Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.

With the development of the portable electrocardiogram (ECG) sensor, R peaks can be monitored during various physical activities in mobile environment. However, such ECG signals contain real-world noise, complicating the accurate detection of R peak. In this paper, we propose a novel approach for R peak detection in ECG signals with real-world noise by using signal envelop filtering (SEF) and Shannon energy envelope with the Savitzky-Golay filter (SEE-SG). SEF was designed to diminish abrupt peaks due to movement by adjusting its amplitude based on an automatically determined threshold. SEE-SG was used to enhance the QRS-complex. We first designed the experimental protocol for physical activity (sit, walk, and ascend), namely, normal ECG in a physical activity database, and validated our method by using the ECG data of 23 subjects. Moreover, to avoid overtuning on our database, we validated the proposed method using two public databases: the MIT-BIH QT database (QTDB) and Noise Stress Test Database (NSTDB). The experimental results show promising performance on QTDB (SE and P+: 99.96% and ER: 0.08%), NSTDB (SE: 94.07%, P+: 93.45, and ER: 12.57%), and NPADB (SE: 99.95%, P+: 99.78%, and ER: 0.27%). We also assessed our method by comparing the absolute differences of R-R intervals and heart rates between actual and detected R peaks. In conclusion, as the proposed method achieved noise robustness and reliable performance on R peak detection during physical activities, it can be used to monitor a user's ECG and cardiac health information accurately in daily life.

Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life.

Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method c...