TU Darmstadt / ULB / TUbiblio

User Independent, Multi-Modal Spotting of Subtle Arm Actions with Minimal Training Data

Bauer, Gerald and Blanke, Ulf and Lukowicz, Paul and Schiele, Bernt (2013):
User Independent, Multi-Modal Spotting of Subtle Arm Actions with Minimal Training Data.
In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE Computer Society, San Diego, CA, USA, pp. 8-13, ISBN 978-1-4673-5075-4,
DOI: 10.1109/PerComW.2013.6529448,
[Conference or Workshop Item]

Abstract

We address a specific, particularly difficult class of activity recognition problems defined by (1) subtle, and hardly discriminative hand motions such as a short press or pull, (2) large, ill defined NULL class (any other hand motion a person may express during normal life), and (3) difficulty of collecting sufficient training data, that generalizes well from one to multiple users. In essence we intend to spot activities such as opening a cupboard, pressing a button, or taking an object from a shelve in a large data stream that contains typical every day activity. We focus on body-worn sensors without instrumenting objects, we exploit available infrastructure information, and we perform a one-to-many-users training scheme for minimal training effort. We demonstrate that a state of the art motion sensors based approach performs poorly under such conditions (Equal Error Rate of 18% in our experiments). We present and evaluate a new multi modal system based on a combination of indoor location with a wrist mounted proximity sensor, camera and inertial sensor that raises the EER to 79%.

Item Type: Conference or Workshop Item
Erschienen: 2013
Creators: Bauer, Gerald and Blanke, Ulf and Lukowicz, Paul and Schiele, Bernt
Title: User Independent, Multi-Modal Spotting of Subtle Arm Actions with Minimal Training Data
Language: German
Abstract:

We address a specific, particularly difficult class of activity recognition problems defined by (1) subtle, and hardly discriminative hand motions such as a short press or pull, (2) large, ill defined NULL class (any other hand motion a person may express during normal life), and (3) difficulty of collecting sufficient training data, that generalizes well from one to multiple users. In essence we intend to spot activities such as opening a cupboard, pressing a button, or taking an object from a shelve in a large data stream that contains typical every day activity. We focus on body-worn sensors without instrumenting objects, we exploit available infrastructure information, and we perform a one-to-many-users training scheme for minimal training effort. We demonstrate that a state of the art motion sensors based approach performs poorly under such conditions (Equal Error Rate of 18% in our experiments). We present and evaluate a new multi modal system based on a combination of indoor location with a wrist mounted proximity sensor, camera and inertial sensor that raises the EER to 79%.

Title of Book: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)
Publisher: IEEE Computer Society
ISBN: 978-1-4673-5075-4
Uncontrolled Keywords: Training, Sensor systems, Cameras, Training data, Support vector machines, Printers
Divisions: Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Event Location: San Diego, CA, USA
Date Deposited: 24 Aug 2017 16:27
DOI: 10.1109/PerComW.2013.6529448
Identification Number: TUD-CS-2013-0471
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)

View Item View Item