Classification of Rest and Active Periods in Actigraphy Data Using PCA

Muns, Isaac W.; Lad, Yogesh Deepak; Guardiola, Ivan G.; Thimgan, Matthew S.

doi:10.1016/j.procs.2017.09.041

Cited by 10 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Home-based sleep staging: Actigraphy-based studies have been conducted for detecting sleep and wake states in healthy individuals as well as those experiencing sleep disorders. Some studies also included determining sleep stages and comparing the detection accuracy with PSG (polysomnography) standards [48][49][50][51][52][53][54].…”

Section: Prior Actigraphy Applicationsmentioning

confidence: 99%

Actigraphy Signal Analysis System for Sleep and Wake Studies

Athavale¹

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Prior Actigraphy Applicationsmentioning

confidence: 99%

Actigraphy Signal Analysis System for Sleep and Wake Studies

Athavale¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Actigraphs have been used by researchers in numerous clinical and consumer studies such as fitness monitoring, calorie consumption, sleep/wake activity analysis and for rehabilitation therapies in disabled individuals. To cite a few examples, actigraphy studies have been conducted in the following domains: Home-based sleep staging [ 13 , 14 , 15 ]. Analyzing movements in individuals suffering from Parkinson’s and Alzheimer’s disease [ 16 , 17 , 18 ].…”

Section: Actigraphymentioning

confidence: 99%

“…In case of tri-axial actigraphy data, our review of prior studies indicates that one must perform vector compounding of individual axial data before analysis, in order to simplify computations, and most importantly ensure that vibration information from all three directions is captured [ 14 , 31 , 32 ]. For example, given a tri-axial signal

, its vector magnitude would be computed as,

…”

Section: Actigraphymentioning

confidence: 99%

“… As per our literature review, human activity is usually captured in the 0.3 to 6 Hz frequency range, and high frequency noise is captured around the sampling frequency. In order to remove the noise, a simple low-pass filter (Butterworth) is employed to capture movement data [ 12 , 14 , 31 , 32 ]. Additional band-pass filters could be implemented in order to remove low frequency artifacts and noise.…”

Section: Actigraphymentioning

confidence: 99%

“…Some actigraphs tend to sample movement data too infrequently, thus leading to information loss in the output raw signal. Many prior studies have been conducted on short-duration actigraphy datasets and did not require extensive memory and computational resources for analysis [ 14 , 22 ]. Translating these studies into long-term activity monitoring solutions is not feasible unless the actigraphy data is subjected to significant compression and segmentation at the source.…”

Section: Actigraphymentioning

confidence: 99%

See 2 more Smart Citations

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

Athavale

Krishnan

2018

Sensors

View full text Add to dashboard Cite

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%.

show abstract

Data connectivity and application scenarios

Krishnan¹

2021

Biomedical Signal Analysis for Connected Healthcare

View full text Add to dashboard Cite

Classification of Rest and Active Periods in Actigraphy Data Using PCA

Cited by 10 publications

References 23 publications

Actigraphy Signal Analysis System for Sleep and Wake Studies

Actigraphy Signal Analysis System for Sleep and Wake Studies

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

Data connectivity and application scenarios

Contact Info

Product

Resources

About