WeAllWalk

Author:

Flores Germán H.1,Manduchi Roberto1

Affiliation:

1. University of California, Santa Cruz, CA

Abstract

We introduce WeAllWalk, a dataset of inertial sensor time series collected from blind and sighted walkers using a long cane or a guide dog. Ten blind volunteers (seven using a long cane, one using a guide dog, and two alternating use of a long cane and of a guide dog) as well as five sighted volunteers contributed to the data collection. The participants walked through fairly long and complex indoor routes that included obstacles to be avoided and doors to be opened. Inertial data were recorded by two iPhone 6s carried by our participants in their pockets and carefully annotated. Ground-truth heel strike times were measured by two small inertial sensor units clipped to the participants’ shoes. We also present an in-depth comparative analysis of various step counting and turn detection algorithms as tested on WeAllWalk. This analysis reveals interesting differences between the achievable accuracy of step and turn detection across different communities of sighted and blind walkers.

Funder

CITRIS

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,Human-Computer Interaction

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SIMUL: Synchronized IMU Dataset of Walking People at Six Body Locations;2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN);2023-09-25

2. Sonification of navigation instructions for people with visual impairment;International Journal of Human-Computer Studies;2023-09

3. Estimation of Spatio-Temporal Parameters of Gait and Posture of Visually Impaired People Using Wearable Sensors;Sensors;2023-06-14

4. Step Counting with Attention-based LSTM;2022 IEEE Symposium Series on Computational Intelligence (SSCI);2022-12-04

5. Recent Advances in Pedestrian Inertial Navigation Based on Smartphone: A Review;IEEE Sensors Journal;2022-12-01

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