Bioelectrical impedance (BI) at popliteal regions was measured using a bioelectrical impedance measurement system (BIMS), which employs the multi-frequency and the two-electrode method. Experiments were performed as follows. First, a constant AC current of 800 µA was applied to the popliteal regions (left and right) and the BI was measured at eight different frequencies from 10 to 500 kHz. When the applied frequency greater than 50 kHz was applied to human's popliteal regions, the BI was decreased significantly. Logarithmic plot of impedance vs. frequency indicated two different mechanisms in the impedance phenomena before and after 50 kHz. Second, the relationship between resistance and reactance was obtained with respect to the applied frequency using BI (resistance and reactance) acquired from the popliteal regions. The phase angle (PA) was found to be strongly dependent on frequency. At 50 kHz, the PA at the right popliteal region was 7.8 o slightly larger than 7.6 o at the left popliteal region. Third, BI values of extracellular fluid (ECF) and intracellular fluid (ICF) were calculated using BIMS. At 10 kHz, the BI values of ECF at the left and right popliteal regions were 1664.14 Ω and 1614.08 Ω, respectively. The BI values of ECF and ICF decreased sharply in the frequency range of 10 to 50 kHz, and gradually decreased up to 500 kHz. Logarithmic plot of BI vs. frequency shows that the BI of ICF decreased noticeably at high frequency above 300 kHz because of a large decrease in the capacitance of the cell membrane.

The bioelectrical impedance (BI) for the young and the elderly was measured using bioelectrical impedance spectroscopy (BIS). First, while applying a current of 600 µA to the foot and hand, BI was measured at 50 frequencies ranging from 5 to 1000 kHz. The BI for young subjects was considerably lower than that for old subjects since young subjects have more lean mass (hydration). The prediction marker was 0.74 for young subjects and 0.78 for old subjects. Second, a Cole-Cole diagram was obtained for young subjects and old subjects, indicating the different characteristic frequencies. At 50 kHz, the average phase angle was 7.8 o for young subjects whereas that was 6.1 o for old subjects. Third, BIVA was analyzed for young subjects and old subjects.

The bioelectrical impedance (BI) at the inner forearms was measured using bioelectrical impedance measurement system (BIMS), which employs the multi-frequency and the two-electrode method. Experiments were performed as follows. First, while applying a constant alternating current of 800A to the inner region of the forearms, BI (Z) was measured at nineteen frequencies ranging from 5 to 500 kHz. The prediction marker (PM) was calculated for right and left forearm. The resistance (R) and the reactance (Xc) were simultaneously measured during impedance measurement. Second, a Cole-Cole plot (relationship between reactance and resistance) was obtained for left and right forearm, indicating the different characteristic frequencies (f c ). Third, the phase angle was obtained, indicating strong dependence on the applied frequency.

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A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (ω res ), and angular variation (θ res ) were calculated using MATLAB. When the measured values of SVM_Acc, ω res , and θ res were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ω res was larger than 1.75 rad/s (TH2), and θ res was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ω res , and θ res were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences. In this study, a threshold-based fall recognition algorithm using Received: Jan. 19, 2017, Revised: Jun. 23, 2017, Accepted: Jan. 30, 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/ licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.