Research Online

Logo

Goldsmiths - University of London

Wearable Eye Tracking for Multisensor Physical Activity Recognition

Hevesi, Peter; Ward, Jamie A; Amiraslanov, Orkham; Pirkl, Gerald and Lukowicz, Paul. 2018. Wearable Eye Tracking for Multisensor Physical Activity Recognition. International Journal On Advances in Life Sciences, 10(12), pp. 103-116. [Article]

[img]
Preview
Text
wearable_eye_tracking_for_multisensor_physical_activity_recognition.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (11MB) | Preview

Abstract or Description

This paper explores the use of wearable eye-tracking to detect physical activities and location information during assembly and construction tasks involving small groups of up to four people. Large physical activities, like carrying heavy items and walking, are analysed alongside more precise, hand-tool activities, like using a drill, or a screwdriver. In a first analysis, gazeinvariant features from the eye-tracker are classified (using Naive Bayes) alongside features obtained from wrist-worn accelerometers and microphones. An evaluation is presented using data from an 8-person dataset containing over 600 physical activity events, performed under real-world (noisy) conditions. Despite the challenges of working with complex, and sometimes unreliable, data we show that event-based precision and recall of 0.66 and 0.81 respectively can be achieved by combining all three sensing modalities (using experiment independent training, and temporal smoothing). In a further analysis, we apply state-ofthe-art computer vision methods like object recognition, scene recognition, and face detection, to generate features from the eye-trackers’ egocentric videos. Activity recognition trained on the output of an object recognition model (e.g., VGG16 trained on ImageNet) could predict Precise activities with an (overall average) f-measure of 0.45. Location of participants was similarly obtained using visual scene recognition, with average precision and recall of 0.58 and 0.56.

Item Type:

Article

Keywords:

Wearable sensors, Machine learning, Activity recognition, Feature extraction, Computer vision

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
6 May 2018Accepted
30 June 2018Published

Item ID:

27405

Date Deposited:

04 Nov 2019 11:25

Last Modified:

05 Nov 2019 05:03

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/27405

View statistics for this item...

Edit Record Edit Record (login required)