Visually challenged people (VCPs) face many difficulties in their routine life. Usually, in many cases, they need to depend upon others, which makes them unconfident in an unfamiliar environment. Thus, in this paper, we present an aid that helps in detecting obstacles and water puddles in their way. This system comprises a walking stick and Android-based applications (APPs). The walking stick is embedded with Raspberry Pi and programmable interface controller (PIC) as a control kernel, sensors, a global position system (GPS) module, and alert-providing components. Sensors help to detect obstacles, and the VCP is informed through vibrations or a buzzer according to the obstacle detected. The GPS module receives the coordinates of the VCP’s location, and the location can be tracked by parents using an APP. Another important APP is used, called an emergency APP, by which the VCP can communicate with parents or friends immediately by just shaking his/her cell phone or pushing the power button four times in 5 s in panic situations. We used fewer components to make the device simple, lighter, and cozy with very good features. This device will help VCPs to live an independent life up to some extent (with security), which ultimately will increase their confidence level in an unknown environment.

Predictive maintenance techniques can determine the conditions of equipment in order to evaluate when maintenance should be performed. Thus, it minimizes the unexpected device downtime, lowers the maintenance costs, extends equipment lifecycle, etc. Therefore, this article developed a predictive maintenance mechanism with the construction of a test platform and data analysis along with machine learning. The information transmission of sensors was based on Raspberry Pi via the TCP/IP (Transmission Control Protocol/Internet Protocol) communication protocol. The sensors used for environmental sensing were implemented on the programmable interface controller and the data were stored in time sequence. A statistical analysis software platform was adopted for data preprocessing, modelling, and prediction to provide necessary maintenance decision. Using multivariate analysis users can obtain more information about the equipment’s status, and the administrator can also determine the operational situation before unexpected device anomalies. The developed modules are decisively helpful in preventing unpredictable losses, thus improving the quality of services.

In recent years the increased rate of the aging population has become more serious. With aging, the elderly sometimes inevitably faces many problems which lead to slow walking, unstable or weak limbs and even fall-related injuries. So, it is very important to develop an assistive aid device. In this study, a fuzzy controller-based smart walker with a distributed robot operating system (ROS) framework is designed to assist in independent walking. The combination of Raspberry Pi and PIC microcontroller acts as the control kernel of the proposed device. In addition, the environmental information and user postures can be recognized with the integration of sensors. The sensing data include the road slope, velocity of the walker, and user’s grip forces, etc. According to the sensing data, the fuzzy controller can produce an assistive force to make the walker moving more smoothly and safely. Apart from this, a mobile application (App) is designed that allows the user’s guardian to view the current status of the smart walker as well as to track the user’s location.

No abstract