Inertial Sensor Algorithm to Estimate Walk Distance

Shah, Vrutangkumar V.; Curtze, Carolin; Sowalsky, Kristen; Arpan, Ishu; Mancini, Martina; Carlson‐Kuhta, Patricia; El‐Gohary, Mahmoud; Horak, Fay B.; McNames, James

doi:10.3390/s22031077

Cited by 11 publications

(9 citation statements)

References 47 publications

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“…Between-study differences in procedures, populations and reference methods make comparisons between studies problematic. However, the study by Shah et al [23] evaluated a similar type of technology to that assessed in the current study, used analogous age-based inclusion criteria, and had a comparable sample size. The results of Shah et al demonstrated that the commercially available IMU being tested had a MAE of 19.77 m for 6MWTs conducted on a 15-m track and an MAE of 18.36 m when the tests were conducted on a 20-m track.…”

Section: Discussionmentioning

confidence: 99%

“…Given the importance and widespread use of the 6MWT in clinical practice, there has been great interest in proposing wearable devices or smartphone applications as potential solutions for enhancing this assessment. Indeed, the precision of several potential solutions for enhancing the 6MWD has been assessed in previous studies, including evaluations of a commercially available wearable inertial measurement unit (IMU) [23], the iPhone's built-in algorithm [24], purpose-built smartphone-based algorithms [25][26][27] and a custom machine-learning algorithm [28]. While some of these studies yielded good results, use of several of these applications had the disadvantage of requiring a priori information such as the patient's height [25] or the course length [26,27], or pre-calibration for each subject being tested [28].…”

Section: Introductionmentioning

confidence: 99%

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Validation of the Six-Minute Walking Distance Measured by FeetMe® Insoles

Mostovov¹,

Jacobs²,

Farid³

et al. 2023

Preprint

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The six-minute walk test (6MWT) is widely used to assess functional capacity in patients with various diseases. Use of wearable devices can make this test more accurate and easier to administer, and may even enhance it by providing additional information. The purpose of this study was to evaluate the validity and reliability of FeetMe® insoles for assessing the total six-minute walking distance (6MWD) by comparing the FeetMe® estimates to those obtained by a rater and to the ground truth measured with a surveyor’s wheel. Data were analyzed from healthy volunteers who performed the 6MWT on 10-m and 30-m tracks while wearing FeetMe® insoles (n = 32), and being simultaneously assessed by a rater (n = 33) and followed by an investigator with a surveyor’s wheel. The mean average error (MAE) of the estimates was below 13 m on both tracks for FeetMe®, whereas it ranged from 16.24 m to 38.88 m on the 30-m and 10-m tracks for the rater. Thus, the FeetMe® insoles provided a more precise estimate than the rater, and the precision of the FeetMe® estimates did not vary according to the track length. We conclude that the FeetMe insoles are a reliable and accurate solution for measuring the 6MWD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of the Six-Minute Walking Distance Measured by FeetMe® Insoles

Mostovov¹,

Jacobs²,

Farid³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Also within the context of the 6MWT, studies propose using smartphones IMUs [ 9 , 16 ]. For example, Mak et al [ 8 ] conducted an extensive study concerning in-clinic and home-based 6MWT using inertial sensors embedded in iPhone 7 smartphones, Apple.Their findings are promising, but rely only on the step count, as measuring distance from IMU is not easy to achieve reliably.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT

Caramaschi,

Olsson,

Orchard

et al. 2024

Sensors

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As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users’ previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

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“…Measuring the total distance walked is crucial in tracking physical fitness and health status, but current methods can be subjective and time-consuming. Shah et al [8] developed an algorithm, based on inertial sensors, to estimate the total distance walked by older adults and patients with multiple sclerosis. The algorithm calculates the distance travelled during each step and the total distance walked as the sum of walk distances for each stride, including turns.…”

Section: Contributionsmentioning

confidence: 99%