Sleep behavior assessment via smartwatch and stigmergic receptive fields
Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Othe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Alfeo, Antonio L. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London Ltd. 2017 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Personal and ubiquitous computing - Springer London, 1997, 22(2017), 2 vom: 05. Juli, Seite 227-243 |
|---|---|
| Übergeordnetes Werk: |
volume:22 ; year:2017 ; number:2 ; day:05 ; month:07 ; pages:227-243 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00779-017-1038-9 |
|---|
| Katalog-ID: |
OLC2108445501 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2108445501 | ||
| 003 | DE-627 | ||
| 005 | 20230502152124.0 | ||
| 007 | tu | ||
| 008 | 230502s2017 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00779-017-1038-9 |2 doi | |
| 035 | |a (DE-627)OLC2108445501 | ||
| 035 | |a (DE-He213)s00779-017-1038-9-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Alfeo, Antonio L. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Sleep behavior assessment via smartwatch and stigmergic receptive fields |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag London Ltd. 2017 | ||
| 520 | |a Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. | ||
| 650 | 4 | |a Sleep monitoring | |
| 650 | 4 | |a Smartwatch | |
| 650 | 4 | |a Stigmergy | |
| 650 | 4 | |a Neural receptive field | |
| 700 | 1 | |a Barsocchi, Paolo |4 aut | |
| 700 | 1 | |a Cimino, Mario G. C. A. |4 aut | |
| 700 | 1 | |a La Rosa, Davide |4 aut | |
| 700 | 1 | |a Palumbo, Filippo |4 aut | |
| 700 | 1 | |a Vaglini, Gigliola |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Personal and ubiquitous computing |d Springer London, 1997 |g 22(2017), 2 vom: 05. Juli, Seite 227-243 |w (DE-627)33216859X |w (DE-600)2053206-4 |w (DE-576)115303367 |x 1617-4909 |7 nnns |
| 773 | 1 | 8 | |g volume:22 |g year:2017 |g number:2 |g day:05 |g month:07 |g pages:227-243 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00779-017-1038-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 22 |j 2017 |e 2 |b 05 |c 07 |h 227-243 | ||
| author_variant |
a l a al ala p b pb m g c a c mgca mgcac r d l rd rdl f p fp g v gv |
|---|---|
| matchkey_str |
article:16174909:2017----::lebhvoassmnvamrwthnsimri |
| hierarchy_sort_str |
2017 |
| publishDate |
2017 |
| allfields |
10.1007/s00779-017-1038-9 doi (DE-627)OLC2108445501 (DE-He213)s00779-017-1038-9-p DE-627 ger DE-627 rakwb eng 004 VZ Alfeo, Antonio L. verfasserin aut Sleep behavior assessment via smartwatch and stigmergic receptive fields 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. Sleep monitoring Smartwatch Stigmergy Neural receptive field Barsocchi, Paolo aut Cimino, Mario G. C. A. aut La Rosa, Davide aut Palumbo, Filippo aut Vaglini, Gigliola aut Enthalten in Personal and ubiquitous computing Springer London, 1997 22(2017), 2 vom: 05. Juli, Seite 227-243 (DE-627)33216859X (DE-600)2053206-4 (DE-576)115303367 1617-4909 nnns volume:22 year:2017 number:2 day:05 month:07 pages:227-243 https://doi.org/10.1007/s00779-017-1038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2017 2 05 07 227-243 |
| spelling |
10.1007/s00779-017-1038-9 doi (DE-627)OLC2108445501 (DE-He213)s00779-017-1038-9-p DE-627 ger DE-627 rakwb eng 004 VZ Alfeo, Antonio L. verfasserin aut Sleep behavior assessment via smartwatch and stigmergic receptive fields 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. Sleep monitoring Smartwatch Stigmergy Neural receptive field Barsocchi, Paolo aut Cimino, Mario G. C. A. aut La Rosa, Davide aut Palumbo, Filippo aut Vaglini, Gigliola aut Enthalten in Personal and ubiquitous computing Springer London, 1997 22(2017), 2 vom: 05. Juli, Seite 227-243 (DE-627)33216859X (DE-600)2053206-4 (DE-576)115303367 1617-4909 nnns volume:22 year:2017 number:2 day:05 month:07 pages:227-243 https://doi.org/10.1007/s00779-017-1038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2017 2 05 07 227-243 |
| allfields_unstemmed |
10.1007/s00779-017-1038-9 doi (DE-627)OLC2108445501 (DE-He213)s00779-017-1038-9-p DE-627 ger DE-627 rakwb eng 004 VZ Alfeo, Antonio L. verfasserin aut Sleep behavior assessment via smartwatch and stigmergic receptive fields 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. Sleep monitoring Smartwatch Stigmergy Neural receptive field Barsocchi, Paolo aut Cimino, Mario G. C. A. aut La Rosa, Davide aut Palumbo, Filippo aut Vaglini, Gigliola aut Enthalten in Personal and ubiquitous computing Springer London, 1997 22(2017), 2 vom: 05. Juli, Seite 227-243 (DE-627)33216859X (DE-600)2053206-4 (DE-576)115303367 1617-4909 nnns volume:22 year:2017 number:2 day:05 month:07 pages:227-243 https://doi.org/10.1007/s00779-017-1038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2017 2 05 07 227-243 |
| allfieldsGer |
10.1007/s00779-017-1038-9 doi (DE-627)OLC2108445501 (DE-He213)s00779-017-1038-9-p DE-627 ger DE-627 rakwb eng 004 VZ Alfeo, Antonio L. verfasserin aut Sleep behavior assessment via smartwatch and stigmergic receptive fields 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. Sleep monitoring Smartwatch Stigmergy Neural receptive field Barsocchi, Paolo aut Cimino, Mario G. C. A. aut La Rosa, Davide aut Palumbo, Filippo aut Vaglini, Gigliola aut Enthalten in Personal and ubiquitous computing Springer London, 1997 22(2017), 2 vom: 05. Juli, Seite 227-243 (DE-627)33216859X (DE-600)2053206-4 (DE-576)115303367 1617-4909 nnns volume:22 year:2017 number:2 day:05 month:07 pages:227-243 https://doi.org/10.1007/s00779-017-1038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2017 2 05 07 227-243 |
| allfieldsSound |
10.1007/s00779-017-1038-9 doi (DE-627)OLC2108445501 (DE-He213)s00779-017-1038-9-p DE-627 ger DE-627 rakwb eng 004 VZ Alfeo, Antonio L. verfasserin aut Sleep behavior assessment via smartwatch and stigmergic receptive fields 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. Sleep monitoring Smartwatch Stigmergy Neural receptive field Barsocchi, Paolo aut Cimino, Mario G. C. A. aut La Rosa, Davide aut Palumbo, Filippo aut Vaglini, Gigliola aut Enthalten in Personal and ubiquitous computing Springer London, 1997 22(2017), 2 vom: 05. Juli, Seite 227-243 (DE-627)33216859X (DE-600)2053206-4 (DE-576)115303367 1617-4909 nnns volume:22 year:2017 number:2 day:05 month:07 pages:227-243 https://doi.org/10.1007/s00779-017-1038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2017 2 05 07 227-243 |
| language |
English |
| source |
Enthalten in Personal and ubiquitous computing 22(2017), 2 vom: 05. Juli, Seite 227-243 volume:22 year:2017 number:2 day:05 month:07 pages:227-243 |
| sourceStr |
Enthalten in Personal and ubiquitous computing 22(2017), 2 vom: 05. Juli, Seite 227-243 volume:22 year:2017 number:2 day:05 month:07 pages:227-243 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Sleep monitoring Smartwatch Stigmergy Neural receptive field |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Personal and ubiquitous computing |
| authorswithroles_txt_mv |
Alfeo, Antonio L. @@aut@@ Barsocchi, Paolo @@aut@@ Cimino, Mario G. C. A. @@aut@@ La Rosa, Davide @@aut@@ Palumbo, Filippo @@aut@@ Vaglini, Gigliola @@aut@@ |
| publishDateDaySort_date |
2017-07-05T00:00:00Z |
| hierarchy_top_id |
33216859X |
| dewey-sort |
14 |
| id |
OLC2108445501 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2108445501</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502152124.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230502s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00779-017-1038-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2108445501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00779-017-1038-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Alfeo, Antonio L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sleep behavior assessment via smartwatch and stigmergic receptive fields</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd. 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sleep monitoring</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smartwatch</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stigmergy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural receptive field</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barsocchi, Paolo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cimino, Mario G. C. A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">La Rosa, Davide</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Palumbo, Filippo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vaglini, Gigliola</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Personal and ubiquitous computing</subfield><subfield code="d">Springer London, 1997</subfield><subfield code="g">22(2017), 2 vom: 05. Juli, Seite 227-243</subfield><subfield code="w">(DE-627)33216859X</subfield><subfield code="w">(DE-600)2053206-4</subfield><subfield code="w">(DE-576)115303367</subfield><subfield code="x">1617-4909</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:2</subfield><subfield code="g">day:05</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:227-243</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00779-017-1038-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2017</subfield><subfield code="e">2</subfield><subfield code="b">05</subfield><subfield code="c">07</subfield><subfield code="h">227-243</subfield></datafield></record></collection>
|
| author |
Alfeo, Antonio L. |
| spellingShingle |
Alfeo, Antonio L. ddc 004 misc Sleep monitoring misc Smartwatch misc Stigmergy misc Neural receptive field Sleep behavior assessment via smartwatch and stigmergic receptive fields |
| authorStr |
Alfeo, Antonio L. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)33216859X |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1617-4909 |
| topic_title |
004 VZ Sleep behavior assessment via smartwatch and stigmergic receptive fields Sleep monitoring Smartwatch Stigmergy Neural receptive field |
| topic |
ddc 004 misc Sleep monitoring misc Smartwatch misc Stigmergy misc Neural receptive field |
| topic_unstemmed |
ddc 004 misc Sleep monitoring misc Smartwatch misc Stigmergy misc Neural receptive field |
| topic_browse |
ddc 004 misc Sleep monitoring misc Smartwatch misc Stigmergy misc Neural receptive field |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Personal and ubiquitous computing |
| hierarchy_parent_id |
33216859X |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Personal and ubiquitous computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)33216859X (DE-600)2053206-4 (DE-576)115303367 |
| title |
Sleep behavior assessment via smartwatch and stigmergic receptive fields |
| ctrlnum |
(DE-627)OLC2108445501 (DE-He213)s00779-017-1038-9-p |
| title_full |
Sleep behavior assessment via smartwatch and stigmergic receptive fields |
| author_sort |
Alfeo, Antonio L. |
| journal |
Personal and ubiquitous computing |
| journalStr |
Personal and ubiquitous computing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
txt |
| container_start_page |
227 |
| author_browse |
Alfeo, Antonio L. Barsocchi, Paolo Cimino, Mario G. C. A. La Rosa, Davide Palumbo, Filippo Vaglini, Gigliola |
| container_volume |
22 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Alfeo, Antonio L. |
| doi_str_mv |
10.1007/s00779-017-1038-9 |
| dewey-full |
004 |
| title_sort |
sleep behavior assessment via smartwatch and stigmergic receptive fields |
| title_auth |
Sleep behavior assessment via smartwatch and stigmergic receptive fields |
| abstract |
Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. © Springer-Verlag London Ltd. 2017 |
| abstractGer |
Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. © Springer-Verlag London Ltd. 2017 |
| abstract_unstemmed |
Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series. © Springer-Verlag London Ltd. 2017 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
2 |
| title_short |
Sleep behavior assessment via smartwatch and stigmergic receptive fields |
| url |
https://doi.org/10.1007/s00779-017-1038-9 |
| remote_bool |
false |
| author2 |
Barsocchi, Paolo Cimino, Mario G. C. A. La Rosa, Davide Palumbo, Filippo Vaglini, Gigliola |
| author2Str |
Barsocchi, Paolo Cimino, Mario G. C. A. La Rosa, Davide Palumbo, Filippo Vaglini, Gigliola |
| ppnlink |
33216859X |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00779-017-1038-9 |
| up_date |
2024-07-03T19:08:21.505Z |
| _version_ |
1803586058591404032 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2108445501</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502152124.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230502s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00779-017-1038-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2108445501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00779-017-1038-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Alfeo, Antonio L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sleep behavior assessment via smartwatch and stigmergic receptive fields</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd. 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sleep monitoring</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smartwatch</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stigmergy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural receptive field</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barsocchi, Paolo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cimino, Mario G. C. A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">La Rosa, Davide</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Palumbo, Filippo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vaglini, Gigliola</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Personal and ubiquitous computing</subfield><subfield code="d">Springer London, 1997</subfield><subfield code="g">22(2017), 2 vom: 05. Juli, Seite 227-243</subfield><subfield code="w">(DE-627)33216859X</subfield><subfield code="w">(DE-600)2053206-4</subfield><subfield code="w">(DE-576)115303367</subfield><subfield code="x">1617-4909</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:2</subfield><subfield code="g">day:05</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:227-243</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00779-017-1038-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2017</subfield><subfield code="e">2</subfield><subfield code="b">05</subfield><subfield code="c">07</subfield><subfield code="h">227-243</subfield></datafield></record></collection>
|
| score |
7.399868 |