从加速计数据中预测能源消耗,使用长期和短期记忆的循环神经网络
Martin Vibæk1, Abdolrahman Peimankar1, Uffe Kock Wiil1
1SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
新的深度学习模型使用加速度计数据准确估计能源支出. 分析运动模式的循环神经网络显示了对儿童体育活动监测的准确性提高.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 机器学习在健康中的应用
背景情况:
- 精确估计能源消耗 (EE) 对体力活动 (PA) 干预和人口监测至关重要.
- 现有的加速度计方法往往忽略了运动数据的时间动态.
- 需要新的方法来提高从客观测量的EE预测的精度.
研究的目的:
- 通过利用加速度计数据的时间方面来研究循环神经网络 (RNN) 在预测能量消耗方面的有效性.
- 将基于RNN的模型与传统方法 (如多重线性回归 (MLR)) 的性能进行比较.
主要方法:
- 收集了33名儿童在自然环境中的标准化活动协议期间的加速度计数据.
- 在多个身体部位测量加速:部,手腕,大腿和背部.
- 使用多重线性回归 (MLR),堆叠的长短期记忆 (LSTM) 网络以及组合的卷积神经网络 (CNN) 和LSTM来建模能量支出.
主要成果:
- 结合LSTM-CNN模型实现了最高的预测准确度,相关性为0.883%,平均绝对百分比误差 (MAPE) 为13.9%.
- 与MLR (相关性:0.76,MAPE:19.9%) 相比,LSTM网络显示出更高的性能 (相关性:0.882,MAPE:14.22%).
- 强烈的PA强度的预测误差明显高 (p < 0.01) 比静止,轻度和中度强度.
结论:
- 通过深度学习将时间移动数据纳入,可以显著提高能源支出预测的准确性.
- 循环神经网络架构,特别是CNN-LSTM组合,比传统方法提供了有前途的进步.
- 需要进一步的研究来解决在剧烈的体力活动期间观察到的高预测误差.
相关概念视频
01:12Energy Stored in a Capacitor
When an archer pulls the string in a bow, he saves the work done in the form of elastic potential energy. When he releases the string, the potential energy is released as kinetic energy of the arrow. A capacitor works on the same principle in which the work done is saved as electric potential energy. The potential energy (UC) could be calculated by measuring the work done (W) to charge the capacitor.
01:25Neural Circuits
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01:10Energy Stored in Capacitors
A parallel plate capacitor, when connected to a battery, develops a potential difference across its plates. This potential difference is key to the operation of the capacitor, as it determines how much electrical energy the capacitor can store.
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01:17Energy and Power Signals
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
01:23Storage
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01:18Long-Term Memory
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