Exposures to select risk factors can be estimated from a continuous stream of inertial sensor measurements during a variety of lifting-lowering tasks

Lim, Sol

doi:10.1080/00140139.2024.2343949

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2024

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Cited by 3 publications

References 41 publications

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Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

Taori,

Lim

2024

Applied Ergonomics

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Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

Taori,

Lim

2024

Applied Ergonomics

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Classification Algorithms Trained on Simple (Symmetric) Lifting Data Perform Poorly in Predicting Hand Loads during Complex (Free-Dynamic) Lifting Tasks

Taori,

Lim

2024

Preprint

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The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements

Hollinger,

Schall,

Chen

et al. 2024

Sensors

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The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

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Exposures to select risk factors can be estimated from a continuous stream of inertial sensor measurements during a variety of lifting-lowering tasks

Cited by 3 publications

References 41 publications

Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

Classification Algorithms Trained on Simple (Symmetric) Lifting Data Perform Poorly in Predicting Hand Loads during Complex (Free-Dynamic) Lifting Tasks

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements

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