A Novel Method for Tilt Compensation in Inertial Sensor Systems

The kinematics of the human body is very complex. Every movement involves many joints, muscles and a special nervous control. The modern miniaturized inertial sensor systems prove to be valuable tools for rehabilitation medicine. We present the way a system of inertial sensors can be used to compare healthy and affected lower limb movements during gait.

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Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Sensors ◽

10.3390/s18093149 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3149 ◽

Cited By ~ 15

Author(s):

Jiheon Kang ◽

Joonbeom Lee ◽

Doo-Seop Eom

Keyword(s):

Indoor Localization ◽

Inertial Sensor ◽

Distance Estimation ◽

Dead Reckoning ◽

Android Application ◽

Sensory Signal ◽

Novel Method ◽

The Cost ◽

Deep Learning Model ◽

Pedestrian Dead Reckoning

We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

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Reliability, Validity and Utility of Inertial Sensor Systems for Postural Control Assessment in Sport Science and Medicine Applications: A Systematic Review

Sports Medicine ◽

10.1007/s40279-019-01095-9 ◽

2019 ◽

Vol 49 (5) ◽

pp. 783-818 ◽

Cited By ~ 13

Author(s):

William Johnston ◽

Martin O’Reilly ◽

Rob Argent ◽

Brian Caulfield

Keyword(s):

Systematic Review ◽

Postural Control ◽

Inertial Sensor ◽

Sensor Systems ◽

Sport Science

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A Wearable Attitude-Measurement System Using a Fiberoptic Gyroscope

Presence Teleoperators & Virtual Environments ◽

10.1162/1054746021470568 ◽

2002 ◽

Vol 11 (2) ◽

pp. 109-118 ◽

Cited By ~ 3

Author(s):

Keiichi Sawada ◽

Masayuki Okihara ◽

Shigeru Nakamura

Keyword(s):

Measurement System ◽

Mixed Reality ◽

Inertial Sensor ◽

Attitude Measurement ◽

Sensor Systems ◽

Wearable Sensor ◽

Measurement Systems

An attitude-measurement system (TISS-5-40) has been developed to achieve a wearable sensor for individuals. This equipment is one of the inertial sensor systems having three fiberoptic gyroscopes and three accelerometers. Heading stability of 1 deg./hr. (1σ) and attitude accuracy of ±0.5 deg. have been demonstrated. At present, some of the attitude-measurement Systems have been applied in the field of mixed-reality technology, and the users confirm and report its effectiveness (Hara, Anabuki, Satoh, Yamamoto, & Tamura, 2000).

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