Gesture Recognition Based on a Convolutional Neural Network–Bidirectional Long Short-Term Memory Network for a Wearable Wrist Sensor with Multi-Walled Carbon Nanotube/Cotton Fabric Material

Flexible pressure sensors play a crucial role in detecting human motion and facilitating human–computer interaction. In this paper, a type of flexible pressure sensor unit with high sensitivity (2.242 kPa−1), fast response time (80 ms), and remarkable stability (1000 cycles) is proposed and fabricated by the multi-walled carbon nanotube (MWCNT)/cotton fabric (CF) material based on a dip-coating method. Six flexible pressure sensor units are integrated into a flexible wristband and made into a wearable and portable wrist sensor with favorable stability. Then, seven wrist gestures (Gesture Group #1), five letter gestures (Gesture Group #2), and eight sign language gestures (Gesture Group #3) are performed by wearing the wrist sensor, and the corresponding time sequence signals of the three gesture groups (#1, #2, and #3) from the wrist sensor are collected, respectively. To efficiently recognize different gestures from the three groups detected by the wrist sensor, a fusion network model combined with a convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) neural network, named CNN-BiLSTM, which has strong robustness and generalization ability, is constructed. The three types of Gesture Groups were recognized based on the CNN-BiLSTM model with accuracies of 99.40%, 95.00%, and 98.44%. Twenty gestures (merged by Group #1, #2, and #3) were recognized with an accuracy of 96.88% to validate the applicability of the wrist sensor based on this model for gesture recognition. The experimental results denote that the CNN-BiLSTM model has very efficient performance in recognizing different gestures collected from the flexible wrist sensor

Morphological and Molecular Characterization, and Demonstration of a Definitive Host, for Sarcocystis masoni from an Alpaca (Vicugna pacos) in China

Only 18S rDNA sequences of Sarcocystis spp. in South American camelids (SACs) are deposited in GenBank as references, and the definitive host of S. masoni in SACs is still unclear. Here, S. masoni sarcocysts detected in an alpaca (Vicugna pacos) in China were investigated with the aid of light (LM) and transmission electron (TEM) microscopy, and characterized using four genetic markers, i.e., 18S rDNA, 28S rDNA and ITS, and the mitochondrial cox1. Additionally, the life cycle of the parasite was completed via experimental animal infection. Under LM, S. masoni sarcocysts exhibited numerous 1.3–2.1 μm conical protrusions. Under TEM, the sarcocyst wall contained conical, cylindrical, or irregular-shaped villar protrusions, similar to type 9j. Two dogs (Canis familiaris) fed S. masoni sarcocysts shed sporocysts with a prepatent period of 8–9 days. The newly obtained 18S rDNA sequences showed 98.4–100% identity with those of S. masoni in SACs previously deposited in GenBank. Interestingly, the newly obtained sequences of 18S rDNA and mitochondrial cox1 shared 99.6–100% and 98.2–98.5% identity, respectively, with those of S. cameli in dromedary camels (Camelus dromedaries). Phylogenetic analysis based on sequences of 18S rDNA, 28S rDNA, or mitochondrial cox1 revealed that S. masoni has a close relationship with Sarcocystis spp. in ruminants. The relationship between S. masoni and S. cameli deserves to be further clarified in the future