A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer
Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hiera...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2010-09, Vol.14 (5), p.1166-1172 |
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| creator | Khan, A M Young-Koo Lee Lee, S Y Tae-Seong Kim |
| description | Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest. |
| doi_str_mv | 10.1109/TITB.2010.2051955 |
| format | Article |
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In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. 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methods</topic><topic>Motor Activity</topic><topic>Neural Networks (Computer)</topic><topic>Normal Distribution</topic><topic>Regression Analysis</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Vectors</topic><topic>Wearable sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khan, A M</creatorcontrib><creatorcontrib>Young-Koo Lee</creatorcontrib><creatorcontrib>Lee, S Y</creatorcontrib><creatorcontrib>Tae-Seong Kim</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Physical Education Index</collection><jtitle>IEEE journal of biomedical and health informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Khan, A M</au><au>Young-Koo Lee</au><au>Lee, S Y</au><au>Tae-Seong Kim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer</atitle><jtitle>IEEE journal of biomedical and health informatics</jtitle><stitle>TITB</stitle><addtitle>IEEE Trans Inf Technol Biomed</addtitle><date>2010-09</date><risdate>2010</risdate><volume>14</volume><issue>5</issue><spage>1166</spage><epage>1172</epage><pages>1166-1172</pages><issn>1089-7771</issn><issn>2168-2194</issn><eissn>1558-0032</eissn><eissn>2168-2208</eissn><coden>ITIBFX</coden><abstract>Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>20529753</pmid><doi>10.1109/TITB.2010.2051955</doi><tpages>7</tpages></addata></record> |
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| subjects | Acceleration Accelerometer Accelerometers Adult Artificial neural networks artificial-neural nets (ANNs) autoregressive (AR) modeling Computer vision Discriminant Analysis Female human-activity recognition Humans IEEE activities Legged locomotion Linear discriminant analysis Male Monitoring, Ambulatory - methods Motor Activity Neural Networks (Computer) Normal Distribution Regression Analysis Signal Processing, Computer-Assisted Vectors Wearable sensors |
| title | A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer |
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