Boo-Ho Yang

The ring sensor: a new ambulatory wearable sensor for twenty-four hour patient monitoring

This paper describes the development of a ring sensor for twenty-four hour patient monitoring. The ring is packed with LEDs and photodetectors where the technology of pulse oximetry is implemented for blood oxygen saturation monitoring. The measured data are transmitted to a computer through a digital wireless communication link. The ring sensor is worn by the patient at all times, hence the health status is monitored 24 hours a day. Detailed descriptions of the hardware and the software of the ring sensor will be presented. Also, the effects of motion artifact and ambient light will be investigated.

A twenty-four hour tele-nursing system using a ring sensor

This paper presents the development of the ring sensor to monitor a patient 24 hours a day for a tele-nursing system. The ring sensor is worn by the patient at all times, hence the health status is monitored 24 hours a day. The sensors packed into the ring include LEDs with different wavelengths, and technologies of photoplethysmography and pulse oximetry are implemented on the ring. The sensor data are transmitted to a computer through the digital wireless communication link and the patient status is analyzed continually and remotely. Any trait of abnormal health status and possible accidents is detected by analyzing the sensor data. A combination of a global receiver and multiple local ones are used to estimate the patients location and activity. Both the physiological data and the position information can be used to make an accurate decision as to whether a warning signal must be sent to a medical professional caring the patient. An issue of power reduction for miniaturization of the ring sensor is also addressed.

Development of the ring sensor for healthcare automation

Abstract This paper presents the development of a miniaturized telemetered ambulatory monitoring device in a ring configuration. The device, called ring sensor, is worn by the patient at all times, hence the health status is monitored 24 hours a day. The ring is equipped with LEDs and photo detectors where the technology of pulse oximetry is implemented for monitoring pulse waves and blood oxygen saturation. The measured data are transmitted to a computer through a digital wireless communication link and the patient health status is analyzed continuously and remotely. Any trait of abnormal health status and possible accidents is detected by analyzing the sensor data. Detailed descriptions of the hardware and the software of the ring sensor including a noise protection algorithm will be presented. Also, unique features of the 24 hour patient monitoring system using the ring sensor will be discussed.

Design of a artifact-free wearable plethysmographic sensor

The ring sensor is a compact, wearable device that was originally designed for continuous physiological monitoring of a human body. In this paper, the authors propose a new design of the ring sensor that can alleviate the artifacts of motion and ambient light significantly.

Skill acquisition from human experts through pattern processing of teaching data

An approach to the teaching of manipulative skills is developed and applied to a deburring robot. Teaching data acquired from a human expert, who can perform an efficient job, are processed on a computer to attain his skilful manipulation strategies. The strategies are described by a group of control laws that relate sensor signals to motion commands. Sensor signals are processed by using pattern recognition techniques to interpret sensor information and to allow the robot to recognize the state of the process. The control laws designate which control action the robot should take in response to each signal pattern generated in the process. A method for driving a compact set of discrimination functions for real-time recognition of signal patterns is also discussed. The method is implemented on a direct-drive deburring robot. It is demonstrated that the robot can mimic the skilful manipulation of the human expert and perform the task efficiently.<<ETX>>

Progressive learning and its application to robot impedance learning

An approach to learning control using an excitation scheduling technique is developed and applied to an impedance learning problem for fast robotic assembly. Traditional adaptive and learning controls incur instability depending on the reference inputs provided to the system. This technique avoids instability by progressively increasing the level of system excitation. Called progressive learning, it uses scheduled excitation inputs that allow the system to learn quasistatic parameters associated with slow input commands first, followed by the learning of dynamic parameters excited by fast input commands. As learning progresses, the system is exposed to a broader range of input excitation, which nonetheless does not incur instability and unwanted erratic responses. In robotic assembly, learning starts with a slow, quasistatic motion and goes to a fast, dynamic motion. During this process, the stiffness terms involved in the impedance controller are learned first, then the damping terms and finally by the inertial terms. The impedance learning problem is formulated as a model-based, gradient following reinforcement learning. The method allows the suppression of excessive parameter changes and thereby stabilizes learning. By gradually increasing the motion speed command, the internal model as well as the control parameters can be learned effectively within a focused, local area in the large parameter space, which is then gradually expanded as speed increases. Several strategies for motion speed scheduling are also addressed.

Hybrid linguistic/numeric control of deburring robots based on human skills

The authors develop a method of modeling a human manipulative skill using human linguistic knowledge about the task. A global nonlinear structure of human control behavior is constructed based on the linguistic information, and all functionalities used by the linguistic structure are identified from human demonstration data. Mapping between sensor space and human mental space for input signals is discussed to elucidate human skills. Techniques for selecting significant features extracted from sensor signals and reducing the dimension of the sensor space are developed. The techniques were applied to a direct-drive deburring robot to verify the feasibility of the method.<<ETX>>

Modeling of finger photoplethysmography for wearable sensors

This paper describes the development of an optophysiological model of a finger in conjunction with a ring-type photoplethysmography device (the ring sensor). It describes the photoplethysmographic effects due to the relative displacement and rotation of a finger to a ring-type optoelectric device that monitors the arterial pulsation noninvasively and continuously. Numerical simulations and experiments were conducted to verify and evaluate this model.

A new approach of adaptive reinforcement learning control

A new learning algorithm for connectionist networks that solves a class of optimal control problems is presented. The algorithm, called adaptive reinforcement learning algorithm, employs a second network to model immediate reinforcement provided from the task environment and adaptively identify it through experience. Output perturbation and correlation techniques are used to translate mere critic signals into useful learning signals for the connectionist controller. Compared with the direct approaches of reinforcement learning, this algorithm shows faster and guaranteed improvement in the control performance. Robustness against inaccuracy of the model is also discussed.

Robot impedance generation from logic task description through progressive learning

In this paper, we present a new approach to learning robot impedance control parameters from a logic task description. In this approach, we first describe the desired behaviour of a robot for performing a given task at a logic level. A simple logic branch control using a quasi-static force-to-motion map is created based on the logic description. The progressive learning method is then applied to this logic branch control in order to create a dynamic control, i.e. impedance control, for performing the task quickly and dynamically. Starting with a simple logic description about the robot behaviour, the system can develop a fully dynamic impedance control by progressively learning the process dynamics. The problem is formulated in the context of high-speed insertion, and the proposed approach is verified through simulation.