Child-activity recognition from multi-sensor data

Sabri Boughorbel; Jeroen Breebaart; Fons Bruekers; Ingrid Flinsenberg; Warner Ten Kate

doi:10.1145/1931344.1931382

Child-activity recognition from multi-sensor data

Sabri Boughorbel^*
, Jeroen Breebaart
, Fons Bruekers
, Ingrid Flinsenberg
, Warner Ten Kate

^*Corresponding author for this work

Koninklijke Philips N.V.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The automatic recognition of child activity using multi-sensor data enablesvarious applications such as child-development monitoring, energy-expenditure estimation, child-obesity prevention, child safety in and around the home, etc. We formulate the activity recognition task as a classification problembased on multiple sensors embedded in a wearable device. The approach we propose in this paper isto apply spectral analysistechniques of multiple sensor data for activity recognition. Quadratic Discriminant Analysis (QDA) classifieris then trained using manually annotated data and applied for activity recognition. The obtained experimental results for the recognition of 7 activities based on a limited data set are promising and show the potential of the proposed method.

Original language	English
Title of host publication	Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10
DOIs	https://doi.org/10.1145/1931344.1931382
Publication status	Published - 2011
Externally published	Yes
Event	7th International Conference on Methods and Techniques in Behavioral Research, MB'10 - Eindhoven, Netherlands Duration: 24 Aug 2010 → 27 Aug 2010

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	7th International Conference on Methods and Techniques in Behavioral Research, MB'10
Country/Territory	Netherlands
City	Eindhoven
Period	24/08/10 → 27/08/10

Keywords

Activity classification
Activity recognition
Feature extraction

Access to Document

10.1145/1931344.1931382

Cite this

Boughorbel, S., Breebaart, J., Bruekers, F., Flinsenberg, I., & Ten Kate, W. (2011). Child-activity recognition from multi-sensor data. In Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10 Article 38 (ACM International Conference Proceeding Series). https://doi.org/10.1145/1931344.1931382

@inproceedings{f7e3e4047cf04dd99f6711b3ff4f4455,

title = "Child-activity recognition from multi-sensor data",

abstract = "The automatic recognition of child activity using multi-sensor data enablesvarious applications such as child-development monitoring, energy-expenditure estimation, child-obesity prevention, child safety in and around the home, etc. We formulate the activity recognition task as a classification problembased on multiple sensors embedded in a wearable device. The approach we propose in this paper isto apply spectral analysistechniques of multiple sensor data for activity recognition. Quadratic Discriminant Analysis (QDA) classifieris then trained using manually annotated data and applied for activity recognition. The obtained experimental results for the recognition of 7 activities based on a limited data set are promising and show the potential of the proposed method.",

keywords = "Activity classification, Activity recognition, Feature extraction",

author = "Sabri Boughorbel and Jeroen Breebaart and Fons Bruekers and Ingrid Flinsenberg and \{Ten Kate\}, Warner",

year = "2011",

doi = "10.1145/1931344.1931382",

language = "English",

isbn = "9781605589268",

series = "ACM International Conference Proceeding Series",

booktitle = "Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10",

note = "7th International Conference on Methods and Techniques in Behavioral Research, MB'10 ; Conference date: 24-08-2010 Through 27-08-2010",

}

Boughorbel, S, Breebaart, J, Bruekers, F, Flinsenberg, I & Ten Kate, W 2011, Child-activity recognition from multi-sensor data. in Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10., 38, ACM International Conference Proceeding Series, 7th International Conference on Methods and Techniques in Behavioral Research, MB'10, Eindhoven, Netherlands, 24/08/10. https://doi.org/10.1145/1931344.1931382

Child-activity recognition from multi-sensor data. / Boughorbel, Sabri; Breebaart, Jeroen; Bruekers, Fons et al.
Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10. 2011. 38 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Child-activity recognition from multi-sensor data

AU - Boughorbel, Sabri

AU - Breebaart, Jeroen

AU - Bruekers, Fons

AU - Flinsenberg, Ingrid

AU - Ten Kate, Warner

PY - 2011

Y1 - 2011

N2 - The automatic recognition of child activity using multi-sensor data enablesvarious applications such as child-development monitoring, energy-expenditure estimation, child-obesity prevention, child safety in and around the home, etc. We formulate the activity recognition task as a classification problembased on multiple sensors embedded in a wearable device. The approach we propose in this paper isto apply spectral analysistechniques of multiple sensor data for activity recognition. Quadratic Discriminant Analysis (QDA) classifieris then trained using manually annotated data and applied for activity recognition. The obtained experimental results for the recognition of 7 activities based on a limited data set are promising and show the potential of the proposed method.

AB - The automatic recognition of child activity using multi-sensor data enablesvarious applications such as child-development monitoring, energy-expenditure estimation, child-obesity prevention, child safety in and around the home, etc. We formulate the activity recognition task as a classification problembased on multiple sensors embedded in a wearable device. The approach we propose in this paper isto apply spectral analysistechniques of multiple sensor data for activity recognition. Quadratic Discriminant Analysis (QDA) classifieris then trained using manually annotated data and applied for activity recognition. The obtained experimental results for the recognition of 7 activities based on a limited data set are promising and show the potential of the proposed method.

KW - Activity classification

KW - Activity recognition

KW - Feature extraction

UR - https://www.scopus.com/pages/publications/79952520898

U2 - 10.1145/1931344.1931382

DO - 10.1145/1931344.1931382

M3 - Conference contribution

AN - SCOPUS:79952520898

SN - 9781605589268

T3 - ACM International Conference Proceeding Series

BT - Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10

T2 - 7th International Conference on Methods and Techniques in Behavioral Research, MB'10

Y2 - 24 August 2010 through 27 August 2010

ER -

Child-activity recognition from multi-sensor data

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this