the underwater exosuit effectively reduces the air consumption rate by$29.77\pm 7.68$% during flutter kick,$25.70\pm 5.99$% during breaststroke kick, and$18.35\pm 4.53$% during underwater walk.

Understanding the effects of different hip assistance modes is a fundamental step in the process of designing hip assistance devices and controllers that can provide better performance in terms of metabolic cost. We have developed and tested a soft exoskeleton for hip assistance, which includes three assistance modes: hip extension assistance (HEA), hip flexion assistance (HFA), and hip extension and flexion assistance (HEFA). A proportional derivative (PD) iterative learning controller based on the feedforward model was proposed to control the assistive force accurately. The three hip assistance modes were evaluated on seven male subjects walking on a treadmill at a speed of 5 km/h in two scenarios-first with a 15-kg backpack and then without any backpack. The net metabolic costs could be reduced during the loaded condition, compared with those under no exoskeleton condition, by 9.95%, 6.25%, and 15.28% for HEA, HFA, and HEFA, respectively. The reductions were found significant in HEA ( p=0.048) and HEFA ( p=0.005) modes, while the HFA mode ( p=0.202) was not found statistically significant. It indicates that the HEA and HEFA modes with the soft exoskeleton provide more benefit to the net metabolic cost compared with the HFA mode. The net metabolic costs reduced during the unloaded condition were 9.21%, 2.58%, and 13.05% for HEA, HFA, and HEFA, respectively. The improvements in the walking efficiency during both the conditions with the developed soft exoskeleton are demonstrated. Note to Practitioners-This article was motivated by the problem that how to reduce the metabolic cost most appropriately of walking by hip assistance of soft exoskeleton. In this article, we conduct a comparison of three hip assistance modes to discuss the balance of system weight and assistance efficiency. We then propose a PD iterative learning controller based on the feedforward model to track the desired assistive force accurately. Preliminary experiments suggest that the hip extension assistance (HEA) is more suitable than hip flexion assistance (HFA) for hip assistance during single motion assistance. Multiple motion assistance is more beneficial for metabolic cost reduction when the weight of the soft exoskeleton is the same. The experimental tests show that the proposed soft exoskeleton and the control algorithm are effective for walking assistance during the loaded condition. However, the performance of the hip assistance device is tested based on the treadmill walking only. In future research, we will conduct a performance evaluation of the soft exoskeleton on a complex road environment.

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

Lower-limb exoskeleton robots can effectively help patients with lower-limb disabilities caused by stroke or spinal cord injury to walk again. However, when faced with complex ground surfaces, patients find it difficult to quickly and accurately transmit the motion intention to the robot, resulting in falls or errors. In this article, we develop the vision-assisted autonomous lower-limb exoskeleton robot (VALOR). Based on the principles of human visual feedback and motion decision-making, a vision-assisted autonomous gait pattern planning method is proposed to improve the adaptability of the robot to the environment. The robot obtains environmental information via an RGB-D camera and extracts the ground object features that might affect gait. Then, the robot makes an autonomous decision according to the environmental features, robot state, and safety constraints. Lastly, a suitable step length and height are given to the parameterized gait pattern planning model of robot to assist with walking. The feasibility of the proposed method is verified on VALOR in a controlled indoor environment with limited obstacles, and our results demonstrate that the method can significantly improve robot adaptability to complex walking environments.

The important purpose of soft exosuit is to decrease the energy consumption of users by providing assistance. If there is something wrong with the judgment of the human gait during the assisting process, the assisting effect will be poor and even people's walking will be affected. A novel method is designed to predict human gait information in the paper, which utilizes the curve shape and mathematical characteristics of the Rayleigh oscillator equation in limit cycle to fit gait information. Only 1/4 gait cycle data is needed to input into the trained neural network to output an Rayleigh oscillator equation that can better predict the remaining gait cycle information. Data of four subjects on different terrains which include flat ground and upstairs are collected. Experiment results showed that the trained ANN model costs about 0.006s in CPU, it has a prediction speed similar to traditional prediction methods. Simultaneously the Rayleigh oscillator equation has good performance in predicting gait information, and it can show a higher stability and accuracy compared with traditional gait prediction methods.

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%.

Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer's performance. The wearer need different assistance modes to reduce the metabolic rate when walking on different terrains. Therefore, assistance modes need to be selected according to different terrains for the wearer of soft Exosuit. Recently, our team has designed a stable terrain classification and recognition system (TCRS) for the soft Exosuit to discriminate terrains and estimating environmental features. Through this system, soft Exosuit can perceive the environment to auxiliary control of the locomotion modes. A depth sensor with an inertial measurement unit(IMU) can acquire to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from the origin 3D point cloud. Then, they are classified to estimate terrain environmental features, including the incline angle of the slope, the width, and the stairs' height. Finally, the TCRS was evaluated by classifying and recognizing five basic terrains in three different experimental scenarios while the subject was wearing the soft Exosuit with the TCRS module. The results show that the terrain classification accuracy rate reaches 97.74 %, and the environmental features estimation error is less than 15 %. The promising results indicate the robustness and the potential application of the presented TCRS to provide proper auxiliary force to reduce the metabolic rate of wearers on different terrains.

The development of sharing technology makes it possible for expensive lower limb exoskeleton robots to be extensively employed. However, due to the uniqueness of gait pattern, it is challenging for lower limb exoskeleton robot to adapt to different wearers' gait patterns. Studies have shown that the gait pattern is affected by many physical factors. This paper proposes an individualized gait pattern generation (IGPG) method for sharing lower limb exoskeleton (SLEX) robot. First, the gait sequences are parameterized to extract gait features. Then, the Gaussian process regression with automatic relevance determination is used to establish the mapping relationships between the body parameters and the gait features, and the weights of each body parameters on gait pattern are also given. The gait features of an unknown subject can be predicted based on the training set. Finally, the individualized gait pattern is reconstructed by autoencoder neural network and scaling process based on predicted gait features. The experimental results show that the gait pattern predicted by IGPG is very similar to the subject's actual trajectory and has been successfully applied on the SLEX robot. With the help of sharing technology, the training set will be increased, and the prediction accuracy of individualized gait pattern will also be improved.