Motor Behavior Regulation of Rat Robots Using Integrated Electrodes Stimulated by Micro-Nervous System.

As a cutting-edge technology, animal robots based on living organisms are being extensively studied, with potential for diverse applications in the fields of neuroscience, national security, and civil rescue. However, it remains a significant challenge to reliably control the animal robots with the objective of protecting their long-term survival, and this has seriously hindered their practical implementation. To address this issue, this work explored the use of a bio-friendly neurostimulation system that includes integrated stimulation electrodes together with a remote wireless stimulation circuit to control the moving behavior of rat robots. The integrated electrodes were implanted simultaneously in four stimulation sites, including the medial forebrain bundle (MFB) and primary somatosensory cortex, barrel field (S1BF). The control system was able to provide flexibility in adjusting the following four stimulation parameters: waveform, amplitude, frequency, and duration time. The optimized parameters facilitated the successful control of the rat's locomotion, including forward movement and left and right turns. After training for a few cycles, the rat robots could be guided along a designated route to complete the given mission in a maze. Moreover, it was found that the rat robots could survive for more than 20 days with the control system implanted. These findings will ensure the sustained and reliable operation of the rat robots, laying a robust foundation for advances in animal robot regulation technology.

An optimized EEGNet decoder for decoding motor image of four class fingers flexion.

As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.

Flexible wide-range multidimensional force sensors inspired by bones embedded in muscle.

Flexible sensors have been widely studied for use in motion monitoring, human‒machine interactions (HMIs), personalized medicine, and soft intelligent robots. However, their practical application is limited by their low output performance, narrow measuring range, and unidirectional force detection. Here, to achieve flexibility and high performance simultaneously, we developed a flexible wide-range multidimensional force sensor (FWMFS) similar to bones embedded in muscle structures. The adjustable magnetic field endows the FWMFS with multidimensional perception for detecting forces in different directions. The multilayer stacked coils significantly improved the output from the µV to the mV level while ensuring FWMFS miniaturization. The optimized FWMFS exhibited a high voltage sensitivity of 0.227 mV/N (0.5-8.4 N) and 0.047 mV/N (8.4-60 N) in response to normal forces ranging from 0.5 N to 60 N and could detect lateral forces ranging from 0.2-1.1 N and voltage sensitivities of 1.039 mV/N (0.2-0.5 N) and 0.194 mV/N (0.5-1.1 N). In terms of normal force measurements, the FWMFS can monitor finger pressure and sliding trajectories in response to finger taps, as well as measure plantar pressure for assessing human movement. The plantar pressure signals of five human movements collected by the FWMFS were analyzed using the k-nearest neighbors classification algorithm, which achieved a recognition accuracy of 92%. Additionally, an artificial intelligence biometric authentication system is being developed that classifies and recognizes user passwords. Based on the lateral force measurement ability of the FWMFS, the direction of ball movement can be distinguished, and communication systems such as Morse Code can be expanded. This research has significant potential in intelligent sensing and personalized spatial recognition.

Fabrication and DC-Bias Manipulation Frequency Characteristics of AlN-Based Piezoelectric Micromachined Ultrasonic Transducer.

Due to their excellent capabilities to generate and sense ultrasound signals in an efficient and well-controlled way at the microscale, piezoelectric micromechanical ultrasonic transducers (PMUTs) are being widely used in specific systems, such as medical imaging, biometric identification, and acoustic wireless communication systems. The ongoing demand for high-performance and adjustable PMUTs has inspired the idea of manipulating PMUTs by voltage. Here, PMUTs based on AlN thin films protected by a SiO2 layer of 200 nm were fabricated using a standard MEMS process with a resonant frequency of 505.94 kHz, a -6 dB bandwidth (BW) of 6.59 kHz, and an electromechanical coupling coefficient of 0.97%. A modification of 4.08 kHz for the resonant frequency and a bandwidth enlargement of 60.2% could be obtained when a DC bias voltage of -30 to 30 V was applied, corresponding to a maximum resonant frequency sensitivity of 83 Hz/V, which was attributed to the stress on the surface of the piezoelectric film induced by the external DC bias. These findings provide the possibility of receiving ultrasonic signals within a wider frequency range, which will play an important role in underwater three-dimensional imaging and nondestructive testing.