NDLI: Assessing activity recognition feedback in long-term psychology trials

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Assessing activity recognition feedback in long-term psychology trials

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Assessing activity recognition feedback in long-term psychology trials

Content Provider	ACM Digital Library
Author	Berlin, Eugen Dietrich, Manuel van Laerhoven, Kristof
Abstract	The physical activities we perform throughout our daily lives tell a great deal about our goals, routines, and behavior, and as such, have been known for a while to be a key indicator for psychiatric disorders. This paper focuses on the use of a wrist-watch with integrated inertial sensors. The algorithms that deal with the data from these sensors can automatically detect the activities that the patient performed from characteristic motion patterns. Such a system can be deployed for several weeks continuously and can thus provide the consulting psychiatrist an insight in their patient's behavior and changes thereof. Since these algorithms will never be flawless, however, a remaining question is how we can support the psychiatrist in assigning confidence to these automatic detections. To this end, we present a study where visualizations at three levels from a detection algorithm are used as feedback, and examine which of these are the most helpful in conveying what activities the patient has performed. Results show that just visualizing the classifier's output performs the best, but that user's confidence in these automated predictions can be boosted significantly by visualizing earlier pre-processing steps.
Starting Page	121
Ending Page	130
Page Count	10
File Format	PDF
ISBN	9781450336055
DOI	10.1145/2836041.2836052
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Context-aware services Interaction design Activity recognition Visualization methods
Content Type	Text
Resource Type	Article

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