Opportunistic Calibration Method for Walking Distance Estimation Using a Waist-Mounted Inertial Sensor

Pham, Thanh Tuan; Duong, Huu Toan; Suh, Young Soo

doi:10.1109/tim.2020.2982812

Cited by 2 publications

(4 citation statements)

References 27 publications

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“…The most popular walking distance estimation method is a pedestrian dead-reckoning (PDR) algorithm, which consists of two phases: step detection and step length estimation [6]. Many of the techniques that determine the distance have been developed to increase the efficiency and accuracy of localization, such as Pedestrian Dead Reckoning (PDR) [7]. By estimating the distance based on successive position displacements distance, or techniques that have relied on collected data from attached sensors to users' bodies, and some techniques depend on determining the distance in the ideal case in which the user walks at a normal speed and steady mode, which may cause the accuracy of the distance estimation to be affected due to irrelevant movements.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results show that the average absolute errors using the SVR model are 0.76 percent for straight-line corridors and 1.14 percent for rectangular paths. T. Pham et al [7] introduced an opportunistic calibration method for walking distance estimation using a waist-mounted inertial sensor; they tested their method using data that are automatically collected from daily normal walking.…”

Section: Introductionmentioning

confidence: 99%

“…Other publications about the same idea with a little improvement using a smoothing algorithm-based approach. Other methods [5], [7], [27], and [28] discussed similar methods using inertial sensors attached to the waist As well. All of them use external sensors to calculate the data of user movement.…”

Section: Introductionmentioning

confidence: 99%

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Distance Estimation Based on Step Frequency Using Accelerometer Data

Al-Naimat,

Harahsheh,

Chen

2024

Preprint

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In recent years, smartphone sensors have become one of the most important and easily available sensors to facilitate people's lives, especially in health care and positioning (indoor environments). However, the data coming from smartphone sensors can be distorted during the user’s movement such as irrelevant movements, walk mode, and speed of walking. This distortion (noise) impairs the estimated distance accuracy (accumulative error) which increases with increasing walking distance. In addition, the accuracy of the distance traveled is affected by the user's speed, as the speed affects the step length. This work proposes a novel approach for calculating step length in an indoor environment using data from a smartphone sensor. The contribution of this work is to determine the dynamic step length and to increase the accuracy of the distance traveled measurement, through the use of a step frequency that can provide the user's speed, and through the determination of the step error criterion that will be used in adjusting the distance traveled. The experimental results show that the proposed method achieves 99.11 percent accuracy over existing approaches when a pedestrian is walking in multiple mode changes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distance Estimation Based on Step Frequency Using Accelerometer Data

Al-Naimat,

Harahsheh,

Chen

2024

Preprint

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“…An IMU typically includes a combination of accelerometers, gyroscopes, and magnetometers. Together, these sensors provide valuable information about the wearer's physical activity and movement patterns, which can be used for healthcare applications, such as steps taken, distance traveled, calories burned, and fall detection [8][9][10]. Recently, photoplethysmographic sensors (PPG) have been widely implemented.…”

Section: Introductionmentioning

confidence: 99%

Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions

Kim

Baek

2023

Electronics

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Photoplethysmography (PPG) is an affordable and straightforward optical technique used to detect changes in blood volume within tissue microvascular beds. PPG technology has found widespread application in commercial medical devices, enabling measurements of oxygen saturation, blood pressure, and cardiac output; the assessment of autonomic nerve function; and the diagnosis of peripheral vascular disease. Recently, the growing demand for non-invasive, portable, cost-effective technology, along with advancements in small semiconductor components, has led to the integration of PPG into various wrist-worn wearable devices. Multiple sensor structures have been proposed and, through appropriate signal processing and algorithmic application, these wearable devices can measure a range of health indicators during daily life. This paper begins by addressing the market status of wrist-worn wearable devices, followed by an explanation of the fundamental principles underlying light operation and its interaction with living tissue for PPG measurements. Moving on to technological advancements, the paper addresses the analog front end for the measurement of the PPG signal, sensor configurations with multiple light emitters and receivers, the minimum sampling rate required for low-power systems, and the measurement of stress, sleep, blood pressure, blood glucose, and activity using PPG signals. Several challenges in the field are also identified, including selecting the appropriate wavelength for the PPG sensor’s light source, developing low-power interpolation methods to extract high-resolution inter-beat intervals at a low sampling rate, and exploring the measurement of physiological phenomena using multi-wavelength PPG signals simultaneously collected at the same location. Lastly, the paper presents future research directions, which encompass the development of new, reliable parameters specific to wearable PPG devices and conducting studies in real-world scenarios, such as 24-h long-term measurements.

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Opportunistic Calibration Method for Walking Distance Estimation Using a Waist-Mounted Inertial Sensor

Cited by 2 publications

References 27 publications

Distance Estimation Based on Step Frequency Using Accelerometer Data

Distance Estimation Based on Step Frequency Using Accelerometer Data

Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions

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