A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health.

Abstract

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

Detection of Sleep and Wake States Based on the Combined Use of Actigraphy and Ballistocardiography

“…The parameters obtained and used from the Xsens MTw was the 3-axis acceleration values (x, y and z). Each of the acceleration axis signals needed to be normalized to obtain the vector magnitude using the following equation [41]:…”

“…Ballistocardiography is a non-invasive method to measure ballistic forces produced by the body as a result of the circulation of blood from the cardiac cycle [41]. BCG was first observed by Gordon, J.W.…”

“…The algorithm for estimating sleep/wake states with actigraphy is a weighted sums equation that is based on a moving average window, i.e. :A value is obtained from the actigraphic device performing a zero-crossing method to generate the raw signals for motion[41]. In addition, each A value can vary in real time length for example, a 10 second epoch would have all actigraphy movements recorded over 10 seconds in a single A value epoch.…”