Actigraphy Signal Analysis System for Sleep and Wake Studies

Abstract

The Internet of Things (IoT) is a trending model in the wake of recent advancements in ubiquitous sensors and smart devices, and is rapidly being deployed in communications, infrastructure, transportation and healthcare services. The Internet of Medical Things (IoMT) is a subset of the IoT and provides a layered architecture for connecting individuals with mobile devices and wearables, such that their vital physiological data can be captured and analyzed non-invasively using smart sensors embedded within these devices. Currently available wearables have embedded sensing modules for measuring movement, direction, light and pressure. Actigraphs are one such type of wearables which exclusively employ the use of accelerometers for capturing human movement-based vibration data. The main intention of this research work is the analysis of unstructured, non-stationary actigraphy signals. This study aims to address three key objectives: (i) enabling compression and denoising of actigraphy data during acquisition; (ii) extracting regions of interest from the actigraphy data, and; (iii) deriving actigraphy specific features for improving the activity classification accuracy. These have been achieved through three key contributions, namely: (A) a signal encoding framework for data compression and denoising, (B) two novel adaptive segmentation schemes which help in extracting specific movement information from actigraphy data, and (C) two key actigraphy specific features, which quantify limb movements, and hence provide a better classification accuracy using machine learning algorithms. The outcome of this research work is a device-independent actigraphy analysis application for estimating the severity of neuromuscular diseases, identifying types of daily activity, and classify joint degeneration severity, which has been tested on five different actigraphy datasets from wake and sleep states. Compared to traditional signal processing methods, the proposed algorithms ensured a 20-90% increase in SNR (signal-to-noise ratio), 50-80% reduction in bit rate, 50-90% data compression, and an increase in activity recognition accuracy by over 5-20%. Results from this systematic investigation indicate that data analysis conducted at the acquisition source, optimizes signal denoising, memory and power usage, and activity recognition, thereby promoting an edge computing approach to physiological signal analysis using wearables in a low resource environment.