We study activity recognition using 104 hours of annotated
data collected from a person living in an instrumented home. The home
contained over 900 sensor inputs, including wired reed switches, current
and water flow inputs, object and person motion detectors, and RFID
tags. Our aim was to compare different sensor modalities on data that
approached “real world” conditions, where the subject and annotator
were unaffiliated with the authors. We found that 10 infra-red motion
detectors outperformed the other sensors on many of the activities studied,
especially those that were typically performed in the same location.
However, several activities, in particular “eating” and “reading” were
difficult to detect, and we lacked data to study many fine-grained activities.
We characterize a number of issues important for designing activity
detection systems that may not have been as evident in prior work when
data was collected under more controlled conditions.
%0 Conference Paper
%1 Logan2007
%A Logan, Beth
%A Healey, Jennifer
%A Philipose, Matthai
%A Tapia, Emmanuel Munguia
%A Intille, Stephen S.
%B Proc. of Ubicomp07
%D 2007
%K imported
%P 483--500
%T A Long-Term Evaluation of Sensing Modalities for Activity Recognition
%X We study activity recognition using 104 hours of annotated
data collected from a person living in an instrumented home. The home
contained over 900 sensor inputs, including wired reed switches, current
and water flow inputs, object and person motion detectors, and RFID
tags. Our aim was to compare different sensor modalities on data that
approached “real world” conditions, where the subject and annotator
were unaffiliated with the authors. We found that 10 infra-red motion
detectors outperformed the other sensors on many of the activities studied,
especially those that were typically performed in the same location.
However, several activities, in particular “eating” and “reading” were
difficult to detect, and we lacked data to study many fine-grained activities.
We characterize a number of issues important for designing activity
detection systems that may not have been as evident in prior work when
data was collected under more controlled conditions.
@inproceedings{Logan2007,
abstract = {We study activity recognition using 104 hours of annotated
data collected from a person living in an instrumented home. The home
contained over 900 sensor inputs, including wired reed switches, current
and water flow inputs, object and person motion detectors, and RFID
tags. Our aim was to compare different sensor modalities on data that
approached “real world” conditions, where the subject and annotator
were unaffiliated with the authors. We found that 10 infra-red motion
detectors outperformed the other sensors on many of the activities studied,
especially those that were typically performed in the same location.
However, several activities, in particular “eating” and “reading” were
difficult to detect, and we lacked data to study many fine-grained activities.
We characterize a number of issues important for designing activity
detection systems that may not have been as evident in prior work when
data was collected under more controlled conditions.},
added-at = {2009-07-17T08:24:14.000+0200},
author = {Logan, Beth and Healey, Jennifer and Philipose, Matthai and Tapia, Emmanuel Munguia and Intille, Stephen S.},
biburl = {https://www.bibsonomy.org/bibtex/26e3b26e5855715cf63e3ef501933a218/aihec},
booktitle = {Proc. of Ubicomp07},
interhash = {72aa316a1f8ffbcaaafbfd3b93d42f05},
intrahash = {6e3b26e5855715cf63e3ef501933a218},
keywords = {imported},
owner = {Clifton Phua},
pages = {483--500},
timestamp = {2009-07-17T08:24:14.000+0200},
title = {A Long-Term Evaluation of Sensing Modalities for Activity Recognition},
year = 2007
}