research-article

Exploring Accelerometer-based Step Detection by using a Wheeled Walking Frame

Authors:

Marian Haescher,

Paul Hanschmann,

Denys J.C. MatthiesAuthors Info & Claims

iWOAR '18: Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction

Article No.: 8, Pages 1 - 6

https://doi.org/10.1145/3266157.3266212

Published: 20 September 2018 Publication History

Abstract

Step detection with accelerometers is a very common feature that smart wearables already include. However, when using a wheeled walking frame / rollator, current algorithms may be of limited use, since a different type of motion is being excreted. In this paper, we uncover these limitations of current wearables by a pilot study. Furthermore, we investigated an accelerometer-based step detection for using a wheeled walking frame, when mounting an accelerometer to the frame and at the user's wrist. Our findings include knowledge on signal propagation of each axis, knowledge on the required sensor quality and knowledge on the impact of different surfaces and floor types. In conclusion, we outline a new step detection algorithm based on accelerometer input data. Our algorithm can significantly empower future off-the-shelf wearables with the capability to sufficiently detect steps with elderly people using a wheeled walking frame. This can help to evaluate the state of health with regard to the human behavior and motor system and even to determine the progress of certain diseases.

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Cited By

Bieber GKraft DHölle BBlenke DBader RHerrmann PMakedon F(2022)Determination of the healing corridor of patients with knee arthroplasty by a motor-powered rollatorProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3529190.3534730(390-395)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.1145/3529190.3534730
Cajamarca GHerskovic VRossel P(2020)Enabling Older Adults’ Health Self-Management through Self-Report and Visualization—A Systematic Literature ReviewSensors10.3390/s2015434820:15(4348)Online publication date: 4-Aug-2020
https://doi.org/10.3390/s20154348
Alinia PFallahzadeh RConnolly CGhasemzadeh H(2020)ParaLabel: Autonomous Parameter Learning for Cross-Domain Step Counting in Wearable SensorsIEEE Sensors Journal10.1109/JSEN.2020.300923120:23(13867-13879)Online publication date: 1-Dec-2020
https://doi.org/10.1109/JSEN.2020.3009231
Show More Cited By

Index Terms

Exploring Accelerometer-based Step Detection by using a Wheeled Walking Frame
1. Human-centered computing
  1. Accessibility
    1. Accessibility technologies
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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cover image ACM Other conferences

iWOAR '18: Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction

September 2018

148 pages

ISBN:9781450364874

DOI:10.1145/3266157

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

Fraunhofer IGD: Fraunhofer Institute for Computer Graphics Research IGD
Rostock: University of Rostock

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2018

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iWOAR '18

iWOAR '18: 5th international Workshop on Sensor-based Activity Recognition and Interaction

September 20 - 21, 2018

Berlin, Germany

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iWOAR '18 Paper Acceptance Rate 15 of 28 submissions, 54%;

Overall Acceptance Rate 46 of 73 submissions, 63%

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Cited By

Bieber GKraft DHölle BBlenke DBader RHerrmann PMakedon F(2022)Determination of the healing corridor of patients with knee arthroplasty by a motor-powered rollatorProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3529190.3534730(390-395)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.1145/3529190.3534730
Cajamarca GHerskovic VRossel P(2020)Enabling Older Adults’ Health Self-Management through Self-Report and Visualization—A Systematic Literature ReviewSensors10.3390/s2015434820:15(4348)Online publication date: 4-Aug-2020
https://doi.org/10.3390/s20154348
Alinia PFallahzadeh RConnolly CGhasemzadeh H(2020)ParaLabel: Autonomous Parameter Learning for Cross-Domain Step Counting in Wearable SensorsIEEE Sensors Journal10.1109/JSEN.2020.300923120:23(13867-13879)Online publication date: 1-Dec-2020
https://doi.org/10.1109/JSEN.2020.3009231
Bieber GChodan WBader RHölle BHerrmann PDreher IMakedon F(2019)RoRoProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3316782.3322779(430-434)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3316782.3322779
Matthies DHaescher MNanayakkara SBieber G(2018)Step Detection for Rollator Users with SmartwatchesProceedings of the 2018 ACM Symposium on Spatial User Interaction10.1145/3267782.3267784(163-167)Online publication date: 13-Oct-2018
https://dl.acm.org/doi/10.1145/3267782.3267784

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