With the recent development of a digital rehabilitation system, research on the rehabilitation of amputees is accelerating. However, research on rehabilitation systems for patients with amputation of the lower extremities is insufficient. For the rehabilitation of amputees, it is important to maintain muscle mass through the improvement of muscle movement memory, continuous rehabilitation learning, and motivation to improve efficiency. The rehabilitation system in a virtual environment is convenient in that there is no restriction on time and space because rehabilitation training of amputees is possible without removing/attaching general prosthetic legs and robot prosthetic legs. In this paper, we propose an XR rehabilitation system for patients with lower extremity amputation to improve the motivational aspect of rehabilitation training. The proposed method is a system that allows patients and clinical experts to perform rehabilitation in the same environment using two XR equipment called HoloLens 2. The content was provided in the form of a game in which the number of movements of amputees was allocated as scores to enhance rehabilitation convenience and motivation aspects. The virtual 3D model prosthetic leg used in-game content worked through the acquisition and processing of the patient’s actual muscle EMG (ElectroMyoGraphy) signal. In order to improve reactivity, there was a time limit for completing the operation. The classified action should be completed by the amputee within the time limit, although the number of times set as the target. To complete the operation, the amputee must force the amputation area to exceed an arbitrarily set threshold. The evaluation results were evaluated through an independent sample t-test. we contribute to the development of digital rehabilitation simulation systems. XR rehabilitation training techniques, operated with EMG signals obtained from actual amputation sites, contribute to the promotion of rehabilitation content in patients with amputation of the lower extremities. It is expected that this paper will improve the convenience and rehabilitation of rehabilitation training in the future.

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

In this paper, we propose a detection method for salient objects whose eyes are focused on gaze tracking; this method does not require a device in a single image. A network was constructed using Neg-Region Attention (NRA), which predicts objects with a concentrated line of sight using deep learning techniques. The existing deep learning-based method has an autoencoder structure, which causes feature loss during the encoding process of compressing and extracting features from the image and the decoding process of expanding and restoring. As a result, a feature loss occurs in the area of the object from the detection results, or another area is detected as an object. The proposed method, that is, NRA, can be used for reducing feature loss and emphasizing object areas with encoders. After separating positive and negative regions using the exponential linear unit activation function, converted attention was performed for each region. The attention method provided without using the backbone network emphasized the object area and suppressed the background area. In the experimental results, the proposed method showed higher detection results than the conventional methods.

Recently, low-latency services for large-capacity data have been studied given the development of edge servers and wireless mesh networks. The 3D data provided for augmented reality (AR) services have a larger capacity than general 2D data. In the conventional WebAR method, a variety of data such as HTML, JavaScript, and service data are downloaded when they are first connected. The method employed to fetch all AR data when the client connects for the first time causes initial latency. In this study, we proposed a prefetching method for low-latency AR services. Markov model-based prediction via the partial matching (PPM) algorithm was applied for the proposed method. Prefetched AR data were predicted during AR services. An experiment was conducted at the Nowon Career Center for Youth and Future in Seoul, Republic of Korea from 1 June 2022 to 31 August 2022, and a total of 350 access data points were collected over three months; the prefetching method reduced the average total latency of the client by 81.5% compared to the conventional method.

Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, we propose and analyze a deep learning model based on recurrent neural networks (RNNs) optimized for the MI sensor, such that it can detect and classify data that are relatively irregular and diverse compared to the EMG and acoustic signals. Our proposed method combines the long short-term memory (LSTM) and gated recurrent unit (GRU) models to detect and classify metal objects from signals acquired by an MI sensor. First, we configured various layers used in RNN with a basic model structure and tested the performance of each layer type. In addition, we succeeded in increasing the accuracy by processing the sequence length of the input data and performing additional work in the prediction process. An MI sensor acquires data in a non-contact mode; therefore, the proposed deep learning approach can be applied to drone control, electronic maps, geomagnetic measurement, autonomous driving, and foreign object detection.