Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety
This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing...
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| Veröffentlicht in: | IEEE sensors journal 2016-05, Vol.16 (10), p.3689-3696 |
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| creator | Yali Zheng Wong, Tracy C. H. Leung, Billy H. K. Poon, Carmen C. Y. |
| description | This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing with another person in Task 2. A Competitive State Anxiety Inventory-2 questionnaire was conducted before each task to evaluate the anxiety level of each participant. Various features were extracted from EEG and PPG. The results of this paper showed that the mean value and average power of alpha band wavelet coefficients and that of beta band are highly correlated with the anxiety level (r = -0.49 and -0.58, p |
| doi_str_mv | 10.1109/JSEN.2016.2539383 |
| format | Article |
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H. ; Leung, Billy H. K. ; Poon, Carmen C. Y.</creator><creatorcontrib>Yali Zheng ; Wong, Tracy C. H. ; Leung, Billy H. K. ; Poon, Carmen C. Y.</creatorcontrib><description>This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing with another person in Task 2. A Competitive State Anxiety Inventory-2 questionnaire was conducted before each task to evaluate the anxiety level of each participant. Various features were extracted from EEG and PPG. The results of this paper showed that the mean value and average power of alpha band wavelet coefficients and that of beta band are highly correlated with the anxiety level (r = -0.49 and -0.58, p <; 0.01 for alpha band, and r = -0.51 and -0.58, p <; 0.01 for beta band, respectively). Features extracted from partial autocorrelation of EEG showed moderate correlation with the anxiety level. Mean pulse rate also acts as a potential anxiety marker for individualized anxiety measurement. Using both EEG and PPG features, the classification accuracy of three-level anxiety by principle component analysis and k-nearest neighbors can achieve 62.5% across subjects. To conclude, wearable sensors have the potential to be used for assessing anxiety level objectively and unobtrusively to facilitate on-site sports performance enhancement and mental-stress-related studies.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2016.2539383</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Anxiety ; Biomedical monitoring ; Body sensor network ; Electroencephalogram ; Electroencephalography ; Feature extraction ; Frequency measurement ; Photoplethysmogram ; Quantified self ; Sensors ; Sports performance ; Stress ; Training ; Unobtrusive sensing ; Wearable sensors</subject><ispartof>IEEE sensors journal, 2016-05, Vol.16 (10), p.3689-3696</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-1a7f6e1e9522e96e1ca40281cdbc475e51108387851dff9873a92cf8c27bec6f3</citedby><cites>FETCH-LOGICAL-c293t-1a7f6e1e9522e96e1ca40281cdbc475e51108387851dff9873a92cf8c27bec6f3</cites><orcidid>0000-0001-7717-4752</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7428822$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7428822$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yali Zheng</creatorcontrib><creatorcontrib>Wong, Tracy C. H.</creatorcontrib><creatorcontrib>Leung, Billy H. K.</creatorcontrib><creatorcontrib>Poon, Carmen C. Y.</creatorcontrib><title>Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing with another person in Task 2. A Competitive State Anxiety Inventory-2 questionnaire was conducted before each task to evaluate the anxiety level of each participant. Various features were extracted from EEG and PPG. The results of this paper showed that the mean value and average power of alpha band wavelet coefficients and that of beta band are highly correlated with the anxiety level (r = -0.49 and -0.58, p <; 0.01 for alpha band, and r = -0.51 and -0.58, p <; 0.01 for beta band, respectively). Features extracted from partial autocorrelation of EEG showed moderate correlation with the anxiety level. Mean pulse rate also acts as a potential anxiety marker for individualized anxiety measurement. Using both EEG and PPG features, the classification accuracy of three-level anxiety by principle component analysis and k-nearest neighbors can achieve 62.5% across subjects. To conclude, wearable sensors have the potential to be used for assessing anxiety level objectively and unobtrusively to facilitate on-site sports performance enhancement and mental-stress-related studies.</description><subject>Anxiety</subject><subject>Biomedical monitoring</subject><subject>Body sensor network</subject><subject>Electroencephalogram</subject><subject>Electroencephalography</subject><subject>Feature extraction</subject><subject>Frequency measurement</subject><subject>Photoplethysmogram</subject><subject>Quantified self</subject><subject>Sensors</subject><subject>Sports performance</subject><subject>Stress</subject><subject>Training</subject><subject>Unobtrusive sensing</subject><subject>Wearable sensors</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKs_QLwseN6aSTZNcpJS6hdVkVr0FrLZiWxpd2uyK_bfu0uLp3kPzzvDPIRcAh0BUH3ztJi9jBiF8YgJrrniR2QAQqgUZKaO-8xpmnH5eUrOYlxRCloKOSC3y6rOm9DG8gcTWxXJc7tuyk1d2HXygTbYfI3JAqtYVl9JUydvra2a0u-SSfVbYrM7JyferiNeHOaQLO9m79OHdP56_zidzFPHNG9SsNKPEVALxlB3ydmMMgWuyF0mBYruCcWVVAIK77WS3GrmvHJM5ujGng_J9X7vNtTfLcbGrOo2VN1JA1JJACUodBTsKRfqGAN6sw3lxoadAWp6T6b3ZHpP5uCp61ztOyUi_vMyY0oxxv8AiotjqQ</recordid><startdate>20160515</startdate><enddate>20160515</enddate><creator>Yali Zheng</creator><creator>Wong, Tracy C. H.</creator><creator>Leung, Billy H. K.</creator><creator>Poon, Carmen C. Y.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-7717-4752</orcidid></search><sort><creationdate>20160515</creationdate><title>Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety</title><author>Yali Zheng ; Wong, Tracy C. H. ; Leung, Billy H. K. ; Poon, Carmen C. Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-1a7f6e1e9522e96e1ca40281cdbc475e51108387851dff9873a92cf8c27bec6f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Anxiety</topic><topic>Biomedical monitoring</topic><topic>Body sensor network</topic><topic>Electroencephalogram</topic><topic>Electroencephalography</topic><topic>Feature extraction</topic><topic>Frequency measurement</topic><topic>Photoplethysmogram</topic><topic>Quantified self</topic><topic>Sensors</topic><topic>Sports performance</topic><topic>Stress</topic><topic>Training</topic><topic>Unobtrusive sensing</topic><topic>Wearable sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yali Zheng</creatorcontrib><creatorcontrib>Wong, Tracy C. H.</creatorcontrib><creatorcontrib>Leung, Billy H. K.</creatorcontrib><creatorcontrib>Poon, Carmen C. Y.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yali Zheng</au><au>Wong, Tracy C. H.</au><au>Leung, Billy H. K.</au><au>Poon, Carmen C. Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2016-05-15</date><risdate>2016</risdate><volume>16</volume><issue>10</issue><spage>3689</spage><epage>3696</epage><pages>3689-3696</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>This paper aims to develop an objective index for anxiety based on features derived from electroencephalogram (EEG) and photoplethysmogram (PPG) collected from wearable headset and glasses. The 20 subjects were asked to ride at his most comfortable speed in Task 1 and ride while imagining competing with another person in Task 2. A Competitive State Anxiety Inventory-2 questionnaire was conducted before each task to evaluate the anxiety level of each participant. Various features were extracted from EEG and PPG. The results of this paper showed that the mean value and average power of alpha band wavelet coefficients and that of beta band are highly correlated with the anxiety level (r = -0.49 and -0.58, p <; 0.01 for alpha band, and r = -0.51 and -0.58, p <; 0.01 for beta band, respectively). Features extracted from partial autocorrelation of EEG showed moderate correlation with the anxiety level. Mean pulse rate also acts as a potential anxiety marker for individualized anxiety measurement. Using both EEG and PPG features, the classification accuracy of three-level anxiety by principle component analysis and k-nearest neighbors can achieve 62.5% across subjects. To conclude, wearable sensors have the potential to be used for assessing anxiety level objectively and unobtrusively to facilitate on-site sports performance enhancement and mental-stress-related studies.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2016.2539383</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-7717-4752</orcidid></addata></record> |
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| subjects | Anxiety Biomedical monitoring Body sensor network Electroencephalogram Electroencephalography Feature extraction Frequency measurement Photoplethysmogram Quantified self Sensors Sports performance Stress Training Unobtrusive sensing Wearable sensors |
| title | Unobtrusive and Multimodal Wearable Sensing to Quantify Anxiety |
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