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Classifying diverse manual material handling tasks using a single wearable sensor.

Micaela Porta¹, Sunwook Kim², Massimiliano Pau¹

¹Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Italy.

Applied Ergonomics

|February 20, 2021

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A single inertial measurement unit (IMU) can accurately classify manual material handling (MMH) tasks and estimate their duration and frequency. This user-friendly approach simplifies workplace physical demand quantification with minimal benefit from additional sensors.

Area of Science:

Occupational health and ergonomics
Wearable sensor technology
Human activity recognition

Background:

Inertial measurement units (IMUs) are increasingly used for physical activity monitoring in various contexts.
Quantifying physical demands in workplaces requires user-friendly methods.
Existing IMU applications often involve multiple sensors, limiting practicality.

Purpose of the Study:

To evaluate the efficacy of a single IMU for classifying manual material handling (MMH) tasks.
To assess the ability of a single IMU to estimate MMH task duration and frequency.
To compare the performance of single IMU configurations with multi-IMU setups.

Main Methods:

Utilized a bidirectional long short-term memory (LSTM) network for task classification.

Keywords:

Exposure assessment Manual material handling Task classification

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Collected data from single IMUs placed on various body locations.

Compared classification accuracy across single, dual, triple, and 17-IMU configurations.

Evaluated performance in identifying task type, duration, and frequency.

Main Results:

A single IMU achieved high classification accuracy (median >97%) for MMH tasks.
Single IMUs effectively estimated task duration and frequency.
Adding more sensors provided limited performance improvements.
Sensor placement on different body parts yielded comparable results.
Classification accuracy was lower for push/pull tasks compared to other MMH tasks.

Conclusions:

A single IMU is a viable and user-friendly tool for quantifying MMH tasks in occupational settings.
The effectiveness of IMU-based activity monitoring can be achieved with minimal sensor configurations.
Further research may be needed to optimize classification for specific tasks like push/pull.