  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.
