Feature Selection for Machine Learning Based Step Length Estimation Algorithms

Vandermeeren, Stef; Bruneel, Herwig; Steendam, Heidi

doi:10.3390/s20030778

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…The experiment results implied that the camerabased method was a promising way to detect all steps when the user was moving slowly, especially in an indoor environment. Recently, the researchers in [21] proposed a machinelearning-based step length estimation algorithm with the use of cameras and smartphones. This research considered a systematic feature selection algorithm to determine the choice of user-specific parameters from a large collection.…”

Section: Related Workmentioning

confidence: 99%

Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

Yang

Tran

Safaei

2022

Sensors

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In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively.

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Section: Related Workmentioning

confidence: 99%

Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios

Yang

Tran

Safaei

2022

Sensors

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“…The dense layer branch input was built with a feature vector extracted from step/stride time series. Features comprise the mean, variance, root mean square (RMS), range, the sum of all values, the step time in seconds and foot size in centimetres (divided by a constant 30) [32,33]. The foot size was estimated from the sensing walkway data and used as a proxy for the participant's height since this latter demographic variable was not recorded in the study.…”

Section: Step/stride Length Dnn Modelmentioning

confidence: 99%

An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease

Peraza

Kinnunen

McNaney

et al. 2021

Sensors

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The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.

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“…Song et al demonstrated the advantages of parameterized blackbox modeling over traditional human gait models with an ANN-based approach to predict walking or running speed over a large range of speeds [4]. Vandermeeren et al extracted a variety of different features to predict stride length from a smartphone accelerometer using multiple machine learning approaches [5]. Classical machine learning techniques for stride length estimation are still ongoing research, but they require signal preprocessing and specific feature extraction.…”

Section: Introductionmentioning

confidence: 99%