Integrated Signal Encoding and Analysis System for Actigraphy-based Long-term Monitoring of Periodic Limb Movements in Sleep

Athavale, Yashodhan; Krishnan, Sridhar; Raissi, Afsaneh; Kirolos, Nardin; Murray, Brian J.; Boulos, Mark I.

doi:10.1109/embc.2018.8513108

“…The reader must note that in case of long-duration sleep actigraphy signals, the proposed encoding scheme’s results have already been published in [ 28 ] by Athavale et al, and hence we’ve shown the same results in this paper, to augment our experiments with daily activity [ 38 ] and vibroarthrography datasets [ 39 ].…”

Section: Actigraphysupporting

confidence: 56%

“…In the proposed study, we have conducted experiments on three datasets: Long-duration, tri-axial,bi-lateral ankle actigraphy signals [ 28 ] Short-duration, tri-axial, wrist-actigraphy signals [ 38 ], and Short-duration, single-axial, vibroarthrographic actigraphy signals [ 39 ] …”

Section: Methodsmentioning

confidence: 99%

“…A variety of actigraphs are currently available in the market (see Figure 3 ), and they are usually worn on wrist, waist or lower ankles for capturing human motor activity [ 27 ]. Typically, an actigraph is able to capture motion data with a sampling frequency in the range of 16–3200 Hz, coupled with an A-to-D quantization of 6–16 bits per sample, depending on the manufacturer [ 7 , 12 , 27 , 28 ] .…”

Section: Actigraphymentioning

confidence: 99%

“…In order to conduct a systematic investigation, we have conducted experiments on actigraphy data acquired from the following applications: Long-duration tri-axial actigraphy signals captured simultaneously with polysomnography in sleep studies [ 28 ]. Activities of Daily Life (ADL) dataset obtained from Dua et al [ 38 ].…”

Section: Actigraphymentioning

confidence: 99%

“…Long-duration tri-axial actigraphy signals captured simultaneously with polysomnography in sleep studies [ 28 ].…”

Section: Actigraphymentioning

confidence: 99%

See 3 more Smart Citations

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

Athavale

¹

,

Krishnan

²

2018

Sensors

Self Cite

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

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

Athavale¹,

Krishnan²

2022

Preprint

Self Cite

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

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

Athavale¹,

Krishnan²

2022

Preprint

Self Cite

0

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

Integrated Signal Encoding and Analysis System for Actigraphy-based Long-term Monitoring of Periodic Limb Movements in Sleep

Cited by 5 publications

References 17 publications

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

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

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

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

Contact Info

Product

Resources

About