A recurrent neural network architecture to model physical activity energy expenditure in older people

• Published in: Data mining and knowledge discovery Vol. 36; no. 1; pp. 477 - 512
• Main Authors: Paraschiakos, Stylianos, de Sá, Cláudio Rebelo, Okai, Jeremiah, Slagboom, P. Eline, Beekman, Marian, Knobbe, Arno
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2022; Springer Nature B.V
• Subjects: Accelerometers; Artificial Intelligence; Chemistry and Earth Sciences; Computer architecture; Computer Science; Data Mining and Knowledge Discovery; Energy requirements; Exercise; Health care; Information Storage and Retrieval; Neural networks; Older people; Optimization; Personal health; Physics; Recurrent neural networks; Special Issue: Mining for Health; Statistics for Engineering; Wrist; Accelerometer; Wearables; Recurrent neural networks; Monitoring older adults; Indirect calorimetry; Physical activity energy expenditure
• DOI: 10.1007/s10618-021-00817-w
• ISSN: 1384-5810

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 health care perspective, 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. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. 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 growing old together validation (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. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.