Using accelerations of single inertial measurement units to determine the intensity level of light-moderate-vigorous physical activities: Technical and mathematical considerations

Marin, F.; Lepetit, Kevin; Fradet, Laetitia; Hansen, Clint; Mansour, Khalil Ben

doi:10.1016/j.jbiomech.2020.109834

Cited by 12 publications

(13 citation statements)

References 44 publications

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“…The major features are defined in terms of ENMO, mean acceleration correlated with activity intensity such as walking versus standing. These include MPD and MAD, Mean Power Deviation and Mean Amplitude Deviation of accelerometer magnitude signals [37].…”

Section: Methodsmentioning

confidence: 99%

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Population Analysis of Mortality Risk: Predictive Models Using Motion Sensors for 100,000 Participants in the Uk Biobank National Cohort

Zhou

Zhu

Ung

et al. 2022

Preprint

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We have developed novel technology for health monitoring, which inputs motion sensors to predictive models of health status. We have validated these models in clinical experiments with carried smartphones, using only their embedded accelerometers. Using smartphones as passive monitors for population measurement is critically important for health equity, since they are ubiquitous in high-income countries already and will become ubiquitous in low-income countries in the near future. Our study simulates smartphones by using accelerometers as sensor input.We analyzed 100,000 participants in UK Biobank who wore activity monitors with motion sensors for 1 week. This national cohort is demographically representative of the UK population, and this dataset represents the largest such available sensor record. We performed population analysis using walking intensity, with participants whose motion during normal activities included daily living equivalent of timed walk tests. We extract continuous features from sensor data, for input to survival analysis for predictive models of mortality risk.Simulating population monitoring, we validated predictive models using only sensors and demographics. This resulted in C-index of 0.76 for 1-year risk decreasing to 0.73 for 5-year. A minimum set of sensor features achieves similar C-index with 0.72 for 5-year risk, which is similar accuracy to previous studies using methods not achievable with phone sensors. The minimum model uses average acceleration, which has predictive value independent of demographics of age and sex, as does the physical measure of gait speed. Our digital health methods achieve the same accuracy as activity monitors measuring total activity, despite using only walking sessions as sensor input, orders of magnitude less than existing methods.AUTHOR SUMMARYSupporting healthcare infrastructure requires screening national populations with passive monitors. That is, looking for health problems without intruding into daily living. Digital health offers potential solutions if sensor devices of adequate accuracy for predictive models are already widely deployed. The only such current devices are cheap phones, smartphone devices with embedded sensors. This limits the measures to motion sensors when the phones are carried during normal activities. So measuring walking intensity is possible, but total activity is not.Our study simulates smartphone sensors to predict mortality risk in the largest national cohort with sensor records, the demographically representative UK Biobank. Death is the most definite outcome, accurate death records are available for 100,000 participants who wore sensor devices some five years ago. We analyzed this dataset to extract walking sessions during daily living, then used these to predict mortality risk. The accuracy achieved was similar to activity monitors measuring total activity and even to physical measures such as gait speed during observed walks. Our scalable methods offer a potential pathway towards national screening for health status.

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Section: Methodsmentioning

confidence: 99%

“…These include MPD and MAD, Mean Power Deviation and Mean Amplitude Deviation of accelerometer magnitude signals [37].…”

Section: Mortality Predictors: Traditional Categorical and Accelerome...mentioning

confidence: 99%

Population Analysis of Mortality Risk: Predictive Models Using Motion Sensors for 100,000 Participants in the Uk Biobank National Cohort

Zhou

Zhu

Ung

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Full formulas for sensor features are given in S2 Table . The major features are defined in terms of ENMO, mean acceleration correlated with activity intensity such as walking versus standing. These include MPD and MAD, Mean Power Deviation and Mean Amplitude Deviation of accelerometer magnitude signals [37].…”

Section: Mortality Predictors: Traditional Categorical and Accelerome...mentioning

confidence: 99%

Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants

Zhou

Zhu²,

Ung³

et al. 2022

PLOS Digit Health

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Many studies have utilized physical activity for predicting mortality risk, using measures such as participant walk tests and self-reported walking pace. The rise of passive monitors to measure participant activity without requiring specific actions opens the possibility for population level analysis. We have developed novel technology for this predictive health monitoring, using limited sensor inputs. In previous studies, we validated these models in clinical experiments with carried smartphones, using only their embedded accelerometers as motion sensors. Using smartphones as passive monitors for population measurement is critically important for health equity, since they are already ubiquitous in high-income countries and increasingly common in low-income countries. Our current study simulates smartphone data by extracting walking window inputs from wrist worn sensors. To analyze a population at national scale, we studied 100,000 participants in the UK Biobank who wore activity monitors with motion sensors for 1 week. This national cohort is demographically representative of the UK population, and this dataset represents the largest such available sensor record. We characterized participant motion during normal activities, including daily living equivalent of timed walk tests. We then compute walking intensity from sensor data, as input to survival analysis. Simulating passive smartphone monitoring, we validated predictive models using only sensors and demographics. This resulted in C-index of 0.76 for 1-year risk decreasing to 0.73 for 5-year. A minimum set of sensor features achieves C-index of 0.72 for 5-year risk, which is similar accuracy to other studies using methods not achievable with smartphone sensors. The smallest minimum model uses average acceleration, which has predictive value independent of demographics of age and sex, similar to physical measures of gait speed. Our results show passive measures with motion sensors can achieve similar accuracy to active measures of gait speed and walk pace, which utilize physical walk tests and self-reported questionnaires.

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“…A correct analysis influences the execution of a refined technique without loss of energy and with the maximum performance of the athlete. In addition, in terms of medicine, they could even anticipate possible injuries that the athlete may suffer due to muscle overload or malpractice [4].…”

Section: Introductionmentioning

confidence: 99%

Biomechanics of the Upper Limbs: A Review in the Sports Combat Ambit Highlighting Wearable Sensors

Ortega¹,

Godoy²,

Wasik³

et al. 2022

Sensors

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Over time, inertial sensors have become an essential ally in the biomechanical field for current researchers. Their miniaturization coupled with their ever-improvement make them ideal for certain applications such as wireless monitoring or measurement of biomechanical variables. Therefore, in this article, a compendium of their use is presented to obtain biomechanical variables such as velocity, acceleration, and power, with a focus on combat sports such as included box, karate, and Taekwondo, among others. A thorough search has been made through a couple of databases, including MDPI, Elsevier, IEEE Publisher, and Taylor & Francis, to highlight some. Research data not older than 20 years have been collected, tabulated, and classified for interpretation. Finally, this work provides a broad view of the use of wearable devices and demonstrates the importance of using inertial sensors to obtain and complement biomechanical measurements on the upper extremities of the human body.

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Using accelerations of single inertial measurement units to determine the intensity level of light-moderate-vigorous physical activities: Technical and mathematical considerations

Cited by 12 publications

References 44 publications

Population Analysis of Mortality Risk: Predictive Models Using Motion Sensors for 100,000 Participants in the Uk Biobank National Cohort

Population Analysis of Mortality Risk: Predictive Models Using Motion Sensors for 100,000 Participants in the Uk Biobank National Cohort

Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants

Biomechanics of the Upper Limbs: A Review in the Sports Combat Ambit Highlighting Wearable Sensors

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