Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, a novel occupant identification solution that makes use of existing and prevalent on-object sensors that are originally designed to monitor the status of objects they are attached to. SenseTribute extracts richer information content from such on-object sensors and analyzes the data to accurately identify the person interacting with the objects. This approach is based on the physical phenomenon that different occupants interact with objects in different ways. Moreover, SenseTribute may not rely on users’ true identities, so the approach works even without labeled training data. However, resolution of information from a single on-object sensor may not be sufficient to differentiate occupants, which may lead to errors in identification. To overcome this problem, SenseTribute operates over a sequence of events within a user activity, leveraging recent work on activity segmentation. We evaluate SenseTribute using real-world experiments by deploying sensors on five distinct objects in a kitchen and inviting participants to interact with the objects. We demonstrate that SenseTribute can correctly identify occupants in 96% of trials without labeled training data, while per-sensor identification yields only 74% accuracy even with training data.
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Privacy-Preserving Understanding of Human Body Orientation for Smart Meetings
We present a method for estimating the body orientation of seated people in a smart room by fusing low-resolution range information collected from downward pointed time-of-flight (ToF) sensors with synchronized speaker identification information from microphone recordings. The ToF sensors preserve the privacy of the occupants in that they only return the range to a small set of hit points. We propose a Bayesian estimation algorithm for the quantized body orientations in which the likelihood term is based on the observed ToF data and the prior term is based on the occupants’ locations and current speakers. We evaluate our algorithm
in real meeting scenarios and show that it is possible to accurately estimate seated human orientation even with very low-resolution systems.
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- Award ID(s):
- 1631674
- NSF-PAR ID:
- 10026263
- Date Published:
- Journal Name:
- 13th IEEE Workshop on Perception Beyond the Visible Spectrum (in conjunction with CVPR 2017)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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