A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

The fatigue due to repetitive and physically challenging jobs may result in workers’ poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects’ fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue (≥17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals’ gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators’ fatigue level, which may subsequently increase the workers’ performance and reduce the risk of WMSDs.

• Publication year

  2019

• Venue

  bioRxiv

• Publication date

  2019-10-01

• Fields of study

  Biology, Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/WNYIPW.2019.8923100
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A machine learning approach to detect changes in gait parameters following a fatiguing occupational task

  2018influential reference
• Sharif-Human movement instrumentation system (SHARIF-HMIS): Development and validation.

  2018cited by this paper
• Vertical ground reaction force spectral analysis for fatigue assessment

  2017cited by this paper
• Impact of task design on task performance and injury risk: case study of a simulated drilling task

  2017cited by this paper
• Binary classification of running fatigue using a single inertial measurement unit

  2017influential reference
• A novel approach to spinal 3-D kinematic assessment using inertial sensors: Towards effective quantitative evaluation of low back pain in clinical settings

  2017cited by this paper
• Cycle time influences the development of muscle fatigue at low to moderate levels of intermittent muscle contraction.

  2016cited by this paper
• Localized Muscle Fatigue: Theoretical and Practical Aspects in Occupational Environments

  2016cited by this paper
• Prevention of Work-Related Musculoskeletal Disorders

  2014influential reference
• Ergonomic evaluation of a wearable assistive device for overhead work

  2014cited by this paper
• Support vector machines for automated gait classification

  2005cited by this paper
• Test-retest reliability of trunk accelerometric gait analysis.

  2004cited by this paper
• Gait event detection for FES using accelerometers and supervised machine learning.

  2000cited by this paper
• Psychophysical bases of perceived exertion.

  1982cited by this paper
• An electromyographic index for localized muscle fatigue.

  1977cited by this paper

• Real-World Classification of Student Stress and Fatigue Using Wearable PPG Recordings

  2026cites this paper
• Deep Learning-Based Fatigue Monitoring in Natural Environments: Multi-Level Fatigue State Classification

  2025cites this paper
• Kinematic indicators of physical fatigue during walking: A systematic review of assessment parameters and protocols.

  2025cites this paper
• Physical Fatigue Evaluation Using Muscle Modeling

  2025cites this paper
• Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors

  2024cites this paper
• Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

  2023cites this paper
• Small Data, Big Challenges: Pitfalls and Strategies for Machine Learning in Fatigue Detection

  2023cites this paper
• Inertial Measurement Units and Partial Least Square Regression to Predict Perceived Exertion During Repetitive Fatiguing Piano Tasks

  2023cites this paper
• Worker's physical fatigue classification using neural networks

  2022cites this paper
• Real-time forecasting of exercise-induced fatigue from wearable sensors

  2022cites this paper
• Fatigue Monitoring Through Wearables: A State-of-the-Art Review

  2021cites this paper
• Model-based data augmentation for user-independent fatigue estimation

  2021cites this paper
• A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate

  2021cites this paper
• Smartphone-based human fatigue level detection using machine learning approaches

  2021cites this paper
• A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors.

  2020cites this paper
• Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study

  2020cites this paper
• Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach

  2020cites this paper
• Design of Fatigue Driving Detection Algorithm Based on Image Processing

  2020cites this paper
• Personalizing 3D Free-Hand Input for Intuitive Smartphone Augmented Reality Interactions

  2020cites this paper
• Smartphone-based Human Fatigue Detection in an Industrial Environment Using Gait Analysis

  2019cites this paper