School of Data Science, City University of Hong Kong, Kowloon, Hong Kong.
School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
J Med Internet Res. 2021 Dec 20;23(12):e30135. doi: 10.2196/30135.
Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest.
The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults.
In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals.
The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN.
The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.
临床活动能力和平衡评估可识别出在诊所中存在高跌倒风险的老年人。在过去的二十年中，传感器已成为活动能力和平衡评估的常用补充手段，可为社区环境提供定量信息和具有成本效益的解决方案。然而，当前基于传感器的平衡评估依赖于手动观察或特定运动特征来识别研究中感兴趣的运动。
本研究旨在开发一种使用惯性传感器收集的信号数据进行平衡活动分析的自动运动数据分析框架，以用于社区居住的老年人。
本研究共招募了 59 名社区居住的老年人（19 名男性，40 名女性；平均年龄=81.86 岁，标准差=6.95 岁）。数据是通过每个个体的 L4 椎骨上的穿戴式惯性测量单元（包括加速度计和陀螺仪）收集的。在通过卷积长短期记忆（LSTM）神经网络进行数据预处理和运动检测后，采用单类支持向量机（SVM）、线性判别分析（LDA）和 k-最近邻（k-NN）对高危个体进行分类。
本研究中开发的框架在检测坐站、360°转身和站坐动作方面的准确率分别为 87%、86%和 89%。使用单类 SVM 和 k-NN 根据 Tinetti 表现导向移动能力评估-平衡（POMA-B）标准对平衡评估分类，异常坐站、360°转身和站坐动作的准确率分别为 90%、92%和 86%。
本研究提出的基于传感器的方法提供了一种高效、省时且省力的方法来识别和预处理惯性信号，从而为医疗专业人员提供了一种有效的平衡评估工具。从长远来看，该方法可能为缓解社区持续健康监测的负担提供一种灵活的解决方案。
