Evaluation of Neural Networks to Identify Types of Activity Using Accelerometers
Evaluation of Neural Networks to Identify Types of Activity Using Accelerometers
复制标题
DOI：
10.1249/mss.0b013e3181e5797d
复制
发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
van Buuren, Stef
中科院分区：
文献类型：
--
作者：
de Vries, Sanne I.;Garre, Francisca Galindo;van Buuren, Stef
DE VRIES、S. I.、F. GALINDO GARRE、L. H. ENGBERS、V. H. 希尔德布兰特和 S. Van BUUREN。使用加速度计评估神经网络以识别活动类型。医学。科学。体育锻炼，卷。 43，第 1 期，第 101-107 页，2011 年。目的：开发和评估两个基于单传感器加速度计数据的人工神经网络 (ANN) 模型和一个基于两个加速度计数据的 ANN 模型，用于识别成人身体活动的类型。方法：49 名受试者（21 名男性和 28 名女性；年龄范围 = 22-62 岁）进行了一系列受控的活动：坐着、站立、爬楼梯、以两种自定节奏的速度行走和骑自行车。所有受试者的臀部和脚踝处都佩戴了 ActiGraph 加速度计。在 ANN 模型中，使用了以下加速度计信号特征：第 10、25、75 和 90 个百分位数、绝对偏差、变异系数和滞后一自相关。结果：基于臀部加速度计数据的模型和基于脚踝加速度计数据的模型对五种活动的正确分类率分别为 80.4% 和 77.7%，而基于两个传感器数据的模型的正确分类率为 83.0%。髋部模型能够更好地对骑车、爬楼梯和坐姿活动进行分类，而踝部模型能够更好地对行走和站立活动进行正确分类。所有这三个模型经常在使用楼梯和静止不动时被错误分类。当区分定期步行或骑自行车与快走或骑自行车以及上下楼梯之间时，模型的准确性显着下降。结论：相对简单的 ANN 模型在根据加速度计数据识别成人活动类型方面表现良好，但在识别成人活动速度方面表现不佳。
DE VRIES, S. I., F. GALINDO GARRE, L. H. ENGBERS, V. H. HILDEBRANDT, AND S. VAN BUUREN. Evaluation of Neural Networks to Identify Types of Activity Using Accelerometers. Med. Sci. Sports Exerc., Vol. 43, No. 1, pp. 101-107, 2011. Purpose: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults. Methods: Forty-nine subjects (21 men and 28 women; age range = 22-62 yr) performed a controlled sequence of activities: sitting, standing, using the stairs, and walking and cycling at two self-paced speeds. All subjects wore an ActiGraph accelerometer on the hip and the ankle. In the ANN models, the following accelerometer signal characteristics were used: 10th, 25th, 75th, and 90th percentiles, absolute deviation, coefficient of variability, and lag-one autocorrelation. Results: The model based on the hip accelerometer data and the model based on the ankle accelerometer data correctly classified the five activities 80.4% and 77.7% of the time, respectively, whereas the model based on the data from both sensors achieved a percentage of 83.0%. The hip model produced a better classification of the activities cycling, using the stairs, and sitting, whereas the ankle model was better able to correctly classify the activities walking and standing still. All three models often misclassified using the stairs and standing still. The accuracy of the models significantly decreased when a distinction was made between regular versus brisk walking or cycling and between going up and going down the stairs. Conclusions: Relatively simple ANN models perform well in identifying the type but not the speed of the activity of adults from accelerometer data.