Evaluating Parameters of the TUG Test Based on Data from IMU and UWB Sensors
Wireless and Mobile Computing, Networking and Communications (WiMob)(2022)
PD Neurotechnol Ltd
Abstract
The Timed Up and Go (TUG) test is a well-established, standardized test used to assess various aspects of a patient's mobility. Although its reliability is proven, instru-mentation is necessary for acquiring accurate information. This work evaluated the instrumentation of the TUG test using devices based on inertial measurement unit (IMU) and UWB radar sen-sors, and subsequently assessed test-related motion parameters, extracted from their data. To that end, five healthy individuals participated in three sessions of a TUG test, performed in slow, normal and fast speeds, while an IMU-based wearable device, the PDMonitor®, and an ultra-wideband (UWB) radar, the Aria Sensing® LT102, monitored their motion. The sessions were also timed, recorded on video, and annotated as a post-processing step. Results showed that both approaches performed very well in estimating walking duration $({R}^{2}=0.9{6}$ for IMU and $R^{2}=0.98$ for UWB) and turning duration $(R^{2}=0.74$ for IMU and $R^{2}=0.66$ for UWB). Moreover, for the IMU sensors, the test duration had excellent correlation with annotations $(R^{2}=0.98)$ and results showed that gait kinematic features could be used as predictors $(AUC=0.9955)$ of detecting a high TUG score $(T^{\mathbf{TUG}}- > 13.5\mathrm{s})$ , identifying increased fall risk. On the other hand, gait speed estimated using UWB data had excellent correlation (R 2 = 0.95) with speed calculated using annotations. The different characteristics of the two approaches, and their good performance in the TUG test's segmentation and assessment of gait parameters, indicate that they could be fused to augment the resulting information.
MoreTranslated text
Key words
IMU sensors,UWB radars,TUG test,Motion analysis,Kinematic analysis
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1991
被引用18710 | 浏览
2013
被引用140 | 浏览
2017
被引用62 | 浏览
2019
被引用70 | 浏览
2020
被引用7 | 浏览
2020
被引用64 | 浏览
2020
被引用18 | 浏览
2020
被引用26 | 浏览
2021
被引用11 | 浏览
2021
被引用24 | 浏览
2021
被引用10 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper