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Proceedings ArticleDOI

Novel method for stride length estimation with body area network accelerometers

07 Mar 2011-pp 79-82
TL;DR: This paper presents a novel method to estimate stride length through the application of the wavelet transform to the signal obtained from a wireless accelerometer on the waist, and introduces a novel metric to determine the level of theWavelet transform detail coefficients from which the step frequency can be directly extracted.
Abstract: Gait analysis using wireless accelerometers deployed as body area networks can provide valuable information for multiple health-related applications. Within this field, stride length estimation represents a difficult task. In this paper we present a novel method to estimate stride length through the application of the wavelet transform to the signal obtained from a wireless accelerometer on the waist. We also introduce a novel metric to determine the level of the wavelet transform detail coefficients from which the step frequency can be directly extracted. Additionally, we show the correlation between the energy of the wavelet transform approximation coefficients and the speed of the gait.
Citations
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Journal ArticleDOI
TL;DR: A systematic review of current techniques for quantitative gait analysis is provided and key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors are proposed.
Abstract: After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics – hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.

293 citations


Cites background or methods from "Novel method for stride length esti..."

  • ...During this time, the leg first pushes backward and then swings forward, transforming the potential energy into kinetic energy, and resulting in the highest values in the acceleration and angular velocity signals....

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  • ...The critical gait phases can be extrapolated, as shown in Fig....

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Journal ArticleDOI
TL;DR: A position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation and achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation.
Abstract: Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper.

109 citations


Cites methods from "Novel method for stride length esti..."

  • ...The step length is estimated byWeinberg [49] approach, (22) is used for heading estimation....

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Journal ArticleDOI
TL;DR: It is demonstrated that clinically relevant 6MWT results can be achieved with typical smartphone hardware and a novel algorithm and could help with clinical decision-making.
Abstract: The 6-minute walk test (6MWT: the maximum distance walked in 6 minutes) is used by rehabilitation professionals as a measure of exercise capacity. Today’s smartphones contain hardware that can be used for wearable sensor applications and mobile data analysis. A smartphone application can run the 6MWT and provide typically unavailable biomechanical information about how the person moves during the test. A new algorithm for a calibration-free 6MWT smartphone application was developed that uses the test’s inherent conditions and smartphone accelerometer-gyroscope data to report the total distance walked, step timing, gait symmetry, and walking changes over time. This information is not available with a standard 6MWT and could help with clinical decision-making. The 6MWT application was evaluated with 15 able-bodied participants. A BlackBerry Z10 smartphone was worn on a belt at the mid lower back. Audio from the phone instructed the person to start and stop walking. Digital video was independently recorded during the trial as a gold-standard comparator. The average difference between smartphone and gold standard foot strike timing was 0.014 ± 0.015 s. The total distance calculated by the application was within 1 m of the measured distance for all but one participant, which was more accurate than other smartphone-based studies. These results demonstrated that clinically relevant 6MWT results can be achieved with typical smartphone hardware and a novel algorithm.

76 citations


Cites background or methods from "Novel method for stride length esti..."

  • ...Other formulas required experimental walking data to determine mathematical constants before distance could be accurately calculated [22,25]....

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  • ...Martin (2011) [25] Varying speeds Varying locations Accelerometer (30 Hz) Continuous wavelet transform (CWT) Stride length (step counting) Not reported...

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  • ...…[23] 20 m, level ground Left leg by hip Accelerometer (100 Hz) Neighborhood search for minimum peaks Cycle detection, distance metric EER = 5.7% Martin (2011) [25] Varying speeds Varying locations Accelerometer (30 Hz) Continuous wavelet transform (CWT) Stride length (step counting) Not…...

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  • ...This requires leg length measurement or participant height for inversed pendulum models [12,26], or determination of constants from walking trials [22,25]....

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Journal ArticleDOI
TL;DR: A novel PDR indoor localization algorithm combined with online sequential extreme learning machine (OS-ELM) that can adapt to localization environment dynamically and reduce the localization errors to a low scale and can predict the position of pedestrian regardless of holding postures.
Abstract: Smartphone-based pedestrian dead-reckoning (PDR) has become promising in indoor localization since it locates users with a smartphone only. However, existing PDR approaches are still facing the problem of accumulated localization errors due to low-cost noisy sensors and complicated human movements. This paper presents a novel PDR indoor localization algorithm combined with online sequential extreme learning machine (OS-ELM). By analyzing the process of PDR localization, this paper first formulates the process of PDR localization as an approximation function, and then, a sliding-window-based scheme is designed to preprocess the obtained inertial sensor data and thus to generate the feature dataset. At last, the OS-ELM-based PDR algorithm is proposed to address the localization problem of pedestrians. Due to the fact of universal approximation capability and extreme learning speed within OS-ELM, our algorithm can adapt to localization environment dynamically and reduce the localization errors to a low scale. In addition, by taking the movement habits of pedestrian into the process of extreme learning, our algorithm can predict the position of pedestrian regardless of holding postures. To evaluate the performance of the proposed algorithm, this paper implements OS-ELM-based PDR on a real android-based smartphone and compares it with the state-of-the-art approaches. Extensive experiment results demonstrate the effectiveness of the proposed algorithm in various different postures and the practicability in indoor localization.

48 citations

Journal ArticleDOI
Yingbiao Yao1, Pan Lei1, Wei Fen1, Xiaorong Xu1, Xuesong Liang1, Xin Xu1 
TL;DR: A dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy and an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy.
Abstract: As an infrastructure-free positioning and navigation method, pedestrian dead reckoning (PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and stride length estimation (SLE) are two key components of PDR, and it is a challenging problem to apply SD and SLE to different walking patterns. Focusing on this problem, this paper proposes a robust SD and SLE method based on recognizing three walking patterns (i.e., Normal Walk, March in Place, and Quick Walk) using a smartphone. First, we propose a dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy. In particular, the proposed SD can accurately identify the starting and ending points of each step in the three walking patterns. Second, according to the extracted features of each step, a random forest algorithm with classification proofreading is used to recognize the three walking patterns. Finally, an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy. The experimental results show that, on average, the SD accuracy is about 97.9%, the recognition accuracy is about 98.4%, and the relative error of the estimated walking distance is about 3.0%, which outperforms those of the existing commonly used SD and SLE methods.

46 citations


Cites methods from "Novel method for stride length esti..."

  • ...In [35], an SLE method based on the wavelet transform is presented....

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References
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Journal ArticleDOI
TL;DR: In this paper, a general-purpose system to obtain the kinematics of gait in the sagittal plane based on body-mounted sensors was developed, consisting of four uniaxial seismic accelerometers and one rate gyroscope per body segment.

674 citations

01 Jan 2002
TL;DR: A general-purpose system to obtain the kinematics of gait in the sagittal plane based on body-mounted sensors was developed, which is accurate, inexpensive and portable and allow long-term recordings in clinical, sport and ergonomics settings.
Abstract: A general-purpose system to obtain the kinematics of gait in the sagittal plane based on body-mounted sensors was developed. It consisted of four uniaxial seismic accelerometers and one rate gyroscope per body segment. Tests were done with 10 young healthy volunteers, walking at five different speeds on a treadmill. In order to study the system’s accuracy, measurements were made with an optic, passive-marker system and the body-mounted system, simultaneously. In all the comparison cases, the curves obtained from the two systems were very close, showing root mean square errors representing o7% full range in 75% of the cases (overall mean 6.64%, standard deviation 4.13%) and high coefficients of multiple correlation in 100% of cases (overall mean 0.9812, standard deviation 0.02). Calibration of the body-mounted system is done against gravity. The body-mounted sensors do not hinder natural movement. The calculation algorithms are computationally demanding and only are applicable off-line. The body-mounted sensors are accurate, inexpensive and portable and allow long-term recordings in clinical, sport and ergonomics settings. r 2002 Elsevier Science Ltd. All rights reserved.

614 citations

Proceedings ArticleDOI
13 Oct 2009
TL;DR: This paper uses low-performance Micro-Electro-Mechanical inertial sensors attached to the foot of a person, and describes, implements and compares several of the most relevant algorithms for step detection, stride length, heading and position estimation.
Abstract: Human localization is a very valuable information for smart environments. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person, by detecting steps, estimating stride lengths and the directions of motion; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we use low-performance Micro-Electro-Mechanical (MEMS) inertial sensors attached to the foot of a person. This sensor has triaxial orthogonal accelerometers, gyroscopes and magnetometers. We describe, implement and compare several of the most relevant algorithms for step detection, stride length, heading and position estimation. The challenge using MEMS is to provide location estimations with enough accuracy and a limited drift. Several tests were conducted outdoors and indoors, and we found that the stride length estimation errors were about 1%. The positioning errors were almost always below 5% of the total travelled distance. The main source of positioning errors are the absolute orientation estimation.

551 citations


"Novel method for stride length esti..." refers methods or result in this paper

  • ...2) Modeling of human gait through the Weiberg algorithm [10], assuming that the stride length is proportional to the vertical displacement of the hip at every step....

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  • ...the zero velocity update at each step, the linear velocity is corrected computing the velocity error accumulated at each step and adjusting the velocity samples in the whole step using a weighted linear interpolation [10]....

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  • ...Through literature review, we have found seven main methods leveraging accelerometers for the estimation of stride length in straight line walking [8]-[10]....

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  • ...Comparing the methods described above, results in [10] show that the double integration with zero-velocity-update approach delivers the best accuracy (errors below 5%)....

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Journal ArticleDOI
TL;DR: This study investigated the possibility of using uni-axial gyroscopes to develop a simple portable gait analysis system by automatically resetting the system to re-initialise the angle in each gait cycle, and high-pass filtering.

518 citations


"Novel method for stride length esti..." refers background in this paper

  • ...Multiple studies use sensors attached to different parts of the body including waist, thigh, shank and foot [1]-[3], with special focus on the estimation of temporal features of gait including stride time and cadence....

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Journal ArticleDOI
TL;DR: In this paper, a step, stride and heading determination method based on pattern recognition is proposed from the analysis of the vertical and horizontal acceleration of the foot during one step of the walking.
Abstract: Recently, several simple and cost-effective pedestrian navigation systems (PNS) have been introduced. These systems utilized accelerometers and gyros in order to determine step, stride and heading. The performance of the PNS depends on not only the accuracy of the sensors but also the measurement processing methods. In most PNS, a vertical impact is measured to detect a step. A step is counted when the measured vertical impact is larger than the given threshold. The numbers of steps are miscounted sometimes since the vertical impacts are not correctly measured due to inclination of the foot. Because the stride is not constant and changes with speed, the step length parameter must be determined continuously during the walk in order to get the accurate travelled distance. Also, to get the accurate heading, it is required to overcome drawbacks of low grade gyro and magnetic compass. This paper proposes new step, stride and heading determination methods for the pedestrian navigation system: A new reliable step determination method based on pattern recognition is proposed from the analysis of the vertical and horizontal acceleration of the foot during one step of the walking. A simple and robust stride determination method is also obtained by analyzing the relationship between stride, step period and acceleration. Furthermore, a new integration method of gyroscope and magnetic compass gives a reliable heading. The walking test is preformed using the implemented system consists of a 1-axis accelerometer, a 1-axis gyroscope, a magnetic compass and 16-bit microprocessor. The results of walking test confirmed the proposed method.

388 citations


"Novel method for stride length esti..." refers methods in this paper

  • ...6) Another empirical approach for the estimation of stride length is described in [14], where the authors propose an experimental equation calibrated through several walking tests:...

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