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Hierarchical feature recovery for robust human activity recognition in body sensor networks

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Version 2 2023-06-12, 07:49
Version 1 2023-06-10, 04:51
conference contribution
posted on 2023-06-12, 07:49 authored by Nobuyuki OishiNobuyuki Oishi, Paula Lago, Phil BirchPhil Birch, Daniel RoggenDaniel Roggen
With the advances in Body Sensor Networks (BSNs) and textile-integrated sensing, more sensor data becomes available from which human activities are recognised. However, some sensors may become unavailable unexpectedly in practice. Previous work proposed to complement the features of a missing sensor with regression-based methods but considered only up to one sensor missing and thus lacked a mechanism for selecting relevant sensors when multiple sensors were missing. The number of unique combinations of missing sensors increases exponentially when multiple sensors may be missing. To handle this, we propose a Hierarchical Feature Recovery (HFR) approach. We first assess the dependencies between sensors by comparing the feature mapping accuracy between each sensor and then evaluate the HFR approach on a dataset of activities of daily living with 17 gestures using 14 motion sensors. Our HFR method can alleviate classification performance drop by up to 8.3 pp compared to a baseline method.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC '22 Adjunct)

Publisher

ACM

Event name

10th International Workshop on Human Activity Sensing Corpus and its Application

Event location

Cambridge

Event type

conference

Event date

September 11-15 2022

ISBN

9781450394239

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Sensor Technology Research Centre Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-09-27

First Open Access (FOA) Date

2022-09-27

First Compliant Deposit (FCD) Date

2022-09-27

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