• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.503-509
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• 2009

The development of technology has led to ubiquitous health care service, which enables many patients to receive medical services anytime and anywhere. For the ubiquitous health care environment, real-time measurement of biomedical signals is very important, and the medical instruments must be small and portable or wearable. So, such devices have been developed to measure biomedical signals. In this study, we develop the biomedical monitoring device which is sensing the PPG signal, one of the useful signal in the field of ubiquitous healthcare. We design a watch-like biomedical signal monitoring system without a finger probe to prevent the user's inconvenience. This system obtains the PPG from the radial artery using a sensor in the wrist band. But, new device developed in this paper is easy to get the motion artifacts. So, we proposed new algorithm removing the motion artifacts from the PPG signal. The method detects motion artifacts by changing the degree of brightness of the light source. If the brightness of the light source is reduced, the PPG pulses will disappear. When the PPG pulses have disappeared completely, the remaining signal is not the signal that results from the changing blood flow. We believe that this signal is the motion artifact and call it the noise reference signal. The motion artifacts are removed by subtracting the noise reference signal from the input signal. We apply this algorithm to the system, so we can stabilize the biomedical monitoring system we designed.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.277.2-277.2
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• 2014

For next-generation electronic applications, human-machine interface devices have recently been demonstrated such as the wearable computer as well as the electronic skin (e-skin). For integration of those systems, it is essential to develop many kinds of components including displays, energy generators and sensors. In particular, flexible sensing devices to detect some stimuli like strain, pressure, light, temperature, gase and humidity have been investigated for last few decades. Among many condidates, a pressure sensing device based on organic field-effect transistors (OFETs) is one of interesting structure in flexible touch displays, bio-monitoring and e-skin because of their flexibility. In this study, we have investigated a flexible e-skin based on highly sensitive, pressure-responsive OFETs using microstructured ferroelectric gate dielectrics, which simulates both rapidly adapting (RA) and slowly adatping (SA) mechanoreceptors in human skin. In SA-type static pressure, furthermore, we also demonstrate that the FET array can detect thermal stimuli for thermoreception through decoupling of the input signals from simultaneously applied pressure. The microstructured highly crystalline poly(vinylidene fluoride-trifluoroethylene) possessing piezoelectric-pyroelectric properties in OFETs allowed monitoring RA- and SA-mode responses in dyanamic and static pressurizing conditions, which enables to apply the e-skin to bio-monitoring of human and robotics.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.69-77
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• 2015

The Microsoft Kinect is a motion sensing input device which is widely used for many motion recognition applications such as fitness, sports, and rehabilitation. Until now, most of remote rehabilitation systems with the Microsoft Kinect have allowed the user or patient to do rehabilitation or fitness by following the motion of a video screen. However in this paper we propose a smart remote rehabilitation system with the Microsoft Kinect motion sensor and a wearable ECG sensor which can allow patients to offer monitoring of the individual's performance and personalized feedback on rehabilitation exercises. The proposed noble smart remote rehabilitation is able to monitor and measure the state of the patient's condition during rehabilitation exercise, and transmits it to the prescriber. This system can give feedback to a prescriber, a doctor and a patient for improving and recovering motor performance. Thus, the efficient rehabilitation training service can be provided to patient in response to changes of patient's condition during exercise.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.