Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics.
Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics.
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DOI：
10.3390/s21103481
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发表时间：
2021-05-17
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影响因子：
--
通讯作者：
Baek S
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文献类型：
--
作者：
Roshdibenam V;Jogerst GJ;Butler NR;Baek S
老年人跌倒会造成有害的身体、精神和经济问题，在最坏的情况下还会导致死亡。越来越多的人进入高危年龄范围，增加了临床医生对干预的关注。临床工具，例如，计时起来和走（TUG）测试，已经创建用于帮助临床医生进行跌倒风险评估。通常很容易评估，这些评估受制于临床医生的判断。采用机器学习算法的可穿戴传感器数据被引入，作为精确量化运动运动学和预测未来跌倒的替代方案。然而，它们需要对受试者的大样本运动进行长期评估，并需要对传感器运动学进行复杂的特征工程。因此，建立一个客观的跌倒风险检测模型，以最小的成本有效地测量生物特征风险因素是至关重要的。我们建立并研究了一个传感器数据驱动的卷积神经网络模型来预测老年人的跌倒风险状态，该模型对老年医生的专家评估具有较高的敏感性。本研究的样本代表了日常医疗实践中出现的多重合并症的老年患者。在TUG测试中，使用三个非侵入式可穿戴传感器来测量参与者的步态运动学。这种数据收集确保了在不同身体位置方便地捕获各种步态障碍方面。
Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician’s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects’ locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults’ fall-risk status with relatively high sensitivity to geriatrician’s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants’ gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations.
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DOI：
10.3390/s140100443
发表时间：
2013-12-27
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
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通讯作者：
Aminian K
影响因子：
3.9
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影响因子：
4
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Iguchi, Akihisa
DOI：
10.1111/j.1532-5415.1991.tb01616.x
发表时间：
1991-02-01
影响因子：
6.3
作者：
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通讯作者：
RICHARDSON, S
DOI：
10.1097/00005373-200101000-00021
发表时间：
2001-01-01
影响因子：
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作者：
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通讯作者：
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