In traditional hand function assessment, patients and physicians always need to accomplish complex activities and rating tasks. This paper proposes a novel wearable glove system for hand function assessment. A sensing system consisting of 12 nine-axis inertial and magnetic unit (IMMU) sensors is used to obtain the acceleration, angular velocity, and geomagnetic orientation of human hand movements. A complementary filter algorithm is applied to calculate the angles of joints after sensor calibration. A virtual hand model is also developed to map with the glove system in the Unity platform. The experimental results show that this glove system can capture and reproduce human hand motions with high accuracy. This smart glove system is expected to reduce the complexity and time consumption of hand kinematics assessment.

This paper presents a novel reconfigurable crawling robot based on an origami twisted tower structure. Compared with other origami structures, the twisted tower can achieve extension, contraction, and bending motions as the flexible body parts in robotic designs. The kinematics of a one-layer twisted tower were analyzed with rotation and bending angles. The mechanical properties of the one-layer, two-layer, and four-layer twisted towers were compared with compression experiments. A rope-motor-driven crawling robot was designed to realize forward, backward, left-turning, and right-turning motions. Two types of crawling robot with specific sliding feet were developed to adapt to different ground conditions: one made of rubber, and the other embedded with an electromagnet. The experimental results show that the proposed robots can move at an average forward speed of 0.48 cm/s on a wooden desk, and at 0.52 cm/s forward speed or 0.65 cm/s backward speed on an iron platform.

We present an image-matching-based automated algorithm capable of accurately determining the self-rotational speed of cancer cells in an optically-induced electrokinetics-based microfluidic chip. To automatically track a specific cell in a video featuring more than one cell, a background subtraction technique was used. To determine the rotational speeds of cells, a reference frame was automatically selected and curve fitting was performed to improve the stability and accuracy. Results show that the algorithm was able to accurately calculate the self-rotational speeds of cells up to ~150 rpm. In addition, the algorithm could be used to determine the motion trajectories of the cells. Potential applications for the developed algorithm include the differentiation of cell morphology and characterization of cell electrical properties.

An electrochemical HbA1c sensor with high sensitivity and good specificity is proposed based on the electrochemical immune principle. The reproducibility and conductivity of the electrode are improved by depositing gold nanoparticles (AuNPs) on the surface of the screen-printed electrode (SPE). The HbA1c antibodies are immobilized on the surface of the modified electrode by adsorption to capture the HbA1c in the sample. The hindering effect of HbA1c on the electrode transfer reaction was exploited as the HbA1c detection mechanism. The electrode’s properties were characterized by electrochemical impedance spectroscopy (EIS), and the measurement properties of the electrode were analyzed using differential pulse voltammetry (DPV) and cyclic voltammetry (CV). The experimental results show that the peak current signal of the electrochemical immunosensor produced a linear response to HbA1c in the concentration range of 20–200 μg/mL, a linear relationship coefficient of 0.9812, a detection limit of 15.5 µg/mL, and a sensitivity of 0.0938 µA/µg·mL⁻¹. The sensor delivered satisfactory repeatability, stability, and anti-interference performance. Due to its small size, high sensitivity, and wide linear detection range, it is expected to play a significant role in managing diabetes at home.

To accurately recognize ordinary handwritten Chinese characters, it is necessary to recognize the normative level of these characters. This study proposes methods to quantitatively evaluate and recognize these characters based on their similarities. Three different types of similarities, including correlation coefficient, pixel coincidence degree, and cosine similarity, are calculated between handwritten and printed Song typeface Chinese characters. Eight features are derived from the similarities and used to verify the evaluation performance and an artificial neural network is used to recognize the character content. The results demonstrate that our proposed methods deliver satisfactory evaluation effectiveness and recognition accuracy (up to 98%~100%). This indicates that it is possible to improve the accuracy in recognition of ordinary handwritten Chinese characters by evaluating the normative level of these characters and standardizing writing actions in advance. Our study can offer some enlightenment for developing methods for the identification of handwritten Chinese characters used in transaction processing activities.

Our group has reported that Melan-A cells and lymphocytes undergo self-rotation in a homogeneous AC electric field, and found that the rotation velocity of these cells is a key indicator to characterize their physical properties. However, the determination of the rotation properties of a cell by human eyes is both gruesome and time consuming, and not always accurate. In this paper, a method is presented to more accurately determine the 3D cell rotation velocity and axis from a 2D image sequence captured by a single camera. Using the optical flow method, we obtained the 2D motion field data from the image sequence and back-project it onto a 3D sphere model, and then the rotation axis and velocity of the cell were calculated. After testing the algorithm on animated image sequences, experiments were also performed on image sequences of real rotating cells. All of these results indicate that this method is accurate, practical, and useful. Furthermore, the method presented there can also be used to determine the 3D rotation velocity of other types of spherical objects that are commonly used in microfluidic applications, such as beads and microparticles.