The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds

Abstract

Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms.

Three dimensional analysis of hip joint reaction force using Q Hip Force (AQHF) software: Implication as a diagnostic tool

“…Comparable results were obtained by another study establishing that the the step length of females decreased compared to the step length of males during ramp walking. This produced a reduction in the friction demand of females hence minimizing the joint compression forces of their lower extremities during the stance phase of walking [ 24 ]. Also, a short stride length probably counteracts the higher friction demand that would otherwise be needed at heel strike in upslope walking [ 25 ].…”

Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors

“…Existing datasets of IMU-style recordings were often designed for particular applications, such as patient control or daily life monitoring. Thus, they differ in the measurement setup, see survey [ 27 ]. We can recognize two principal types of datasets for motion speed estimation: (i) shoe-mounted IMUs, e.g., [ 27 , 28 , 29 , 30 ], and (ii) smartphone based data with varying positions (backpack or pocket) [ 14 , 31 , 32 , 33 ].…”

“…Thus, they differ in the measurement setup, see survey [ 27 ]. We can recognize two principal types of datasets for motion speed estimation: (i) shoe-mounted IMUs, e.g., [ 27 , 28 , 29 , 30 ], and (ii) smartphone based data with varying positions (backpack or pocket) [ 14 , 31 , 32 , 33 ]. While the shoe mounted sensor is a traditional setup for feature-based methods, the deep-learning methods are predominantly trained on smartphone data in hand or pocket.…”

Smart and Sentient Retail High Streets

“…Significant characteristics of pedestrian motion can be derived from a suite of sensors that are routinely available on the phones and watches that people carry while mobile, as well as via fitness trackers. A variety of features of motion (speed, periods of rest, effort, cadence, activity type) can be resolved by analyzing the data generated by inertial measurement units (IMUs) [125,[128][129][130][131]. These insights are available chiefly from accelerometer (which measures acceleration as the derivative of velocity) and gyroscope (which measures angular velocity as the derivative of orientation) data.…”