International Conference on Control, Automation and Systems 

About: International Conference on Control, Automation and Systems is an academic conference. The conference publishes majorly in the area(s): Mobile robot & Control theory. Over the lifetime, 5848 publications have been published by the conference receiving 27178 citations.

International Conference on Control, Automation and Systems

Abstract: The following topics are dealt with: intelligent control; spacecraft guidance; process control; PID control; dextrous manipulation; mobile robot; signal processing; UAV flight control; nonlinear control; embedded systems; obstacle avoidance; virtual reality; time-delay system and adaptive control.

Development of a robot balancing on a ball

Abstract: This paper proposes a robot balanced on a ball. In contrast to an inverted pendulum with two wheels, such as the Segway Human Transporter, an inverted pendulum using a ball can traverse in any direction without changing its orientation, thereby enabling stable motion. Such robots can be used in place of the two-wheeled robots. The robot proposed in this paper is equipped with three omnidirectional wheels with stepping motors that drive the ball and two sets of rate gyroscopes and accelerometers as attitude sensors. The robot has a simple design; it is controlled with a 16-bit microcontroller and runs on Ni-MH batteries. It can not only stand still but also traverse on floor and pivot around its vertical axis. Inverted pendulum control is applied in two axes for attitude control, and commanded motions are converted into velocity commands for the three wheels. The mechanism, control method, and experimental results are described in this paper.

Smartphone based fall detection system

Abstract: This paper describes the design of a smartphone based fall detection system and characterizes the preliminary efficacy of the proposed system in activities of daily living (ADLs). Using the embedded sensors available in a smartphone (i.e., accelerometer, gyroscope and magnetometer), kinematic analysis of movement can be performed in real-time, allowing for continuous monitoring of fall status. Fall sensing thresholds are defined based on angular rate of change (TH1), maximum acceleration (TH2), and maximum attitude change (TH3). TH1 is measured from the resultant pitch and roll angular velocity vector and defined as 3.1 rad/s (∼180°/s). TH2 is measured from the resultant acceleration vector and defined as 1.6 g. TH3 is measured from the resultant vector of the pitch and roll angles, and defined at 0.59 rad (39°). A proof-of-concept study was performed on five ADL tasks: 1) comfortable walking, 2) stand-to-seated posture, 3) seated-to-standing posture, 4) pivoting at the waist to pick up an object, and 5) stand-to-seated-to-laying transition. No trials violated the defined thresholds for fall detection, signifying no false positives. These results are important for the definition of machine learning algorithms, currently under development, to minimize false positive and false negative fall detection events.

Activity monitoring from real-time triaxial accelerometer data using sensor network

Abstract: With the advancement in the wireless sensor network technologies, it is possible to use them for health monitoring from remote location. The monitoring of human movement can provide valuable information about individual's daily activities. Wireless sensor node with 3-axes accelerometer were used to monitor daily activities i.e. walking, running, resting and dangerous activities such as falling. Three axes MEMS accelerometer measure body's acceleration and transmit acceleration data to the base station attached to PC. On the PC the real time accelerometer data is processed and various inferences like walking, running and falling, are taken accordingly. This paper presents the methodologies and parameters used for chest case worn sensor as well as for wrist worn sensor and compare them. The paper also presents various design aspects of real time data acquistion from accelerometer sensor attached to sensor node. Firmware for sensor unit is developed in nesC which use tinyos as a platform. Results shows chest is better position to activity monitoring and fall detection. In total 16 experiments for chest, fall is detected in 13 with an accuracy of 81%.