Novel method for stride length estimation with body area network accelerometers

Martin, Eladio

doi:10.1109/biowireless.2011.5724356

Cited by 35 publications

(28 citation statements)

References 8 publications

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“…The estimated stride length is multiplied by the number of strides to determine the distance traveled. Empirical relationships from various studies, such as the Weinberg algorithm, rely on calibration to the individual from experimental walking data [14]. This requires leg length or subject height for inverse pendulum models [7], [15] or determination of constants through walking trials [11], [14].…”

Section: B Calculating Distance Walkedmentioning

confidence: 99%

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A smartphone approach for the 2 and 6-minute walk test

Capela

Lemaire

Baddour

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The 2 and 6-minute walk tests (2-6MWT) are used by rehabilitation professionals as a measure of exercise capacity. Our research has produced a new 2-6MWT BlackBerry smartphone application (app) that can be used to run the 2-6MWT and also provide new information about how the person moves during the test. The smartphone is worn on a belt at the lower back to record phone sensor data while walking. This data is used to identify foot strikes, calculate the total distance walked and step timing, and analyze pelvis accelerations. Information on symmetry, walking changes over time, and poor walking patterns is not available from a typical 2-6MWT and could help with clinical decision-making. The 2-6MWT app was evaluated in a pilot test using data from five able-bodied participants. Foot strike time was within 0.07 seconds when compared to gold standard video recordings. The total distance calculated by the app was within 1m of the measured distance.

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Section: B Calculating Distance Walkedmentioning

confidence: 99%

“…However, this requires careful calibration, extensive computation, and works best when the accelerometer is mounted low on the person's body (i.e., on the foot) [14] .…”

Section: B Calculating Distance Walkedmentioning

confidence: 99%

A smartphone approach for the 2 and 6-minute walk test

Capela

Lemaire

Baddour

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Existing research leverages sensors (accelerometers, magnetic compasses, gyroscopes) attached to different parts of the body including waist, thigh, shank and foot (see [4]- [6] and references therein). The main issues of these systems relate to the stride length calculation, with average errors ranging from 5% to 15% [3,7].…”

Section: A Estimation Of Kinematics Through Accelerometrymentioning

confidence: 99%

Linking Computer Vision with Off-the-Shelf Accelerometry through Kinetic Energy for Precise Localization

Martin

Shia

Yan

et al. 2011

2011 IEEE Fifth International Conference on Semantic Computing

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In this paper we propose the integration of computer vision with accelerometry in order to provide a precise localization solution. In terms of accelerometry, our approach makes use of a single off-the-shelf accelerometer on the waist to precisely obtain the velocity of the user. This allows us to calculate the kinetic energy of the person being tracked, and link the accelerometry data with the computer vision part of the system, where we employ segmentation of local regions of motion in the motion history image to estimate movement, and we leverage the number of pixels within the movement silhouettes as a metric accounting for the kinetic energy and the distance to the camera for the person being tracked. The fusion of the data from both technologies with a Kalman filter delivers an accuracy in the localization solution of up to 0.5 meters.

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“…Therefore, the diffusion constant reflects the mobility of the user, regardless of the direction of the movement. It is interesting to note that the sensors embedded in current state-of-the-art smart phones can be leveraged to obtain very precise information about the mobility of the user [12][13][14][15][16][17], which can be very helpful to manage the location of the mobile communications networks' users. Another clear consequence inferred from Figure 1 is the fact that as the p or q values increase, so does the diffusion constant, but for combinations of p and q which add up the same quantity, the diffusion constant will be higher for those values of p and q that enclose the larger area in a theoretical rectangle of dimensions q p ⋅ .…”

Section: Study Of the Diffusion Constant For One-dimensional Movementsmentioning

confidence: 99%

“…Most of the recent research in this field has focused on the evaluation of the signalling costs involved in both location update and paging [7][8][9][10]. Common techniques to assess Location Management signalling costs make use of time-varying probability distributions on the mobile user's location, derived either from motion models or approximated by means of empirical data [11][12][13][14][15][16][17]. This strategy is especially suitable when the mobile terminal changes location according to stochastic processes.…”

Section: Introductionmentioning

confidence: 99%

Analysis Of the Paging Costs In Location Management for Mobile Communications Networks Leveraging The Diffusion Constant

Martin¹,

Bajcsy²

2011

IJWMN

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The recent growth of users in mobile communications networks has shown the need to efficiently manage the signalling burden related to Mobility Management. Within this field, the optimisation of Location Management costs has become a key research topic. In this sense, the development of accurate mobility models to describe the user's behaviour is still a pending task. In this article, having characterized the diffusion constant for distinct types of movements, the evaluation of the paging costs for different motion processes through the diffusion constant is reviewed, obtaining useful guidelines for their ranking in terms of signalling costs. As a consequence of this analysis, a novel parameter is introduced to accountfor how searchable a mobile user is, and whose inverse approximates the paging costs of a wide variety of motion processes.

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Novel method for stride length estimation with body area network accelerometers

Cited by 35 publications

References 8 publications

A smartphone approach for the 2 and 6-minute walk test

A smartphone approach for the 2 and 6-minute walk test

Linking Computer Vision with Off-the-Shelf Accelerometry through Kinetic Energy for Precise Localization

Analysis Of the Paging Costs In Location Management for Mobile Communications Networks Leveraging The Diffusion Constant

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