This study proposed a wearable device capable of recognizing six human daily activities (walking, walking upstairs, walking downstairs, sitting, standing, and lying) through a deep learning algorithm. Existing wearable devices are mainly watches or wristbands, and almost none are to be worn on the waist. Wearable devices in the forms of watches and wristbands are unfriendly to patients who are critically ill, such as patients undergoing dialysis. Patients undergoing dialysis have artificial blood vessels on their arm, and they cannot perform intense exercise. For this type of users, general hand wearable devices cannot correctly identify wearers' activities. Therefore, we proposed a waist wearable device and these types of daily life activities to assess their exercise. The hardware of the wearable device consisted of an inertial sensor, which included a microcontroller, a three-axis accelerometer, and a three-axis gyroscope. The activity recognition algorithm of the software used motion signals acquisition, signal normalization, and a feature learning method. The feature learning method was based on a 1D convolutional neural network that automatically performed feature extraction and classification from raw data. One part of the experimental data was from the dataset of the University of California (UCI), and the other part was recorded by this study. To capture the data recorded, the wearable inertial sensing device was attached to the waists of 21 experimental participants who performed six common movements in a laboratorial environment, and the subsequent records were collected to verify the validity of the proposed deep learning algorithm in relation to the inertial sensor of the wearable device. For the six common activities in the UCI dataset and the data recorded, the recognition rates in the training sample reached 98.93% and 97.19%, respectively, and the recognition rates in the testing sample were 95.99% and 93.77%, respectively.
This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board computer (SBC) and six-axis sensors. The deep-learning algorithm employs three parallel convolutional neural networks for local feature extraction and for subsequent concatenation to establish feature fusion models of varying kernel size. By using kernels of different sizes, relevant local features of varying lengths were identified, thereby increasing the accuracy of human activity recognition. Regarding experimental data, the database of University of California, Irvine (UCI) and self-recorded data were used separately. The self-recorded data were obtained by having 21 participants wear the device on their waist and perform six common activities in the laboratory. These data were used to verify the proposed deep-learning algorithm on the performance of the wearable device. The accuracy of these six activities in the UCI dataset and in the self-recorded data were 97.49% and 96.27%, respectively. The accuracies in tenfold cross-validation were 99.56% and 97.46%, respectively. The experimental results have successfully verified the proposed convolutional neural network (CNN) architecture, which can be used in rehabilitation assessment for people unable to exercise vigorously.
This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-todigital converter [MCP3008]), which were connected using a serial peripheral interface. The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP). To increase the robustness of the DL model, this study input data of 100 Asian participants into the training database, including those with and without cardiovascular disease, each with a proportion of approximately 50%. The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17 ± 0.46 mmHg. The mean absolute error and standard deviation of DBP was 0.27 ± 0.52 mmHg. The mean absolute error and standard deviation of MAP was 0.16 ± 0.40 mmHg.
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