Gait retraining: out of the lab and onto the streets with the benefit of wearables

Napier, Christopher J.; Esculier, Jean-François; Hunt, Michael A.

doi:10.1136/bjsports-2017-098637

Cited by 26 publications

(17 citation statements)

References 13 publications

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“…Furthermore, they may not reflect a runner's actual gait pattern while running in their usual environment [20]. Wearable sensors represent a potential low-cost solution to collect in-field data [21]. Accurate wearables could be used to monitor the ability and compliance of runners when gait modifications are recommended to reduce injury risk, return to running after an injury or improve performance [22].…”

mentioning

confidence: 99%

Validating Commercial Wearable Sensors for Running Gait Parameters Estimation

et al. 2020

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A growing number of people all over the world are running. Gathering in-field data with wearable sensors is attractive for runners, clinicians and coaches to improve running performance, avoid injury or return to running after an injury. However, it is yet to be proven that commercially available wearable sensors provide valid data. The objective of this study was to assess the validity of five wearable sensors (Moov Now TM , MilestonePod, RunScribe TM , TgForce and Zoi) to measure ground reaction force related metrics, step rate, foot strike pattern, and vertical displacement of the center of mass during running. Concurrent/criterion validity was assessed against a laboratory-based system using Pearson's correlation coefficients and ANOVAs. Step rate measurement provided by all wearable sensors was valid (all r > 0.96 and p < 0.001). Only Zoi provided valid vertical displacement of the center of mass (r = 0.81, p < 0.001); only TgForce provided meaningful estimates of instantaneous vertical loading rate (r = 0.76, p < 0.001); only MilestonePod could discriminate between a rear-, mid-and fore-foot strike pattern during running (p < 0.001). None of the wearable sensors was valid for estimating peak braking force. In conclusion, only a few metrics provided by these commercially available wearable sensors are valid. Potential buyers should therefore be aware of such limitations when monitoring running gait variables.

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confidence: 99%

Validating Commercial Wearable Sensors for Running Gait Parameters Estimation

et al. 2020

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“…It could even be argued that individual practice between retraining sessions with clinician feedback—like we used in our study—would be justified to enhance transfer compared with discharging runners without further feedback after eight sessions of treadmill retraining. However, this remains purely hypothetical until studies use wearable devices to assess the retention of gait modifications during community runs 7. While we cannot ascertain—similar to previous studies—that the newly acquired gait modifications were reproduced out of the lab, participants in our study effectively reduced vertical and patellofemoral joint loading rates after the intervention.…”

Section: Retraining Schedulementioning

confidence: 63%

Response to: ’Optimising the efficacy of gait retraining'

2018

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“…Flexible textile strain sensors provide a convenient approach to measuring biomechanical changes resulting from fatigue. Gold standard methods, including force plate analysis or optical motion capture systems, are challenging for running applications because of the restriction of data collection to lab settings [ 51 ]. Other methods that allow for collection outside of the lab, including sEMG, can be plagued with poor signal quality from the difficulty of maintaining appropriate contact with the skin during prolonged vigorous movements.…”

Section: Discussionmentioning

confidence: 99%

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Gholami

Napier

Patiño

et al. 2020

Sensors

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Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.

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Gait retraining: out of the lab and onto the streets with the benefit of wearables

Cited by 26 publications

References 13 publications

Validating Commercial Wearable Sensors for Running Gait Parameters Estimation

Validating Commercial Wearable Sensors for Running Gait Parameters Estimation

Response to: ’Optimising the efficacy of gait retraining'

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

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