Trajectory and Sway Prediction Towards Fall Prevention

@article{Wang2022TrajectoryAS,
  title={Trajectory and Sway Prediction Towards Fall Prevention},
  author={Weizhuo Wang and Michael Raitor and Steve Collins and C. Karen Liu and Monroe Kennedy},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.11886}
}
Falls are the leading cause of fatal and non-fatal injuries, particularly for older persons. Imbalance can result from the body's internal causes (illness), or external causes (active or passive perturbation). Active perturbation results from applying an external force to a person, while passive perturbation results from human motion interacting with a static obstacle. This work proposes a metric that allows for the monitoring of the person's torso and its correlation to active and passive… 
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References

SHOWING 1-10 OF 29 REFERENCES

Bump’em: an Open-Source, Bump-Emulation System for Studying Human Balance and Gait

A robotic, rope-driven system capable of rendering bumps and force-fields at a person's pelvis in any direction in the transverse plane with forces up to 200 N, and a 90% rise time of as little as 44 ms, which is faster than a human’s ability to sense and respond to the force.

Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3-D Pedestrian Pose and Gait Prediction

A biomechanically inspired recurrent neural network that can predict the location and three-dimensional (3-D) articulated body pose of pedestrians in a global coordinate frame, given 3-D poses and locations estimated in prior frames with inaccuracy is proposed.

Gait stability in response to platform, belt, and sensory perturbations in young and older adults

To evaluate the potential of using contra-lateral sway and deceleration perturbations for fall risk identification by including both healthy and frail older adults, a large sample of young and older adults is suggested.

Recent Development in Human Motion and Gait Prediction

Recent development in predicting bipedal gait dynamics and the corresponding human motion trajectory is presented, and the potential opportunities in the field going forward are discussed.

Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies

This paper argues local representations on different body components should be learned separately and, based on such idea, proposes a network, Skeleton Network (SkelNet), for long-term human motion prediction, which is the first to use local structure representations for predicting the human motion.

A Reliable Fall Detection System Based on Wearable Sensor and Signal Magnitude Area for Elderly Residents

This work uses a waist- mounted tri-axial accelerometer to capture movement data of the human body, and proposes a fall detection method that uses the area under a signal magnitude curve to distinguish between falls and daily activities.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

The state-of-the-art in fall detection and prediction systems are reviewed and the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems are described.

Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector Machines

This study conducted laboratory experiments to determine how the number and location of wearable inertial sensors influences the accuracy of a machine learning algorithm in distinguishing near-falls from activities of daily living (ADLs).

The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait

Investigation of whether a single inertial measurement unit placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice.

A Study on Machine Learning Algorithms for Fall Detection and Movement Classification

  • Y. ChoiA. RalhanS. Ko
  • Computer Science
    2011 International Conference on Information Science and Applications
  • 2011
This paper responds to the challenge of classifying different movements as a part of a system designed to fulfill the need for a wearable device to collect data for fall and near-fall analysis.