A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor
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ORIGINAL ARTICLE
A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor Tugba Aydemir1 · Mehmet Şahin1 · Onder Aydemir2 Received: 13 July 2020 / Accepted: 5 October 2020 © Taiwanese Society of Biomedical Engineering 2020
Abstract Purpose Different kinds of sensors such as accelerometers and gyroscopes have been used for inferring, predicting, and monitoring human activities for various kinds of applications, including human–computer interaction, surveillance, smart home, health care, and security. In this study, we present a novel and robust method to recognize human activities, including resting, squat, and stepper exercises, solely from photoplethysmography (PPG), which is a non–invasive, simple, and lowcost opto-electronic technique that takes measures from the skin surface. Methods The features were extracted in raw PPG segments by Hilbert transform and then classified by the k-nearest neighbor, naïve Bayes, and decision tree algorithms. Results The proposed method was successfully applied to the data set recorded from seven subjects and achieved an average classification accuracy rate of 89.39% on the test data. The smaller standard deviation results proved that the proposed method was robust, and the detection of human activities can be effectively performed by Hilbert transform features and decision tree classifier. Conclusions This PPG-based approach could provide human-activity information in addition to monitoring heart rates and early screenings of various atherosclerotic pathologies, such as cardiovascular and hypertension diseases. Keywords Photoplethysmography · Activity monitoring · Wireless sensor · Classification
1 Introduction Photoplethysmography (PPG) is a non-invasive, simple, and low-cost opto-electronic technique that takes measures from the skin surface and can be used to detect blood volume changes in the microvascular bed of tissue [1]. Due to the great potential of widespread clinical application, the PPG technique has been extensively studied in heart rate monitoring [2–4], alcohol consumption [5], and early screening of * Tugba Aydemir [email protected] Mehmet Şahin [email protected] Onder Aydemir [email protected] 1
Department of Physics, Faculty of Science, Recep Tayyip Erdogan University, 53100 Rize, Turkey
Department of Electrical & Electronics Engineering, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
2
various atherosclerotic pathologies research, such as cardiovascular and hypertension diseases [6, 7]. In the literature, some studies analyzed PPG signals recorded during various physical activities [8–10]. For example, Zhang et al. proposed a wrist-type PPG signalbased algorithm to combine ensemble empirical mode decomposition with spectrum subtraction for monitoring heart rates during subjects’ physical activities, including walking or running on a treadmill. They performed their method on the data set recorded from 12 subjects, achieving an average absolut
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