This paper presents a novel framework that aims to address the important issue of worker biomechanical risk during lifting tasks. By combining online human state estimation, action recognition, and motion prediction, this framework enables early assessment and prevention of potential risks. This is achieved by leveraging the NIOSH index for online risk assessment, making it suitable for real-time applications.

The framework begins by retrieving the human state using inverse kinematics/dynamics algorithms from wearable sensor data. This allows for a detailed understanding of the worker’s posture and movements during the lifting task. Importantly, the human action recognition and motion prediction components of the framework use an LSTM-based Guided Mixture of Experts architecture, which is trained offline and can be inferred online.

By accurately recognizing the actions being performed by the worker, the framework is able to break down a single lifting activity into a series of continuous movements. This is crucial for applying the Revised NIOSH Lifting Equation, which provides a standardized method for assessing risk. By quantifying the biomechanical stress on the worker’s body, the framework can provide valuable insights into potential risks.

In addition to assessing risk during the lifting task, the framework also enables the anticipation of future risks through motion prediction. By analyzing the historical data and understanding the patterns and trends in the worker’s movements, this framework can provide early warnings of potential risks that may arise in the future. This proactive approach to risk prevention can significantly enhance worker safety.

An interesting aspect of this framework is the inclusion of a haptic actuator embedded in the wearable system. This actuator can alert the worker of potential risks in real-time, acting as an active prevention device. By providing tactile feedback, the actuator can help the worker make adjustments to their posture or technique to minimize the risk of injury.

To validate the performance of the proposed framework, real lifting tasks were executed while the subjects were equipped with the iFeel wearable system. This allowed for the collection of real-world data and enabled a thorough evaluation of the framework’s effectiveness.

This framework has significant potential in improving worker safety during lifting tasks. By combining online human state estimation, action recognition, and motion prediction, this framework provides a comprehensive solution for assessing and preventing biomechanical risks. The integration of a haptic actuator further enhances the capabilities of this system. Future research could focus on refining the accuracy of the human state estimation, exploring additional risk assessment methods, and evaluating the performance of this framework in different workplace scenarios.

Read the original article