基于RNN的老年人体力活动能量消耗监测
RNNs on Monitoring Physical Activity Energy Expenditure in Older People
摘要：
通过量化体力活动能量消耗，保健监测有可能刺激活力和健康的老龄化，诱导老年人的行为变化，并将这些与个人健康收益联系起来。为了能够在监测环境中测量PAEE，已经开发了来自可穿戴加速度计的方法，然而，主要针对年轻人。由于老年受试者的能量需求和体力活动范围不同，目前的模型可能不适合估计老年人的PAEE。由于过去的活动影响目前的PAEE，我们提出了一种建模方法，其建模能力的顺序数据，递归神经网络（RNN）。为了训练老年人群的RNN，我们使用了GOTOV数据集，其中有34名60岁及以上（平均65岁）的健康参与者，进行了16种不同的活动。我们使用加速度计放在手腕和脚踝，并通过间接量热法测量能量计数。优化后，我们提出了一个架构，由一个RNN与3 GRU层和一个前馈网络相结合的加速度计和参与者级数据。在本文中，我们描述了我们超越基于GRU的RNN的标准设施的努力，目的是实现超越最先进水平的准确性。这些努力包括将聚合函数从均值切换到分散度量（SD，IQR，...），结合时间和静态数据（特定于个人的细节，如年龄，体重，BMI），并添加由先前训练的ML模型预测的符号活动数据。由此产生的架构设法将其性能提高了约10%，同时将训练输入减少了10倍。因此，它可以用来调查PAEE与代谢和认知健康以及心理健康相关的活力参数的关联。
英文摘要：
Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a monitoring environment, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the Recurrent Neural Network (RNN). To train the RNN for an elderly population, we used the GOTOV dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. In this paper, we describe our efforts to go beyond the standard facilities of a GRU-based RNN, with the aim of achieving accuracy surpassing the state of the art. These efforts include switching aggregation function from mean to dispersion measures (SD, IQR, ...), combining temporal and static data (person-specific details such as age, weight, BMI) and adding symbolic activity data as predicted by a previously trained ML model. The resulting architecture manages to increase its performance by approximatelly 10% while decreasing training input by a factor of 10. It can thus be employed to investigate associations of PAEE with vitality parameters related to metabolic and cognitive health and mental well-being.
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