Abstract
Automatic Labeling Framework for Wearable Sensor-based Human Activity Recognition
Highlights
Since the 1990s, researchers have begun to use wearable sensors for human activity recognition (HAR).(1) From the perspective of sensor types, research studies on HAR mainly include vision sensors, ambient sensors in smart home scenes, and wearable sensors [such as accelerometers, gyroscopes, and inertial measurement units (IMUs)], which mainly apply supervised learning methods to learn different human activity patterns from collected human motion data
An annotator needs to compare between the video footage of the whole data acquisition process and the acquired time series to complete raw data annotation.[9,10] For long-term HAR studies, especially focusing on activity monitoring, online methods are more appealing for realistic application whereas offline methods almost make it impossible to obtain ground truth labels, which are always labor-intensive and usually unacceptable due to privacy concerns.[11]. There is a tradeoff between the accuracy of an annotation method and the time and effort required for annotation
We aimed at reducing labeling efforts on time series data, which is collected over diverse individuals using multiple body-worn IMUs in a laboratory setting
Summary
Since the 1990s, researchers have begun to use wearable sensors for human activity recognition (HAR).(1) From the perspective of sensor types, research studies on HAR mainly include vision sensors (such as cameras), ambient sensors in smart home scenes, and wearable sensors [such as accelerometers, gyroscopes, and inertial measurement units (IMUs)], which mainly apply supervised learning methods to learn different human activity patterns from collected human motion data. Video and audio recordings,(3,4) and online methods include direct observations,(5) time diary, and experience sampling.[6,7] For studies acquiring data in a laboratory scenario, direct observation and video recordings are usually taken as the annotation methods, which might be called on-site annotation and post hoc annotation, respectively.[8] For the former method, an annotator records the timestamp range of human activity currently performed by a subject For the latter method, an annotator needs to compare between the video footage of the whole data acquisition process and the acquired time series to complete raw data annotation.[9,10] For long-term HAR studies, especially focusing on activity monitoring, online methods are more appealing for realistic application whereas offline methods almost make it impossible to obtain ground truth labels, which are always labor-intensive and usually unacceptable due to privacy concerns.[11] There is a tradeoff between the accuracy of an annotation method and the time and effort required for annotation. Online methods are less time-consuming, inaccurate annotations and more ambiguity may be introduced to the labeled dataset
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