Step Length Estimation Using Handheld Inertial Sensors

Renaudin, Valérie; Susi, Melania; Lachapelle, G.

doi:10.3390/s120708507

Cited by 204 publications

(141 citation statements)

References 18 publications

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“…For this reason, here, a structured set of thresholds for reliability assessment is proposed, starting from the general accepted value of 5% [16,[27][28][29][30]. A gradual scale of reliability evaluation based on quantitative criteria for each spatiotemporal parameter and step number is used as acceptability criteria to evaluate the results (Table 11).…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, there are solutions to solve the drift error so that it is possible to integrate twice the raw acceleration data and to measure distance [9,15]. By integration, the typical percentages of error over the walking distance are between 2.5% and 5.0% [16]. Other studies face the interpretation of 3D movements of the COM with more complex models [17][18][19][20][21].…”

Section: Theoretical Modelmentioning

confidence: 99%

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Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

2018

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A model based on a harmonic oscillator describing human walking and balance with the sinusoidal trajectory of the center of mass of a subject during gait is presented. This model allows overcoming the traditional drift due to the double integration of raw acceleration data. The protocol uses a single 3D accelerometer worn at the pelvis level. The system computes the spatiotemporal gait and balance parameters when the subject is walking with or without aids. An incremental methodological approach is proposed and followed in the implementation and accuracy assessment. Eleven healthy subjects have participated to the study performing 6 trials over a fixed linear walking path at a self-selected speed. For reference, the protocol has imposed the execution of 52 steps whose length has been fixed at 60 cm. Different processing methods have been implemented and tested. The model identifies steps, walking time, and stepping frequency with an excellent reliability (absolute percentage accuracy error < 5%). When the information about the expected step length is given to the model, the percentage error in the measure of walking distance and speed is 3.25%. Without this input, this error rises to 4.95%, while for the anthropometric method is 3.68%.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Modelmentioning

confidence: 99%

Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

2018

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“…Another aspect is the fact that the step length has been assumed fixed and known. As it has been shown in the literature (see, for example [11], [20]), this should rather be estimated either from a person's physical attributes or directly from the measurement data (which would require some extensions to the measurement model).…”

Section: Discussionmentioning

confidence: 99%

IMU and magnetometer modeling for smartphone-based PDR

Hostettler

Särkkä

2016

2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-In this paper, IMU and magnetometer models that enable pedestrian dead-reckoning without step detection or zero velocity updates on hand-held devices such as smartphones are proposed. The models are suitable for usage with any standard Bayesian filtering or smoothing technique and thus, do not require customized estimators. The method is evaluated in a real scenario using a dataset of approximately three minutes with a trajectory length of 302 m. It is found that the overall shape of the estimated trajectory matches well with the GPS reference trajectory with an estimated trajectory length of 293 m. Some problems in the heading estimation are observed.

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“…For our PNS, we have selected the Weinberg algorithm [19]. Although Renaudin's algorithm [23] is more precise, the robustness of Weinberg's algorithm is higher and the calibration process is easier. Besides, in Ref.…”

Section: Pdrmentioning

confidence: 99%

Building up an Inertial Navigation System Using Standard Mobile Devices

Gunia¹,

Wu²,

Joram³

et al. 2017

JEE

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This paper presents the development of a PNS (Pedestrian Navigation System), which utilizes accelerometer, gyroscope and magnetometer data to enable accurate positioning. Therefore, the sensor basics as well as the mathematics regarding reference frames and coordinate transformations are introduced initially. Particular focus is given to quaternions, since they provide a performance-effective means to execute rotations. In great detail the two distinct approaches for a PNS are introduced, i.e. INS (inertial navigation systems) and PDR (pedestrian dead reckoning). For each, a comparison of state-of-the-art techniques is presented. Special attention is paid to orientation estimation and stance phase detection. Our system combines the most promising techniques and describes improvements, whose usefulness becomes obvious in our experiments. We have applied our PNS in five different test scenarios. For the most complex rectangular-shaped use case, we achieve on average error of 1.66% with regard to the total travelled distance, which is superior to other recent PNS utilizing comparable sensors.

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Step Length Estimation Using Handheld Inertial Sensors

Cited by 204 publications

References 18 publications

Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

Method for Wearable Kinematic Gait Analysis Using a Harmonic Oscillator Applied to the Center of Mass

IMU and magnetometer modeling for smartphone-based PDR

Building up an Inertial Navigation System Using Standard Mobile Devices

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