Online Action Recognition for Human Risk Prediction with Anticipated
  Haptic Alert via Wearables
Cheng Guo Lorenzo Rapetti Kourosh Darvish Riccardo Grieco Francesco Draicchio Daniele Pucci
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.