Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses. Four transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method. The classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input. This study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application. The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.

The accurate recognition of hand motion intentions is an essential prerequisite for efficient human-machine interaction (HMI) systems such as multifunctional prostheses and rehabilitation robots. Surface electromyography (sEMG) signals and muscle shape change (MSC) signals which are usually detected with different types of sensors have been used for human hand motion intention recognition. However, using different sensors to measure sEMG and MSC respectively, it would be inconvenient and add deploying difficulty for human-machine interaction systems. In this study, a novel flexible and stretchable sensor was fabricated with a nano gold conductive material, which could simultaneously sense both sEMG and MSC signals. Accordingly, a wireless signal acquisition device was developed to record both sEMG and MSC signals with the fabricated hybrid sensors. The performance of the proposed in-situ dual-mode signal measurement (IDSM) system was evaluated by the recording signal quality and the accuracy of hand gesture recognition. The results demonstrated that by using two pairs of the hybrid sensors, the proposed IDSM system could obtain two-channel sEMG at a noise level of about 0.89 μVrms and four-channel MSC with a resolution of about 0.1 Ω. For a recognition task of 11 classes of hand gestures, the results showed that only with two pairs of the hybrid sensors, the average accuracy over all the subjects was 95.6 ± 2.9%, which was about 7% higher than that with two-channel sEMG and six-channel accelerometer signals. These results suggest that the proposed IDSM method would be an efficient way to simplify the human-machine interaction system with fewer sensors for high recognition accuracy of hand motions.

Background: Since the 1990s, neuromodulation technology has experienced rapid advancements, providing new therapeutic approaches for clinical rehabilitation in neurological disorders. The objective of this study is to utilize CiteSpace and VOSviewer to investigate the current research status, key topics, and future trends in the field of neuromodulation technology over the past decade.Methods: Relevant literature in the field of neuromodulation technology published in Web of Science database from January 1, 2014 to June 18, 2024 were retrieved, and imported into CiteSpace and VOSviewer for visualization. VOSviewer was used for counties, institutions, authors and keywords analyses. CiteSpace was used for presentation visualization analysis of co-cited references, keywords clusters and bursts.Results: This study encompasses a total of 1,348 relevant publications, with the number of publications showing an increasing trend year by year. The most significant growth was observed between 2020 and 2021. The United States, China and the United Kingdom are the three leading countries with high output in this regard. The top three institutions in terms of the publication volume are Harvard Medical School, the University of Toronto and Stanford University. Keyword co-occurrence and cluster analysis identified that deep brain stimulation, transcranial magnetic stimulation, transcranial direct current stimulation, and focused ultrasound stimulation are the most widely used central nerve stimulation techniques in neuromodulation. The treatment of intractable chronic pain also emerged as a key focus within neuromodulation techniques. The recent keywords bursts included terms such as recovery, movement, nucleus, modeling and plasticity, suggesting that the future research trend will be centered on these areas.Conclusion: In conclusion, neuromodulation technology is garnering increasing attention from researchers and is currently widely used in brain diseases. Future research is expected to delve deeper, particularly into exploring deep brain structure stimulation targets and restoring motor function based on neuroplasticity theory.

Estimating joint torque in athletes provides important information for understanding neuromuscular performance and optimizing high-intensity training. While surface electromyography (sEMG) offers a non-invasive measure of muscle activation, existing methods often struggle to balance prediction accuracy, computational efficiency, and generalizability across different movement conditions. This study proposes a Hierarchical Three-Stage Feature Selection (H3FS) strategy for efficient and stable sEMG-based joint torque estimation. H3FS integrates domain knowledge, statistical and model-driven screening to generate compact, physiologically meaningful feature sets tailored to various application contexts. Using isokinetic movement data from the shoulder, elbow, and knee joints of 39 young athletes, H3FS identified the optimal configuration, ELATE-3, comprising logarithmic mean absolute value (LMAV), waveform length (WL), and Teager–Kaiser energy operator (TKEO). The study also introduces an Integrated Performance Index (IPI) to evaluate prediction accuracy and computational cost. Results indicate that XGBoost with ELATE-3 achieves the best balance between accuracy and real-time performance (average R² = 0.85, RMSE = 16.2, IPI = 0.013). It outperforms both traditional and data-driven feature sets, demonstrating strong generalization and low latency across joints, making it highly suitable for low-latency estimation on the adopted computational platform. The proposed strategy offers a systematic, scalable solution for joint torque estimation, supporting athletic training optimization and neuromuscular assessment, with potential future applications in rehabilitation monitoring.

Underwater communication devices typically suffer from large size and high power consumption, which pose significant challenges for real-time monitoring of swimmers’ heart rate and distance. To tackle these challenges, this study successfully developed a wearable acoustic-Bluetooth dual model communication-based real-time heart rate monitoring and ranging system (WARM) for swimmers by implementing an integrated miniaturized acoustic transducer design, narrow-pulse OOK modulation, and acoustic multipath interference suppression techniques. The final self-developed system measures 47 mm × 36 mm × 18 mm and weighs 54 g. Six swimming volunteers were recruited to conduct underwater real-time heart rate monitoring and distance measurement experiments for performance evaluation of this self-developed system. Experimental results demonstrate that within an effective communication range of 2500 cm, the system achieved an average transmission power consumption of 52–58 mW, a frame loss rate of only 1.1%, and a mode-switching time of 1–2 s between the underwater acoustic and Bluetooth transmissions. In addition, the system enabled real-time heart rate monitoring and underwater ranging, with an average ranging error below 50 cm. These results verify the reliability and stability of the proposed system and provide a useful reference for the design and application of wearable underwater communication systems.