Automated Detection of Activity Transitions for Prompting

Kyle D Feuz; Diane J Cook; Cody Rosasco; Kayela Robertson; Maureen Schmitter-Edgecombe

doi:10.1109/THMS.2014.2362529

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Automated Detection of Activity Transitions for Prompting

Journal article

Open access

Peer reviewed

Automated Detection of Activity Transitions for Prompting

Kyle D Feuz, Diane J Cook, Cody Rosasco, Kayela Robertson and Maureen Schmitter-Edgecombe

IEEE transactions on human-machine systems, Vol.45(5), pp.575-585

10/2015

DOI: https://doi.org/10.1109/THMS.2014.2362529

Handle:

https://hdl.handle.net/2376/116096

PMCID: PMC4805372

PMID: 27019791

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Abstract

Temperature sensors

Supervised learning

Smart homes

prompting systems

Sensor phenomena and characterization

Activity recognition

change-point detection

machine learning

smart environments

Intelligent sensors

Individuals with cognitive impairment can benefit from intervention strategies like recording important information in a memory notebook. However, training individuals to use the notebook on a regular basis requires a constant delivery of reminders. In this study, we design and evaluate machine-learning-based methods for providing automated reminders using a digital memory notebook interface. Specifically, we identify transition periods between activities as times to issue prompts. We consider the problem of detecting activity transitions using supervised and unsupervised machine-learning techniques and find that both techniques show promising results for detecting transition periods. We test the techniques in a scripted setting with 15 individuals. Motion sensors data are recorded and annotated as participants perform a fixed set of activities. We also test the techniques in an unscripted setting with eight individuals. Motion sensor data are recorded as participants go about their normal daily routine. In both the scripted and unscripted settings, a true positive rate of greater than 80% can be achieved while maintaining a false positive rate of less than 15%. On average, this leads to transitions being detected within 1 min of a true transition for the scripted data and within 2 min of a true transition on the unscripted data.

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Title: Automated Detection of Activity Transitions for Prompting
Creators: Kyle D Feuz - Comput. Sci. Dept., Weber State Univ., Ogden, UT, USA
Diane J Cook - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Cody Rosasco - Dept. of Psychol., Washington State Univ., Pullman, WA, USA
Kayela Robertson - Dept. of Psychol., Washington State Univ., Pullman, WA, USA
Maureen Schmitter-Edgecombe - Dept. of Psychol., Washington State Univ., Pullman, WA, USA
Publication Details: IEEE transactions on human-machine systems, Vol.45(5), pp.575-585
Academic Unit: Psychology, Department of; Electrical Engineering and Computer Science, School of
Publisher: IEEE
Identifiers: 99900547992101842
Language: English
Resource Type: Journal article