In recent years, lower-limb exoskeletons have been applied to assist people with weak mobility in daily life, which requires enhanced adaptability to complex environments. To achieve a smooth transition between different assistive strategies and provide proper assistance at the desired timing during locomotion on various terrains, two significant issues should be addressed: the delay of locomotion mode recognition (LMR) and the accuracy of gait phase estimation (GPE), which are yet critical challenges for exoskeleton controls. To tackle these challenges, a high-level exoskeleton control, including a depth sensor-based LMR method and an adaptive oscillator-based GPE approach, is developed in this study for terrain-adaptive assistive walking. An experimental study was conducted to evaluate the effectiveness and usability of the proposed control in a real-world scenario. Experimental results suggested that the depth sensor-based LMR method can detect the locomotion mode change 0.5 step ahead of the assistive strategy switch of the leading leg, while the average environment classification accuracy across five subjects was higher than 98 $\%$ . The accuracy is comparable with the state-of-the-art LMR methods, but its predictive capability is beyond existing LMR methods applied in lower-limb exoskeletons. Moreover, the adaptive oscillator-based GPE approach accurately estimated the user’s gait phase during locomotion on various terrains, with a root-mean-square (RMS) gait phase reset error of only $\pm$ 0.27 $\%$ , outperforming the literature standard.

The quality of breast ultrasound imaging is greatly affected by the contact force of the probe, which largely requires experienced sonographers to complete the clinical examination. We propose a flexible and fully autonomous ultrasound scanning system for breast ultrasound imaging. It consists of an ultrasound machine, a dual robotic arms system, a multi-structured light system, a human–computer interaction system, and a flexible ultrasound probe clamping device (FUPCD). First, the dynamics model of the FUPCD was analyzed, and a closed-loop force control strategy was established. We then implemented an automatic scanning system. The hysteresis characteristics of the FUPCD and transient response of the force controller were experimentally verified. The system could keep the steady-state error less than $\pm$ 5 $\%$ within 0.5 s. Second, the performance of the control system to maintain constant contact force at different scanning speeds ( $<$ 20 mm/s) was evaluated. In a series of comparative studies, the fluctuation range of the target contact force applied by the flexible automatic scanning was 10 $\%$ . The force control capability of the system was 30.84 times higher than that of traditional manual scanning, 18.48 times higher than that of flexible automatic scanning (FC off), and 24.3 times higher than that of rigid automatic scanning (FC on). Furthermore, the effectiveness of the FUPCD arc design and flexibility was verified. In addition, the change trend of the average grayscale level (GSL) of the ultrasound image was basically the same as the contact force, demonstrating that the stability and repeatability of the ultrasound image can be improved by controlling the stability of the contact force. In conclusion, the proposed system can simplify the workflow of breast ultrasound examination and improve the diagnostic capabilities of the ultrasound imaging system. Note to Practitioners—The motivation of this study is to solve the problem of poor image reproducibility in breast ultrasound scanning, but the proposed system can also be applied to ultrasound scanning of other parts of the body. The position, direction, and contact force of the ultrasound probe affects the image quality and repeatability of the ultrasound, thereby affecting the diagnostic ability. Therefore, we propose and develop a flexible and fully autonomous breast ultrasound scanning system. We aimed to improve the autonomy and stability of the scanning process by designing end-to-end automated scanning strategies, flexible clamping devices, and closed-loop force control strategies. Among them, the fully automatic three-dimensional perception and trajectory planning can provide global fitting capabilities to different forms of breast surfaces and control the probe to maintain the best contact posture. At the same time, the fully automatic workflow reduces additional interference and reduces the complexity of the workflow. The flexible ultrasound probe clamping device improves the passive applicability to flexible tissues and reduces the resistance in the scanning process. The closed-loop force control strategy can adjust the contact force in real time. Experiments verified that the repeatability could be evaluated by contact force. A series of human–machine comparison experiments verified the advancement and effectiveness of the system. In future research, we will further solve the problem of abnormalities and repeatability of ultrasound images acquired during the scanning process by combining the multi-modal feedback of images and forces.

The 6-min walk distance (6MWD) and the Fugl-Meyer assessment lower-limb subscale (FMA-LE) of the stroke patients provide the critical evaluation standards for the effect of training and guidance of the training programs. However, gait assessment for stroke patients typically relies on manual observation and table scoring, which raises concerns about wasted manpower and subjective observation results. To address this issue, this paper proposes an intelligent rehabilitation assessment method (IRAM) for rehabilitation assessment of the stroke patients based on sensor data of the lower limb exoskeleton robot. Firstly, the feature parameters of the patient were collected, including age, height, and duration, etc. The sensor data of the exoskeleton robot were also collected, including joint angle, joint velocity, and joint torque, etc. Secondly, a gait feature model was constructed to deduce the walking gait parameters of the patient according to the sensor data of the exoskeleton, including the support phase to swing phase ratio, step length and leg lift height of the patient, etc. Then, the 6MWD and FMA-LE values were collected by traditional methods, feature parameters, gait parameters and human-machine interaction parameters (joint torque) of the patient were adopted to train the rehabilitation assessment model. Finally, the assessment model was trained by a machine-learning based algorithm. The new stroke patients’ the 6MWD and FMA-LE values can be predicted by the trained model. The experimental results present that the prediction accuracy for the 6MWD and FMA-LE values reach to 85.19% and 92.66%, respectively.

Exoskeleton robots play an essential role in the gait rehabilitation of lower limbs in stroke patients. Existing lower limb exoskeleton robots commonly employ reference trajectories to perform gait rehabilitation training for patients. However, a significant gait deviation may occur between the gait trajectory of the wearer and the reference trajectory due to the following reasons. They are the physically flexible connection and the applied compliance control strategy between the wearer and the exoskeleton. This paper proposes a gait deviation correction method (GDCM) to decrease the gait deviation for lower limb exoskeleton robots. First, the gait curves of the exoskeleton and the wearer were obtained by using the clinical gait analysis (CGA) curve as a reference trajectory for the exoskeleton. Then, a gait correction model was trained by a database containing the CGA curve, the gait trajectory of both the exoskeleton and wearer obtained from 15 participants, and their body feature parameters such as height, weight, and hip-length. Finally, the gait correction model was introduced into the control system to modify the input trajectory of the exoskeleton. The experimental results showed that the gait trajectories of the wearer’s hip and knee after correction were closer to the CGA curve than without modification. The minor gait trajectory deviation should lead to a better efficacy of the training with the robot if the reference trajectory is assumed to be optimal.

Predicting the next foot placement of humans during walking can help improve compliant interactions between humans and walking aid robots. Previous studies have focused on foot placement estimation with wearable inertial sensors after heel-strike, but few have predicted foot placements in advance during the early swing phase. In this study, a Bayesian inference-based foot placement prediction approach was proposed. Possible foot placements were modeled as a probability distribution grid map. With selected foot motion feature events detected sequentially in the early swing phase, the foot placement probability map could be updated iteratively using the feature models we built. The weighted center of the probability distribution was regarded as the predicted foot placement. Prediction errors were evaluated with collected walking data sets. When testing with the data from inertial measurement units, the prediction errors were (5.46 cm ± 10.89 cm, −0.83 cm ± 10.56 cm) for cross-velocity walking data and (−4.99 cm ± 12.31 cm, −11.27 cm ± 7.74 cm) for cross-subject–cross-velocity walking data. The results were comparable to previous works yet the prediction could be made earlier. For the subject who walked with more stable gaits, the prediction error can be further decreased. The proposed foot placement prediction approach can be utilized to help walking aid robots adjust their pose before each heel-strike event during walking, which will make human–robot interactions more compliant. This study is also expected to inspire additional probabilistic gait analysis works.

The capability to generate electricity from human motion can reduce the battery requirements for wearable devices. The key challenge faced by wearable energy harvesters is the generation of sufficient power without interfering with the wearer. To our knowledge, currently available joint-motion energy harvesters are all for level walking (LW). In this study, we developed an energy-harvesting exoskeleton that can generate electricity during LW, downhill walking (DW), and stair descent (SD) without sensors. Bi-directional knee motion is transformed to the unidirectional rotation of the generator by one-way bearing and appropriate gear train. The average electricity generated by each leg for the three modes of walking is $5.4 \pm 0.8{\rm{\ W\ }}({\text{LW},\ 5{\rm{\ km}}/\mathrm{h}})$ , $6.5 \pm 0.6{\rm{W\ }}({\text{DW},\ 5{\rm{\ km}}/\mathrm{h}})$ , and $8.2 \pm 0.4{\rm{W\ }}({\text{SD},4{\rm{\ stairs}}/\mathrm{s}})$ , respectively. The Pearson coefficients of the knee angle under exo-on and no-exo conditions are 0.995 (LW), 0.996 (DW), and 0.999 (SD), respectively. The cost of harvesting is −0.006 (LW) and −0.01 (DW). The increase in the metabolic rate in energy harvesting on condition is merely 2.3% (LW) and 1.6% (DW) compared to the no-exo condition. There is no significant increase in the metabolic cost while generating electricity $(P\ = \ 0.363,$ LW; $P = 0.662$ , DW). In summary, the knee energy-harvesting exoskeleton developed in this study can generate substantial and sustainable electricity with little extra effort, which indicates its potential as a means to charge-powered portable exoskeletons and prosthetics.