The dental disease is a common disease for a human. Screening and visual diagnosis which are currently performed in clinics possibly cost a lot in various manners. Along with the progress of the Internet of Things (IoT) and Articial Intelligence (AI), the internet-based intelligent system have shown great potential in applying home-based healthcare. Therefore, a smart dental Health-IoT system based on intelligent hardware, deep learning and mobile terminal is proposed in this paper, aiming at exploring the feasibility of its application on In-home dental healthcare. Moreover, a smart dental device is designed and developed in this study to perform the image acquisition of teeth. Based on the data set of 12, 600 clinical images collected by the proposed device from 10 private dental clinics, an automatic diagnosis model trained by MASK R-CNN is developed for the detection and classication of 7 different dental diseases including decayed tooth, dental plaque, uorosis and periodontal disease, with the diagnosis accuracy of them reaching up to 90%, along with high sensitivity and high specicity. Following the one-month test in 10 clinics, compared with that last month when the platform is not used, the mean diagnosis time reduces by 37.5% for each patient, helping explain the increase of the number of treated patients by 18.4%. Furthermore, application software (App) on mobile terminal for client-side and for dentist-side are implemented to provide service of pre-examination, consultation, appointment, and evaluation.

Unstaffed retail shop has been emerging in the past years and significantly affected conventional shopping styles. In this area, unmanned retail container plays an important role, it can greatly influence the user shopping experience, the traditional way based on weighing sensors cannot sense what the customer is taking. This paper proposes a smart unstaffed retail shop scheme based on artificial intelligence (AI) and the internet ofthings (IoT), aiming at exploring the feasibility of implementing the unstaffed retail shopping style. Based on the data set of 11, 000 images in different scenarios that containing 10 different types of stock keeping unit (SKU), an end-to-end classification model trained by the MASK-RCNN method is developed for SKU counting and recognition, and the proposed solution in this study is able to achieve 97.7% counting accuracy and 98.7% recognition accuracy on the test dataset, which indicates that the system can make up for the deficiency of traditional unmanned container.

Unstaffed retail shops have emerged recently and been noticeably changing our shopping styles. In terms of these shops, the design of vending machine is critical to user shopping experience. The conventional design typically uses weighing sensors incapable of sensing what the customer is taking. In the present study, a smart unstaffed retail shop scheme is proposed based on artificial intelligence and the internet of things, as an attempt to enhance the user shopping experience remarkably. To analyze multiple target features of commodities, the SSD ( $300\times 300$ ) algorithm is employed; the recognition accuracy is further enhanced by adding sub-prediction structure. Using the data set of 18, 000 images in different practical scenarios containing 20 different type of stock keeping units, the comparison experimental results reveal that the proposed SSD ( $300\times 300$ ) model outperforms than the original SSD ( $300\times 300$ ) in goods detection, the mean average precision of the developed method reaches 96.1% on the test dataset, revealing that the system can make up for the deficiency of conventional unmanned container. The practical test shows that the system can meet the requirements of new retail, which greatly increases the customer flow and transaction volume.

Recently, virtual-reality (VR) has been an emerging technology, to this regard, it is widely employed by therapists to provide rich training tasks for the purpose of motor rehabilitation in clinics. Meanwhile, along with the progress of sensing technologies as means for the interaction with virtual environment, a large amount of data, such as motor trajectory, foot pressure or electromyography, is measured via VR-based motor training tasks and is considered as important clues for functional evaluations. However, very few study thoroughly applied the sensor-based data for motor assessment, instead, evaluation scales, such as TEMPA or Fugl-Meyer, were highly relied. In this study, a VR upper-limb motor training system was proposed for stroke rehabilitation. Clinical trials with 22 stroke patients were performed to exanimate the effectiveness of the propose VR system. Moreover, a variety of motor indicators derived via motion trajectory were proposed. Further, integrating multi-model data, such as motion trajectory, task performance and evaluation scales, machine-learning method was applied to develop evidence-based assessment models in order to evaluate upper-limb motor function. The results indicated that the proposed VR system was significantly effective for motor rehabilitation. Also, a few motor indicators were found significantly different between pre and post trials and were highly correlated with the evaluation scales. Finally, with the fusion of multi-model data, the accuracy rate of machine-learning assessment model was up to 92.72% which revealed its great potential for clinical use.

The smart unmanned vending machine using machine vision technology suffers from the sharp decrease of detection accuracy due to the incomplete image collection of items by monocular camera in complex environment, and the lack of obvious features in dense stacking of items. In this article, a binocular camera system is designed to effectively solve the problems of distortion and coverage caused by monocular camera. Besides, an image-stitching algorithm is developed to splice the images captured by the camera, which reliefs the burden of computation for back-end recognition processing brought by the binocular camera. A new neural network structure-the YOLOv3-TinyE is proposed based on YOLOv3-tiny model. Based on the dataset of 21,000 images captured in real scenarios containing 20 different type of beverages, the comparison experimental results show that YOLOv3-TinyE model achieves the mean average precision of 99.15%, and the inference speed is 2.91 times faster than that of YOLOv3 model, and the detection accuracy of YOLOv3-TinyE model based on binocular vision is higher than that based on monocular vision. The results suggest that the designed method achieves the goal in terms of inference speed and average precision, that is, it is able to satisfy the requirements for real-world applications.