Abstract
Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.
Highlights
Human activity recognition (HAR) using wearable sensors, i.e., devices directly positioned on the human body, is one of the most popular research areas, which focuses on automatically detecting what a particular human user is doing based on sensor data
After the recurrent neural network (RNN) has been designed, we investigate the porting and performance of the network on an embedded device, namely the STM32 microcontroller architecture from ST, using their “STM32Cube.AI” software solution [41]
An RNN was built for human activity recognition, using PPG and accelerometer data from a publicly available data set
Summary
HAR is at the core of a wide variety of applications, such as smart homes [1,2], health care [3,4,5,6,7], surveillance [8,9], skill assessment [10], and industrial settings [11], to cite just a few. To this end, photoplethysmography (PPG) is an optical technique commonly employed in wearables and other medical devices to measure the change in the volume of blood in the microvascular tissue. Being that PPG is a noninvasive method for HR estimation with respect to electrocardiography (ECG) and surface electromyography, requiring simpler body contact at peripheral sites on the body, such sensors are being more and more used in wearable devices, such as smart watches, as the preferred modality for HR monitoring in everyday activities
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