Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.

Recently people often purchase their daily needs at retail stores. Therefore, crowds might happen due to a manual queueing system. To overcome the problem, the smart system based on object detection has been conducted using several object detection methods. This study proposed YOLOv8 combined with transformer Real-Time Detection Transformer (RT-DETR) model to enhance the method performance in detecting the detail products. The intra-Class Variation method has been used to recognize the characteristics of the products such as size, color, and variant of the product. To validate the proposed model, three different datasets have been applied that is grocery dataset that displays products one by one in the training and validation process, the RPC-dataset that has many products in one image, and the D2S dataset with products that have varying lighting and stacked products. Results showed that the proposed model outperformed compared to other models, with a mean Average Precision (mAP) of 99.5% for the grocery dataset, 99.3% RPC-dataset, and 85.5% D2S dataset, respectively.

Localization is one of the problems that often appears in the world of robotics. Monte Carlo Localization (MCL) are the one of the popular algorithms in localization because easy to implement on issues Global Localization. This algorithm using particles to represent the robot position. MCL can simulated by Robot Operating System (ROS) using robot type is Pioneer3-dx. In this paper we will discuss about this algorithm on ROS, by analyzing the influence of the number particle that are used for localization of the actual robot position.

Perbandingan Kinerja Algoritma Optimasi pada Metode Random Forest untuk Deteksi Kegagalan Jantung

Sleep analysis is often used to identify sleep-related human health. In many cases, sleep disorders could cause a particular disease. One of the approaches to detect sleep disorders is by investigating human sleep stages. However, the selection of the proper electrocardiogram (ECG) features is still considered challenging and becomes an issue to achieve the performance of the algorithm used. Therefore, it is necessary to investigate which ECG features are very significant to the performance of the algorithm. In this study, the support vector machine (SVM) method has been utilized to classify sleep stages into two classes namely awake and sleep. In order to improve the classification performances, an optimization method of grid search was used to find the best parameters of the SVM. Feature selection of information gain was then used to find the most significant ECG features. To validate the performance results, one leave-subject out cross-validation has been conducted during the implementation. There were ten subjects involved in this implementation. The ECG signals from those ten subjects were used to differentiate awake from sleep state. Based on the results, our method obtained an average accuracy of 85.46% a precision of 84.05% and a recall of 85.44% respectively.