Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
Department of Kinesiology, Recreation and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA.
Sensors (Basel). 2024 May 11;24(10):3046. doi: 10.3390/s24103046.
Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.
代谢综合征在全球范围内构成重大健康挑战，需要整合体力活动监测和能量消耗的综合策略。可穿戴传感器设备已被用于能量摄入和能量消耗 (EE) 的估计。传统上，传感器附着在臀部或手腕上。本研究的主要目的是研究使用眼镜式可穿戴能量摄入传感器（Automatic Ingestion Monitor v2，AIM-2）同时识别体力活动 (PAR) 和估计稳态 EE，与传统的髋部佩戴设备相比。研究数据来自六名参与者进行的六项结构化活动，参考 EE 使用间接测热法（COSMED K5）测量，并以任务代谢当量 (MET) 报告。接下来，开发了一种基于新型深度卷积神经网络的多任务模型 (Multitasking-CNN) 用于 PAR 和 EE 估计。Multitasking-CNN 使用两步渐进式训练方法进行训练，以提高准确性，在第一步中训练用于 PAR 的模型，在第二步中微调用于 EE 估计的模型。最后，将 Multitasking-CNN 在眼镜上附着的 AIM-2 的性能与附着在右臀部上的 ActiGraph GT9X (AG) 进行比较。在 AIM-2 数据上，Multitasking-CNN 在 PAR 方面达到了 95%的测试精度，最小 0.59 METs 均方误差 (MSE) 和 11%平均绝对百分比误差 (MAPE) 的最大 EE 估计值。相比之下，在 AG 数据上，Multitasking-CNN 模型在 PAR 方面达到了 82%的测试精度，最小 0.73 METs MSE 和 13%的 EE 估计值的最大 MAPE。这些结果表明，使用眼镜式传感器进行 PAR 和 EE 估计是可行的。
