Step detection algorithm for accurate distance estimation using dynamic step length

Abadleh, Ahmad; Al-Hawari, Eshraq; Alkafaween, Esra’a; Alsawalqah, Hamad

doi:10.1109/mdm.2017.52

Cited by 43 publications

(27 citation statements)

References 9 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Conventional step detector algorithms use accelerometer sensor data to detect steps. Ahmad et al [31] studied the fundamental analysis of stepdetection algorithms and discussed their performance based on dynamic step lengths. Ho et al [32] reported an adaptive step length estimator.…”

Section: Related Workmentioning

confidence: 99%

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

2019

View full text Add to dashboard Cite

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 positionestimation 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. INDEX TERMS Indoor positioning system (IPS), pedestrian dead reckoning (PDR), heading estimation, indoor navigation, Android-based smartphone, quaternion, Kalman filter, sensor fusion.

show abstract

Section: Related Workmentioning

confidence: 99%

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

2019

View full text Add to dashboard Cite

show abstract

“…It integrated the acceleration experienced in three coordinate axes to give an overall acceleration. A Python program was developed to calculate the number of steps and walking speed from the accelerometer's raw data [22,23]. More detailed description is available in the supplementary material [see Additional file 1].…”

Section: Final Devicementioning

confidence: 99%

The development and validation of a prototype mobility tracker for assessing the life space mobility and activity participation of older adults

Ong

Tan²,

Tan

et al. 2020

BMC Geriatr

View full text Add to dashboard Cite

Background: There is increasing interest in examining the life space mobility and activity participation of older adults in the community using sensor technology. Objective data from these technologies may overcome the limitations of self-reported surveys especially in older adults with age-associated cognitive impairment. This paper describes the development and validation of a prototype hybrid mobility tracker for assessing life space mobility and out-of-home activities amongst 33 community-ambulant older adults in Singapore. Methods: A hybrid mobility tracker was developed by combining a passive Global Positioning System logger, triaxial accelerometer and radio-frequency identification. Objective measures of life space, derived from 1 week of tracking data using Geographic Information Systems, were the maximum Euclidean distance from home (Max Euclid) and the area of the minimum convex polygon surrounding all GPS waypoints (MCP area). Out-of-home activities were quantified by visually identifying the total number of activity nodes, or places where participants spent ≥5 min, from mobility tracks. Self-reported measure of life space in 4 weeks was obtained using the University of Alabama at Birmingham Study of Life Space Assessment (UAB-LSA) questionnaire. Self-reported out-ofhome activities were recorded daily in a travel diary for 1 week. Bivariate correlations were used to examine convergent validity between objective and subjective measures of life space and out-of-home activities. Results: The mean age of participants was 69.2 ± 7.1 years. The mean UAB-LSA total score was 79.1 ± 17.4. The median (range) Max Euclid was 2.44 km (0.26-7.50) per day, and the median (range) MCP area was 3.31 km 2 (0.03-34.23) per day. The UAB-LSA total score had good correlation with Max Euclid (r = 0.51, p = 0.002), and moderate correlation with MCP area (r = 0.46, p = 0.007). The median (range) total number of activity nodes measured by

show abstract

“…The algorithm's parameters have been optimized and applied to measured data from three different volunteers holding the smartphone at different positions. A new algorithm based on the smartphone sensor has been proposed in [11] to estimate the traveled distance by walking accurately. The algorithm counts the number of peaks in the measured signal by the smartphone's accelerometer.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, Acceleration values will be determined like human walking or running regardless of the accelerometer's orientation. The overall acceleration value is calculated as the square root of three acceleration values, as expressed by equation(2) as in[11].…”

mentioning

confidence: 99%

Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Muhsen¹,

Al-Amaydeh²,

Al-Hamlan³

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

This paper aims to design an accurate and robust algorithm for counting the steps based on the smartphone's accelerometer for indoor applications. Different daily activities have been considered in the experimental scenarios, including normal walking with various smartphone positions and running activities. The detection process's accuracy is ensured by setting an appropriate segregation approach and defining a fixed threshold for each experiment. The algorithm generates a new envelope signal that mimics the collected steps signal without the vibrations and noise elements to avoid miscounting the exact steps. The followed approach results have been compared with the pedometer applications on the android platform and HUAWEI Watch GT-731. The proposed algorithm showed improved detection accuracy in contrast to the other literature approaches. The obtained accuracy was around 99% for the standard walking scenario and approximately 97% for the running activity scenario.

show abstract

Step detection algorithm for accurate distance estimation using dynamic step length

Cited by 43 publications

References 9 publications

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

The development and validation of a prototype mobility tracker for assessing the life space mobility and activity participation of older adults

Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Contact Info

Product

Resources

About