The purpose of this paper is to investigate a method for detecting a fall-down event based on body's vertical displacement. The vertical displacement is computed from data of a wearable device with a tri-axial accelerometer and three single axis gyroscopes. In order to remove the intrinsic offsets in these sensors, an ofset compensation method is proposed. The effectiveness of the fall-down detection is evaluated by experiments with subjects. Experiments of various kinds of fall-down are carried out. As a result, a threshold to detect the fall-down is fbund. This threshold is 70%of the distance from the ground to the mounted device.

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In order to prevent from an external injury in case of the fall for the aged people, a hip protector is commonly used. However, it is difficult to attach it to the body for a long time, because of difficulty to wear. In order to solve these problems, we are developing the system which prevents externally caused injury by expanding an air bag at the time of a fall. It is necessary to operate an air bag by detecting a fall before the impulse of a fall occurs, in order to develop this system. In this study, we reviewed the algorithm of the fall process, and determined the inflating trigger signals from the algorithm. To determine the triggering signal for the air bag, the mimicking fall, walking and jogging has been performed. The accelerometer was attached to the16younger healthy subjects, and the48mimicking falls were carried out. This experiment was approved by the ethic committee of Faculty of Engineering, Chiba University and written informed consent was obtained by each subject. The free fall acceleration could be observed around100to300[ms]before falling down completely. However, misdetection occurred while using the detection algorithm in jogging, which used the accelerometer.

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Signal analysis methods for EEG signal are applied in blind signal separation, artifact removal, and feature extraction. These methods are utilized in time-domain or frequency-domain. A fundamental problem in EEG signal is that signal from the scalp exhibits nonstationarity and is mixed spatiotemporally. In this study, we analyzed EEG signal using a combination method with ICA(Independent Component Analysis)and semiparametric approach. Theta is associated with creative inspiration, deep mediation, and attention. During the episodic memory retrieval, the combination method is used to analyze data of7channels, in which EEG data from6electrodes and one EMG data are recorded every5ms. Subjects are required to push a right-side button when they recognized a presented word-pair is the same one as they previously remembered. Three seconds length EEG data are recorded before and after button pressed, then the artifact (eye blinking)is removed by ICA. Our proposed combination method is more effective than parametric AR model in context of extracting theta activity(4Hz to8Hz)during the episodic memory retrieval.

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To grasp the conditions of patients and implantable artificial hearts, it is important to monitor the blood flow rate continuously and noninvasively. However, it is difficult to monitor the pump flow rate especially in an implantable axial flow blood pump, because the power consumption of the axial flow blood pump has neither linearity nor uniqueness with respect to the pump flow rate. In this study, we develop a miniaturized mass flow meter that uses centrifugal force for discharged patients with an axial flow blood pump. This mass flow meter measures the centrifugal force corresponding to the mass flow rate in the curved tube, and implements compensation for the static pressure. Because the strain gauges are attached outside of the curved tube, the mass flow meter has no blood contact point, resulting in a compact design. The sensing areas are determined based on the computational fluid dynamic analysis. To evaluate measurement performances, the mass flow meter was compared with the conventional ultrasonic flow meter. As a result, the measurement error ranging from0. 0to5. 0l/min was less than 0. 5l/min. The tracking performance of pulsation flow was approximately equivalent to that of the conventional flow meter.

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