This paper presents BatchQR, a mobile APP that can batch read the densely arranged QR codes attached to caps of the tubes and vials in clinical and biological labs. The basic idea of BatchQR is to detect each code in the image and then decode in an one-by-one manner. However, the unique characteristics of the QR code and the application scenario bring technical challenges: First, off-the-shelf lightweight object detection mechanisms are unable to distinguish those densely arranged codes that are highly similar to each other; second, the focus area of the camera is limited, which blurs or distorts parts of the image. To this end, we propose a lightweight code detection mechanism, which can adaptively adjust operating parameters to identify densely arranged QR codes in practice. We also propose a simple but effective image refocus mechanism, which takes an auto-focused image and multiple refocused ones, and then replaces the blurred or distorted code parts with the high-quality counterparts in the refocused images. Comprehensive experimental results show that BatchQR can read 160-180 Version 1-L QR codes in batch with 90%-95% accuracy in 10-14s, which is only 4% of the time consumed by the regular QR code reader in the same situation.

Facial paralysis makes patients lose their facial movements, which can incur eye damage even blindness since patients are incapable of blinking. We design and implement a pair of smart glasses iBlink to assist facial paralysis patients to blink. The basic idea is to monitor the normal side of the face with a camera and stimulate the paralyzed side, so that the blink of the both eyes become symmetric. Our contributions are: First, we propose an eye-blink detection mechanism based on support vector machine (SVM), which can detect asymmetric blinks of patients under various illumination conditions with an accuracy above 99 percent. Our eye-image library for training the model is published online for further related studies, which contains more than 30,000 eye images. Second, we design and implement an automatic stimulation circuits to generate electrical impulse for stimulating the patient's facial nerve branches, which can configure operational parameters in a self-adaptive manner for different patients. Third, we implement the entire iBlink system, which integrates the two functions above and a communication function module for tele-medicine applications. We conduct experiments in a hospital to obtain the design basis and verify effectiveness of our device.

Facial paralysis is a disease caused by nerve damage, which can make patients lose facial movements. Facial paralysis patients usually have muscles on one side of the face noticeably droop, which seriously impacts the person's quality of life as shown in Fig. [skull]. Worse still, the eye on the affected side is unable to blink and will become dry and infected by debries, which can incur eye damage even blindness. To the best of scientists' knowledge, the paralysis is due to the pressure incurred by infection in the tunnel containing main trunk of facial nerves, where the tunnel is inside of the people's head termed as the Facial canal. In this demo, we present iBlink [1], a novel system to help paralysis patients to blink. Paralysis usually occurs in just one side of the face, and clinical trials show that electrical stimulation could trigger blink. Based on such observations, the basic idea of iBlink is to monitor the normal side of the face with a camera and stimulate the paralysed side, so that eye-movements of the both sides become symmetric.

Facial paralysis makes patients lose their facial movements, which can incur eye damage even blindness since patients are incapable of blinking. The paralysis usually occurs on just one side of the face, and clinical trials show that electrical stimulation could trigger blink. Based on such observations, we design and implement a pair of smart glasses iBlink to assist facial paralysis patients to blink. The basic idea is to monitor the normal side of the face with a camera and stimulate the paralysed side, so that the blink of the both eyes become symmetric. To the best of our knowledge, this is the first piece of wearable device for facial paralysis therapy. Our contributions are: First, we propose an eye-blink detection mechanism based on deep convolutional neural network (CNN), which can detect asymmetric blinks of patients under various illumination conditions with an accuracy above 99%. Our eye-image library for training CNN models is published online for further related studies, which contains more than $30,000$ eye images. Second, we design and implement an automatic stimulation circuits to generate electrical impulse for stimulating the patient's facial nerve branches, which can configure operational parameters in a self-adaptive manner for different patients. Third, we implement the entire iBlink system, which integrates the two functions above and a communication function module for tele-medicine applications. Moreover, we conduct clinical trials in a hospital, in order to obtain the design basis and verify effectiveness of our device.