Manual Material Handling (MMH) activities—such as lifting, carrying, and lowering—are routine in labor-intensive sectors like manufacturing, logistics, and construction. Although necessary, they are also some of the biggest causes of on-the-job injury, specifically to the lower back. As sectors strive for safer, more productive working environments, the function of Human Activity Recognition (HAR) algorithms in tracking and assessing MMH activities is increasingly becoming a big hit.
The Rise of Wearable Technology and HAR
With the development of wearable sensor technologies—particularly Inertial Measurement Units (IMUs)—researchers and engineers are now equipped to monitor real-time motion data with high precision and low intrusiveness. IMUs monitor orientation, acceleration, and angular velocity, providing a rich picture of worker movement during MMH tasks.
However, data gathering is only half of the equation. The true challenge is correctly interpreting that data with advanced algorithms capable of telling different activities apart (e.g., asymmetrical and symmetrical lifting) and detecting variations in form that are likely to be problematic.
The Role of Machine Learning in HAR
Machine learning models are at the center of HAR systems. The models learn from acquired sensor data to detect patterns and identify activities appropriately. In general, two types of models are employed:
Classical Machine Learning Algorithms include decision trees, support vector machines (SVMs), and k-nearest neighbors (k-NN). They are effective when trained on well-labeled datasets and perform reliably in controlled environments.
Deep Learning Approaches: More recently, neural networks, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have proved more effective at HAR because they can learn intricate, time-varying features from data streams.
Critical Factors Affecting Algorithm Performance
Based on recent research, such as Sochopoulos et al. (2025), the success of HAR algorithms in MMH systems relies on several factors that are interconnected:
Sensor Placement and Quantity: Multiple sensors strategically positioned enhance detection quality, particularly for complicated or delicate movements.
Time Window Selection: The length of data segments considered for analysis may dramatically impact the system’s ability to identify activity transitions and subtleties.
Classifier Architecture: The selected algorithm’s temporal sensitivity and computational complexity may influence performance, particularly when implementing models on embedded platforms such as wearable exoskeletons.
These findings are significant in the design of occupational exoskeletons—wearable robots intended to mitigate physical effort. By incorporating HAR algorithms, such systems can learn to adapt to user activity and provide focused support, improving safety and usability.
Challenges and the Path Forward
Despite impressive progress, several challenges remain:
Generalization Across Users: Body type variability, movement patterns, and work environment make it challenging to create one-size-fits-all models.
Real-Time Constraints: HAR systems need to run in real time for workplace applications, requiring efficient algorithms that can be executed on low-power hardware.
Activity Diversity: Most available datasets are centered on simple or symmetrical activities. Broadening the scope to cover a wider variety of realistic workplace activities will be essential to growing the applicability of HAR solutions.
Conclusion
HAR algorithms provide a revolutionary opportunity to improve occupational safety, particularly in areas where manual handling is unavoidable. With sensor technology and machine learning advances, these systems will become more accurate, adaptive, and part of routine industrial workflows.
The way forward is technical improvement and human-centered design—making systems as intuitive and flexible as they are smart.
For more detailed insights into recent developments in this area, you can refer to the full research study here:
Human Activity Recognition Algorithms for Manual Material Handling Activities (Nature, 2025)
