The increasing elderly population necessitates increased geriatric care. However, a shortage of caregivers leads to a risk of falls and bedsores in the elderly, both of which result in severe injuries. Whilst wearable devices, and vision sensors have been adopted for monitoring. However, these sensors come with limitations, impacting comfort and privacy for the elderly. To address these challenges, non-intrusive sensing devices integrated into the environment offer promising value for continuous elderly activity monitoring. This study uses a panel sensor embedded with four sensors, consisting of two piezoelectric sensors and two pressure sensors. It is placed beneath the mattress. The position classification encompasses five distinct positions: off-bed, sitting, lying in the center, lying on the left side, and lying on the right side. To find the best position for placing the panel, the positions of the panel and the combination of panel sensors positions are evaluated for five-bed positions classification. As a result, the best position for a sensor panel was in the middle of the bed (position No. 3), with an accuracy of 97.12%. This suggests the panel sensor should be placed at 123.5 cm, measured from the top of the bed. Moreover, in the case of placing two-panel sensors, the most effective arrangement comprises placing one-panel sensor placed at the the bed-top (position No. 1) and the other in the middle of the bed (position No. 3), yielding accuracy 99.93%.

The widespread adoption of the internet has transformed communication, work, and information access, emphasizing the need for high-speed connectivity. Accurate prediction of network latency, particularly Ping, is essential for enhancing user experiences and optimizing network efficiency. This study focuses on predicting Ping latency using data from ADSL internet speed tests, incorporating variables such as geographical coordinates (longitude and latitude), subscribed package download and upload speeds, and internet provider band. The dataset is split into training and testing sets for model evaluation. Through our analysis of ADSL speed test data, we achieve a Mean Absolute Percentage Error (MAPE) of 11.98% for Ping prediction. These results provide valuable insights for stakeholders aiming to enhance the reliability and efficiency of broadband services. For network operators and service providers, our results provide a roadmap for optimizing infrastructure and refining management approaches to deliver superior service quality. Likewise, end-users stand to benefit from improved network performance, leading to smoother online interactions and heightened satisfaction.

Computer programming is popularized in 21st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system.

This study proposes a data labelling scheme for bed position classification task. The labelling scheme provides a set of bed position for the purpose of preventing the bed fall and bedsore injuries which seriously imperil the aging people health. Most of the elderly fall down when they attempt to get out of bed with unassisted bed exit. Also, there is a high possibility of rolling out of bed when an elderly lies close to the edge of the bed. In addition, a bedridden person, who cannot reposition by him/herself, has a high risk of bedsores. Repositioning in every two hours alleviates the prolonged pressure over on the body. We collected the data from a specific set of bed sensor and classified the signal into five positions on the bed, which are off-bed, sitting, lying center, lying left, and lying right. These five positions are the fundamental information for developing a model to capture the movement of the elderly on the bed. The precaution strategy is then able to be designed for the bed fall and bedsore prevention. The data of the five different positions are manually annotated by observing the synchronized video through a specially designed workbench. The combination of the positions of off-bed, sitting, and lying is used to detect a bed exit situation, and the combination of the positions in the lying state, i.e. lying center, lying left, and lying right, is used to detect the rolling out of bed situation. Moreover, to notify for reposition assisting in the bedridden, the three lying positions are used to calculate the time of the abiding position.

This study proposes bed posture classification using a Neural Network and a Bayesian Network for elderly care. The data are collected in a hospital. The on-bed postures are analyzed into five types, those are, out of bed, sitting, lying down, lying left, and lying right, by using signals from a sensor panel (composed of piezoelectric sensors and pressure sensors). The sensor panel is placed under a mattress in the thoracic area. To eliminate the effect of weight and the bias between different types of sensors, the sensing data are normalized into a range of 0 to 1 by the unity-based normalization (or feature scaling) method. In addition, a Bayesian Network is adopted to estimate the likelihood of consecutive postures. The results from both a Neural Network and Bayesian Network estimation are combined by the weighted arithmetic mean. The experimental results yield the maximum accuracy of posture classification when the coefficient of Bayesian probability and a Neural Network are set to 0.7 and 0.3 respectively.