Teaching Chinese sign language with a smartphone

Background There is a large group of deaf-mutes worldwide, and sign language is a major communication tool in this community. It is necessary for deaf-mutes to be able to communicate with others who are capable of hearing, and hearing people also need to understand sign language, which produces a great demand for sign language tuition. Even though there have already been a large number of books written about sign language, it is inefficient to learn sign language through reading alone, and the same can be said on watching videos. To solve this problem, we developed a smartphone-based interactive Chinese sign language teaching system that facilitates sign language learning. Methods The system provides a learner with some learning modes and captures the learner's actions using the front camera of the smartphone. At present, the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary, and the system will return the top three retrieved candidates;thus, it can remind the learners what the sign is. Results This system provides interactive learning to enable a user to efficiently learn sign language. The system adopts an algorithm based on point cloud recognition to evaluate a user's sign and costs about 700ms of inference time for each sample, which meets the real-time requirements. Conclusion This interactive learning system decreases the communication barriers between deaf-mutes and hearing people.

Rare-earth ions coordination enhanced ratiometric fluorescent sensing platform for quantitative visual analysis of antibiotic residues in real samples

Levofloxacin(LVFX)as a representative drug of quinolone antibiotics is widely used in clinical,and its residues enriched in water bodies and sideline products seriously damage human health.It is imperative to develop a real-time/on-site sensing method for monitoring residual antibiotics.Here,we report a portable sensing platform by utilizing a composite fluorescent nanoprobe constructed by the cerium ions(Ce^(3+))coordination functionalized Cd Te quantum dots(QDs)for the visual and quantitative detection of LVFX residues.This fluorescent probe provides a distinct color variation from red to green,which shows a good linear relationship to LVFX residues concentrations in the range of 0-6.0μmol/L with a sensitive limit of detection(LOD)of 16.3 nmol/L.The smartphone platform with Color Analyzer App installed,which could accomplish quantified detection of LVFX in water,milk,and raw pork with a LOD of 27.9nmol/L.The facile sensing method we proposed realizes rapid visualization of antibiotics residual in the environment and provides a practical application pathway in food safety and human health.

Artificial intelligence-assisted colorimetry for urine glucose detection towards enhanced sensitivity,accuracy,resolution,and anti-illuminating capability

Colorimetry often suffers from deficiency in quantitative determination,susceptibility to ambient illuminance,and low sensitivity and visual resolution to tiny color changes.To offset these deficiencies,we incorporate deep machine learning into colorimetry by introducing a convolutional neural network(CNN)with powerful parallel processing,self-organization,and self-learning capabilities.As a proof of concept,a plasmonic nanosensor is proposed for the colorimetric detection of glucose by coupling Benedict’s reagent with gold nanoparticles(AuNPs),which relies on the assemble of AuNPs into dendritic nanochains by Cu2O.The distinct difference of refractive index between Cu2O and Au and the localized surface plasmon resonance coupling effect among AuNPs leads to a broad spectral shift as well as abundant color changes,thereby providing sufficient data for selflearning enabled by machine learning.The CNN is then used to fully diversify the learning and training of the images from standard samples under different ambient conditions and to obtain a classifier that can not only recognize tiny color changes that are imperceptible to human eyes,but also exhibit high accuracy and excellent anti-environmental interference capability.This classifier is then compiled as an application(APP)and implanted into a smartphone with Android environment.306 clinical urine samples were detected using the proposed method and the results showed a satisfactory correlation(87.6%)with that of a standard blood glucose test method.More importantly,this method can be generalized to other applications in colorimetry,and more broadly,in other scientific domains that involve image analysis and quantification.