Abstract
Unknown On-Body Device Position Detection Based on Ensemble Novelty Detection
Highlights
People carry on-body devices in a variety of ways, including in pockets and bags.[1]. These ways play an important role in the usability of an on-body device and the quality of sensordependent services that facilitate human–human communication, reduce unnecessary energy consumption, and automatically select an appropriate notification method.[2,3,4] On-body device position recognition is gaining attention in ubiquitous computing communities that use multiclass classification techniques and where the number of positions is fixed before use.[2,5,6,7,8] in reality, a variety of on-body device positions are available, and a user may carry the device in a position not originally intended for the system, i.e., an unknown position
We propose an ensemble novelty detection method based on the principle of ensemble learning, such as random forest,(10) and apply it to unknown position detection
The results discussed in Sect. 3.3.4 show that the underlying assumption required for applying the Tmax estimation (Test) estimation method was not established
Summary
People carry on-body devices in a variety of ways, including in pockets and bags.[1] These ways play an important role in the usability of an on-body device and the quality of sensordependent services that facilitate human–human communication, reduce unnecessary energy consumption, and automatically select an appropriate notification method.[2,3,4] On-body device position recognition is gaining attention in ubiquitous computing communities that use multiclass classification techniques and where the number of positions is fixed before use.[2,5,6,7,8] in reality, a variety of on-body device positions are available, and a user may carry the device in a position not originally intended for the system, i.e., an unknown position. The feasibility of the proposed method is evaluated for unknown position detection in on-body device position recognition under various conditions, such as different datasets and combinations of known positions
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