Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Context-aware recommender system for multi-user smart home

Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches

Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks

In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks

Navigation and interaction in a real-scale digital mock-up using natural language and user gesture

This paper tries to demonstrate a very new real-scale 3D system and sum up some firsthand and cutting edge results concerning multi-modal navigation and interaction interfaces. This work is part of the CALLISTO-SARI collaborative project. It aims at constructing an immersive room, developing a set of software tools and some navigation/interaction interfaces. Two sets of interfaces will be introduced here: 1) interaction devices, 2) natural language (speech processing) and user gesture. The survey on this system using subjective observation (Simulator Sickness Questionnaire, SSQ) and objective measurements (Center of Gravity, COG) shows that using natural languages and gesture-based interfaces induced less cyber-sickness comparing to device-based interfaces. Therefore, gesture-based is more efficient than device-based interfaces.FUI CALLISTO-SAR

Indoor localization systems-tracking objects and personnel with sensors, wireless networks and RFID

Advances in ubiquitous mobile computing and rapid spread of information systems have fostered a growing interest in indoor location-aware or location-based technologies. In this paper we will introduce the primary technologies used in indoor localization systems by classifying them in three categories: Non-RF technologies, Active-RF technologies and Passive-RF technologies. Both commercialized products and research prototypes in all categories are involved in our discussion. The Passive-RF technologies are further divided into “Mobile tag” and “Mobile reader” systems. We expect such classification can cover most of the indoor localization systems. Features of these systems are briefly compared at the end of this paper

Application of Sensor in Mining Machinery to Recognise Rock Surfaces

the success of automation applications in the mining industry traditionally has not been well. In many of these cases the benefits of automation have been advertised as the definitive solution to a wide variety of problems faced by the mining industry, such as increased safety and improved productivity. These applications have in many cases been introduced prematurely without adequate consideration of the rigors of the mining environment. As a result, effective technology has often been labeled as a failure before it has had a chance to demonstrate its true capability. Therefore, we believe that a major requirement is essential to develop automation technologies for mining systems or sub-systems, which needs minimal operator input requirement. This can be achieved in several ways. First, by narrowing the domain in which the automated mining system must operate such that less complex automation technology can be applied robustly. Alternately, more sophisticated control technologies are required that can react to the wider range of operating mining scenarios resulting from an uncertain, dynamic and very unstructured geological highly variable and unpredictable environment. Automation of shearer machines, with the help of an opto-tactile sensor, should make the machine capable to detect the coal-rock interface in the roof and the floor. In this article an attempt has been made to apply, in association with an existing shearer machine, a newly developed opt-tactile sensor to detect different types of material layers where a shearer machine can operate at the longwall face of underground coal mines. The proposed tactile sensor should be capable to detect different types of materials (coal, limestone, sandstone, and shell) recognizing their surface textures