Resumen
With the continuous evolution of technology, mobile devices are becoming more and more important in people’s lives. In the same way, new needs related to the information provided by their users arise, making evident the need to develop systems that take advantage of their daily use. The recognition of personal activity based on the information provided by the last generation mobile devices can easily be considered as an useful tool for many purposes and future applications. This paper presents the use of information provided from two smart devices in different acquisition schemes, assessing conventional supervised classifiers to recognize personal activity by an identification of seven classes. The classifiers were trained with a generated database from eight users and were evaluated in offline mode with other two generated databases. The prediction experiments were qualified by using F1-score indicator and were compared with the native prediction from the cellphone. The obtained results presented a maximum F1-score of 100% for the first validation test and 80.7% for the second validation test.
| Idioma original | Inglés |
|---|---|
| Título de la publicación alojada | Applied Computer Sciences in Engineering - 6th Workshop on Engineering Applications, WEA 2019, Proceedings |
| Editores | Juan Carlos Figueroa-García, Mario Duarte-González, Sebastián Jaramillo-Isaza, Alvaro David Orjuela-Cañon, Yesid Díaz-Gutierrez |
| Editorial | Springer Healthcare |
| Páginas | 487-499 |
| Número de páginas | 13 |
| ISBN (versión impresa) | 9783030310189 |
| DOI | |
| Estado | Publicada - 1 ene 2019 |
| Evento | 6th Workshop on Engineering Applications, WEA 2019 - Santa Marta, Colombia Duración: 16 oct 2019 → 18 oct 2019 |
Serie de la publicación
| Nombre | Communications in Computer and Information Science |
|---|---|
| Volumen | 1052 |
| ISSN (versión impresa) | 1865-0929 |
| ISSN (versión digital) | 1865-0937 |
Conferencia
| Conferencia | 6th Workshop on Engineering Applications, WEA 2019 |
|---|---|
| País | Colombia |
| Ciudad | Santa Marta |
| Período | 16/10/19 → 18/10/19 |
Huella dactilar
Citar esto
}
A Personal Activity Recognition System Based on Smart Devices. / Murcia, Harold; Triana, Juanita.
Applied Computer Sciences in Engineering - 6th Workshop on Engineering Applications, WEA 2019, Proceedings. ed. / Juan Carlos Figueroa-García; Mario Duarte-González; Sebastián Jaramillo-Isaza; Alvaro David Orjuela-Cañon; Yesid Díaz-Gutierrez. Springer Healthcare, 2019. p. 487-499 (Communications in Computer and Information Science; Vol. 1052).Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia
TY - GEN
T1 - A Personal Activity Recognition System Based on Smart Devices
AU - Murcia, Harold
AU - Triana, Juanita
PY - 2019/1/1
Y1 - 2019/1/1
N2 - With the continuous evolution of technology, mobile devices are becoming more and more important in people’s lives. In the same way, new needs related to the information provided by their users arise, making evident the need to develop systems that take advantage of their daily use. The recognition of personal activity based on the information provided by the last generation mobile devices can easily be considered as an useful tool for many purposes and future applications. This paper presents the use of information provided from two smart devices in different acquisition schemes, assessing conventional supervised classifiers to recognize personal activity by an identification of seven classes. The classifiers were trained with a generated database from eight users and were evaluated in offline mode with other two generated databases. The prediction experiments were qualified by using F1-score indicator and were compared with the native prediction from the cellphone. The obtained results presented a maximum F1-score of 100% for the first validation test and 80.7% for the second validation test.
AB - With the continuous evolution of technology, mobile devices are becoming more and more important in people’s lives. In the same way, new needs related to the information provided by their users arise, making evident the need to develop systems that take advantage of their daily use. The recognition of personal activity based on the information provided by the last generation mobile devices can easily be considered as an useful tool for many purposes and future applications. This paper presents the use of information provided from two smart devices in different acquisition schemes, assessing conventional supervised classifiers to recognize personal activity by an identification of seven classes. The classifiers were trained with a generated database from eight users and were evaluated in offline mode with other two generated databases. The prediction experiments were qualified by using F1-score indicator and were compared with the native prediction from the cellphone. The obtained results presented a maximum F1-score of 100% for the first validation test and 80.7% for the second validation test.
KW - Activity recognition
KW - Cell phone data
KW - Machine learning
KW - Myo armband
KW - Wearable devices
UR - http://www.scopus.com/inward/record.url?scp=85075680771&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-31019-6_41
DO - 10.1007/978-3-030-31019-6_41
M3 - Contribución a la conferencia
AN - SCOPUS:85075680771
SN - 9783030310189
T3 - Communications in Computer and Information Science
SP - 487
EP - 499
BT - Applied Computer Sciences in Engineering - 6th Workshop on Engineering Applications, WEA 2019, Proceedings
A2 - Figueroa-García, Juan Carlos
A2 - Duarte-González, Mario
A2 - Jaramillo-Isaza, Sebastián
A2 - Orjuela-Cañon, Alvaro David
A2 - Díaz-Gutierrez, Yesid
PB - Springer Healthcare
ER -