# Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables

arXiv: https://arxiv.org/abs/2401.05365 · HTML: https://mlsanity.com/paper/2401.05365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Human Motion Prediction via Spatio-Temporal Inpainting](https://mlsanity.com/paper/1812.05478/) (2018)
- [Learning Human Motion Models for Long-term Predictions](https://mlsanity.com/paper/1704.02827/) (2017)
- [Real-time Robot-assisted Ergonomics](https://mlsanity.com/paper/1805.06270/) (2018)
- [Simultaneous Action Recognition and Human Whole-Body Motion and Dynamics Prediction from Wearable Sensors](https://mlsanity.com/paper/2303.07655/) (2023)
- [Model-Based Real-Time Motion Tracking using Dynamical Inverse Kinematics on SO(3)](https://mlsanity.com/paper/1909.07669/) (2019)
- [Whole-Body Human Kinematics Estimation using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter](https://mlsanity.com/paper/2205.07835/) (2022)

## Abstract (arXiv)

This paper proposes a framework that combines online human state estimation, action recognition and motion prediction to enable early assessment and prevention of worker biomechanical risk during lifting tasks. The framework leverages the NIOSH index to perform online risk assessment, thus fitting real-time applications. In particular, the human state is retrieved via inverse kinematics/dynamics algorithms from wearable sensor data. Human action recognition and motion prediction are achieved by implementing an LSTM-based Guided Mixture of Experts architecture, which is trained offline and inferred online. With the recognized actions, a single lifting activity is divided into a series of continuous movements and the Revised NIOSH Lifting Equation can be applied for risk assessment. Moreover, the predicted motions enable anticipation of future risks. A haptic actuator, embedded in the wearable system, can alert the subject of potential risk, acting as an active prevention device. The performance of the proposed framework is validated by executing real lifting tasks, while the subject is equipped with the iFeel wearable system.