Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea.
Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea.
Sensors (Basel). 2021 Mar 4;21(5):1786. doi: 10.3390/s21051786.
Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.
肌少症可引起多种老年疾病，是影响老年生活质量的重要因素。为了在日常生活中诊断、评估和监测肌肉减少症，本研究选择了 10 名肌少症患者和 10 名正常对照者，使用瘦体重指数和握力进行评估，并使用基于惯性传感器的步态设备获取他们的步态信号。由于惯性传感器可以测量加速度和角速度，因此在步行运动学分析中非常有用。本研究检测了临床实践中使用的时空参数和所有七个步态阶段的描述性统计参数，以进行详细分析。为了提高肌少症识别的准确性，我们使用 Shapley Additive 解释选择了有助于提高分类准确性的重要参数。支持向量机（SVM）、随机森林和多层感知器是需要传统特征提取的分类方法，而深度学习方法则使用原始数据作为输入来识别肌少症。因此，使用七个步态阶段的描述性统计参数作为输入可以获得更高的准确性。基于知识的步态参数检测比使用深度学习进行自动特征选择更能准确地识别肌少症。使用具有 20 个描述性统计参数的 SVM 模型可达到 95%的最高准确性。我们的研究结果表明，肌少症可以通过可穿戴设备在日常生活中进行监测。
