ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors

Abstract

Abstract Sensor-based activity recognition seeks to provide higher-level knowledge about human activities from multiple sensors such as accelerometer and gyroscope. Thanks to growing ubiquity of sensor-rich smartphones and wearable devices, activity recognition research has made tremendous progress in recent years. Many sensors, such as motion sensors and cameras, have been previously used for this task. In this paper, we present the first work to recognize human activities solely from photoplethysmography (PPG) data. PPG sensors use a light-based technology to sense the rate of blood flow as controlled by the heart's pumping action. Although they are not originally intended to sense body motions and gestures, in this paper, we propose a novel method to extract meaningful features from the PPG to predict human activities. We exploit PPG signals' susceptibility to corruption by noise introduced by motion artifact and extract, as opposed to discard, the motion artifact signals combined with cardiac and respiratory signals to predict the types of activities performed by users. We combine convolutional and recurrent neural networks to predict different types of daily activities (e.g., walking, running, jumping) from the raw PPG signal. Results using data generated by wrist-worn smartwatches of 12 participants demonstrate the feasibility of our approach at predicting five types of activities (standing, walking, jogging, jumping, and sitting), and highlight new insights about how to use PPG sensors for human activity recognition. Results also highlight the importance of extracting motion artifact signals to understand human behaviors.