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In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use.

We proposed and tested a method to estimate sleep period time (SPT) using electrodermal activity (EDA) signals. Eight healthy subjects and six obstructive sleep apnea patients participated in the experiments. Each subject's EDA signals were measured at the middle and ring fingers of the dominant hand during polysomnography (PSG). For nine of the 17 participants, wrist actigraphy was also measured for a quantitative comparison of EDA- and actigraphy-based methods. Based on the training data, we observed that sleep onset was accompanied by a gradual reduction of amplitude of the EDA signals, whereas sleep offset was accompanied by a rapid increase in amplitude of EDA signals. We developed a method based on these EDA fluctuations during sleep-wake transitions, and applied it to a test dataset. The performance of the method was assessed by comparing its results with those from a physician's sleep stage scores. The mean absolute errors in the obtained values for sleep onset, offset, and period time between the proposed method, and the results of the PSG were 4.1, 3.0, and 6.1 min, respectively. Furthermore, there were no significant differences in the corresponding values between the methods. We compared these results with those obtained by applying actigraphic methods, and found that our algorithm outperformed these in terms of each estimated parameter of interest in SPT estimation. Long awakening periods were also detected based on sympathetic responses reflected in the EDA signals. The proposed method can be applied to a daily sleep monitoring system.

No abstract

Owing to advancements in daily physiological monitoring technology, diverse healthcare applications have emerged recently. The monitoring of skin conductance responses has extensive feasibility to support healthcare applications such as detecting emotion changes. In this study, we proposed a highly wearable and reliable galvanic skin response (GSR) sensor that measures the signals from the back of the user. To enhance its wearability and usability, we employed flexible conductive foam as the sensing material and designed it to be easily attachable to (and detachable from) a wide variety of clothes. We evaluated the sensing reliability of the proposed sensor by comparing its signal with a reference GSR. The average correlation between the two signals was 0.768; this is sufficiently high to validate the feasibility of the proposed sensor for reliable GSR sensing on the back.