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KKT-ZSMF STS MOVEMENT MONITORING DATASET
- Citation Author(s):
- Submitted by:
- Jie Zuo
- Last updated:
- Mon, 07/08/2024 - 15:58
- DOI:
- 10.21227/5zzt-3y49
- License:
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- Keywords:
Abstract
As the global aging population continues to grow, there has been a significant increase in the number of fall-related injuries among the elderly, primarily due to reduced muscle strength and balance control, especially during sit-to-stand (STS) movements. Intelligent wearable robots have the potential to provide fall prevention assistance to individuals at risk, but an accurate and timely assessment of human movement stability is essential. This paper presents a fall prediction algorithm for STS movements based on the Karush-Kuhn-Tucker (KKT) optimized zonotope set-membership filter (KKT-ZSMF), enabling real-time assessment of human stability. To quantify the feasible stability region of human STS movement, a mathematical model is proposed based on dynamic stability theory. Additionally, an online fall-prediction approach is developed, utilizing the zonotope set-membership filter to iteratively update the set that represents the instantaneous stability region. The approach incorporates a KKT optimization algorithm to compute the optimal convex hull, thereby enhancing the accuracy and efficiency of the set-membership filter. Experimental validation is conducted with the participation of eight healthy subjects, comparing the performance of the proposed KKT-ZSMF algorithm with other relevant methods. The results confirm the accuracy and real-time performance of the KKT-ZSMF algorithm for predicting human STS movement stability, achieving an overall prediction accuracy of 93.49% and a runtime of no more than 7.91 ms. These findings demonstrate the suitability of the algorithm for fall prevention assistance in daily activities.
We recruited eight healthy subjects to test the real-time performance of the proposed algorithm for fall prediction during STS, as it is difficult and unsafe to make elderly subjects fall repeatedly. Ethical approval (IORG0003571) was obtained, and informed consent was obtained from all participants. Table I provides the basic information about the subjects. Each subject completes three sets of trials: (1) normal STS without external disturbance, (2) STS with an external forward perturbation to simulate the subject's forward stepping condition, and (3) STS with an external backward perturbation to simulate the subject's backward sitting condition. Each set of trials was repeated four times. A successful STS case is defined as the subject standing steadily without moving their feet, otherwise it is considered a failed case.
The experimental setup includes the following equipment: (1) Optitrack motion capture system (NaturalPoint, USA) with markers placed on the subject's shoulder joints, hip joints, knee joints, ankle joints, thigh centers, calf centers, and upper torso center to capture kinematic data for computing the COM using the double inverted pendulum model; (2) other equipment such as a chair, spring tension meter, and rope for applying external disturbances.
The experimental preparation involves the following steps: (1) recording a demonstration video of the STS experiment and showing it to the subjects; (2) preparing the motion capture system and verifying the effectiveness of marker placement; (3) adjusting the chair height to ensure the subject's femur is parallel to the ground; (4) allowing the subject to practice natural standing up from the chair and marking a comfortable landing area; (5) testing the pulling force experienced by subjects during the STS movement when subjected to external disturbances causing forward stepping or backward sitting.
The experimental procedure involves the following steps: (1) subjects starting in a seated position with their feet placed naturally on the marked landing area; (2) following the demonstration video to perform the STS movement while being simultaneously recorded by the Optitrack; (3) applying appropriate external disturbances during the STS process to induce forward stepping or backward sitting; (4) running the real-time fall-prediction program to record and compare the predicted results with the actual observations.
