Human activity recognition using machine learning techniques in a low-resource embedded system
Resumen:
Human activity recognition aims to infer a person’s actions from a set of observations captured by several sensors. Data acquisition, processing and inference on edge devices add a complexity factor to the task, as they involve a trade-off between hardware efficiency and performance. We present a prototype of a wearable device that identifies a person’s activity: walking, running or staying still. The system consists of a Texas Instruments MSP-EXP430G2ET launchpad, connected to a BOOSTXL-SENSORS boosterpack with a BMI160 accelerometer. The designed prototype can take acceleration measurements, process them and either transmit them to a computer or classify the activity in the microcontroller. Additionally, our system has LEDs to display coloured signals according to the inferred activity in real-time. The classification algorithm is based on the calculation of statistical features (mean, standard deviation, maximum and minimum) for each accelerometer axis, the application of a dimensionality reduction algorithm (LDA, Linear Discriminant Analysis) and an SVM (Support Vector Machines) classification model.
| 2021 | |
| Este trabajo fue parcialmente financiado por la Comisión Académica de Posgrado (CAP, UdelaR), Espacio Interdisciplinario (EI, UdelaR) y la Comisión Sectorial de Investigación Científica (CSIC, UdelaR) “Proyecto I + D : Sistema electrónico para la caracterización del comportamiento de ovinos". | |
|
Human Activity Recognition Acceleration Sensor Linear Discriminant Analysis Support Vector Machines |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/30548 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522899136872448 |
|---|---|
| author | Stolovas, Ilana |
| author2 | Suárez, Santiago Pereyra, Diego De Izaguirre, Francisco Cabrera, Varinia |
| author2_role | author author author author |
| author_facet | Stolovas, Ilana Suárez, Santiago Pereyra, Diego De Izaguirre, Francisco Cabrera, Varinia |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Stolovas Ilana, Universidad de la República (Uruguay). Facultad de Ingeniería. Suárez Santiago, Universidad de la República (Uruguay). Facultad de Ingeniería. Pereyra Diego, Universidad de la República (Uruguay). Facultad de Ingeniería. De Izaguirre Francisco, Universidad de la República (Uruguay). Facultad de Ingeniería. Cabrera Varinia, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Stolovas, Ilana Suárez, Santiago Pereyra, Diego De Izaguirre, Francisco Cabrera, Varinia |
| dc.date.accessioned.none.fl_str_mv | 2021-12-23T13:49:11Z |
| dc.date.available.none.fl_str_mv | 2021-12-23T13:49:11Z |
| dc.date.issued.none.fl_str_mv | 2021 |
| dc.description.abstract.none.fl_txt_mv | Human activity recognition aims to infer a person’s actions from a set of observations captured by several sensors. Data acquisition, processing and inference on edge devices add a complexity factor to the task, as they involve a trade-off between hardware efficiency and performance. We present a prototype of a wearable device that identifies a person’s activity: walking, running or staying still. The system consists of a Texas Instruments MSP-EXP430G2ET launchpad, connected to a BOOSTXL-SENSORS boosterpack with a BMI160 accelerometer. The designed prototype can take acceleration measurements, process them and either transmit them to a computer or classify the activity in the microcontroller. Additionally, our system has LEDs to display coloured signals according to the inferred activity in real-time. The classification algorithm is based on the calculation of statistical features (mean, standard deviation, maximum and minimum) for each accelerometer axis, the application of a dimensionality reduction algorithm (LDA, Linear Discriminant Analysis) and an SVM (Support Vector Machines) classification model. |
| dc.description.sponsorship.none.fl_txt_mv | Este trabajo fue parcialmente financiado por la Comisión Académica de Posgrado (CAP, UdelaR), Espacio Interdisciplinario (EI, UdelaR) y la Comisión Sectorial de Investigación Científica (CSIC, UdelaR) “Proyecto I + D : Sistema electrónico para la caracterización del comportamiento de ovinos". |
| dc.format.extent.es.fl_str_mv | 5 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Stolovas, I., Suárez, S., Pereyra, D. y otros. Human activity recognition using machine learning techniques in a low-resource embedded system [Preprint]. Publicado en : IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, 5 p. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/30548 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Udelar.FI. |
| dc.relation.ispartof.es.fl_str_mv | IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, pp. 1-5. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.en.fl_str_mv | Human Activity Recognition Acceleration Sensor Linear Discriminant Analysis Support Vector Machines |
| dc.title.none.fl_str_mv | Human activity recognition using machine learning techniques in a low-resource embedded system |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | Human activity recognition aims to infer a person’s actions from a set of observations captured by several sensors. Data acquisition, processing and inference on edge devices add a complexity factor to the task, as they involve a trade-off between hardware efficiency and performance. We present a prototype of a wearable device that identifies a person’s activity: walking, running or staying still. The system consists of a Texas Instruments MSP-EXP430G2ET launchpad, connected to a BOOSTXL-SENSORS boosterpack with a BMI160 accelerometer. The designed prototype can take acceleration measurements, process them and either transmit them to a computer or classify the activity in the microcontroller. Additionally, our system has LEDs to display coloured signals according to the inferred activity in real-time. The classification algorithm is based on the calculation of statistical features (mean, standard deviation, maximum and minimum) for each accelerometer axis, the application of a dimensionality reduction algorithm (LDA, Linear Discriminant Analysis) and an SVM (Support Vector Machines) classification model. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_8fe0328638d1d15d84e561b0bf1ca90d |
| identifier_str_mv | Stolovas, I., Suárez, S., Pereyra, D. y otros. Human activity recognition using machine learning techniques in a low-resource embedded system [Preprint]. Publicado en : IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, 5 p. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/30548 |
| publishDate | 2021 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Stolovas Ilana, Universidad de la República (Uruguay). Facultad de Ingeniería.Suárez Santiago, Universidad de la República (Uruguay). Facultad de Ingeniería.Pereyra Diego, Universidad de la República (Uruguay). Facultad de Ingeniería.De Izaguirre Francisco, Universidad de la República (Uruguay). Facultad de Ingeniería.Cabrera Varinia, Universidad de la República (Uruguay). Facultad de Ingeniería.2021-12-23T13:49:11Z2021-12-23T13:49:11Z2021Stolovas, I., Suárez, S., Pereyra, D. y otros. Human activity recognition using machine learning techniques in a low-resource embedded system [Preprint]. Publicado en : IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, 5 p.https://hdl.handle.net/20.500.12008/30548Human activity recognition aims to infer a person’s actions from a set of observations captured by several sensors. Data acquisition, processing and inference on edge devices add a complexity factor to the task, as they involve a trade-off between hardware efficiency and performance. We present a prototype of a wearable device that identifies a person’s activity: walking, running or staying still. The system consists of a Texas Instruments MSP-EXP430G2ET launchpad, connected to a BOOSTXL-SENSORS boosterpack with a BMI160 accelerometer. The designed prototype can take acceleration measurements, process them and either transmit them to a computer or classify the activity in the microcontroller. Additionally, our system has LEDs to display coloured signals according to the inferred activity in real-time. The classification algorithm is based on the calculation of statistical features (mean, standard deviation, maximum and minimum) for each accelerometer axis, the application of a dimensionality reduction algorithm (LDA, Linear Discriminant Analysis) and an SVM (Support Vector Machines) classification model.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2021-12-22T18:49:45Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) SSPDC21.pdf: 593173 bytes, checksum: c3bcb81b64412e329daa56eb303972e9 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2021-12-22T19:43:33Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) SSPDC21.pdf: 593173 bytes, checksum: c3bcb81b64412e329daa56eb303972e9 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2021-12-23T13:49:11Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) SSPDC21.pdf: 593173 bytes, checksum: c3bcb81b64412e329daa56eb303972e9 (MD5) Previous issue date: 2021Este trabajo fue parcialmente financiado por la Comisión Académica de Posgrado (CAP, UdelaR), Espacio Interdisciplinario (EI, UdelaR) y la Comisión Sectorial de Investigación Científica (CSIC, UdelaR) “Proyecto I + D : Sistema electrónico para la caracterización del comportamiento de ovinos".5 p.application/pdfenengUdelar.FI.IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov 2021, pp. 1-5.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Human Activity RecognitionAcceleration SensorLinear Discriminant AnalysisSupport Vector MachinesHuman activity recognition using machine learning techniques in a low-resource embedded systemPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaStolovas, IlanaSuárez, SantiagoPereyra, DiegoDe Izaguirre, FranciscoCabrera, VariniaElectrónicaMicroelectrónicaLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/30548/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/30548/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | Human activity recognition using machine learning techniques in a low-resource embedded system Stolovas, Ilana Human Activity Recognition Acceleration Sensor Linear Discriminant Analysis Support Vector Machines |
| status_str | submittedVersion |
| title | Human activity recognition using machine learning techniques in a low-resource embedded system |
| title_full | Human activity recognition using machine learning techniques in a low-resource embedded system |
| title_fullStr | Human activity recognition using machine learning techniques in a low-resource embedded system |
| title_full_unstemmed | Human activity recognition using machine learning techniques in a low-resource embedded system |
| title_short | Human activity recognition using machine learning techniques in a low-resource embedded system |
| title_sort | Human activity recognition using machine learning techniques in a low-resource embedded system |
| topic | Human Activity Recognition Acceleration Sensor Linear Discriminant Analysis Support Vector Machines |
| url | https://hdl.handle.net/20.500.12008/30548 |
