Abstract
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.
Highlights
At older age, the extension of health span and maintenance of mobility are of great importance for the quality of life
We show that longer windows of prior sensor lead to better physical activity energy expenditure (PAEE) estimation, and that Gated Recurrent Units (GRU) model based on standard deviation can deal with these longer windows efficiently
We developed and tested a recurrent neural network architecture based on an efficient down-sampling method that incorporates standard deviation for downsampling the input data to estimate physical activity energy expenditure within an elderly population
Summary
The extension of health span and maintenance of mobility are of great importance for the quality of life. Regular physical activity (PA) of moderate intensity is known to offer positive effects on the reduction of disease incidence and mortality risk (Manini et al 2006; Chen et al 2012; Cicero et al 2012; Petersen et al 2012). To quantify and monitor the intensity of PA, estimation of energy expenditure during physical activity is an obvious necessity. By monitoring physical activity energy expenditure (PAEE), older people may better engage in physical activities, leading to better health and reduced (multi)morbidity and mortality risk (Manini et al 2006). One way to measure PAEE is using direct calorimetry and measurements of heat production, but expensive equipment is required. The Doubly Labeled Water Technique (DLW) provides an accurate technique of TEE estimation from where PAEE can be estimated, similar to direct calorimetry, it requires sophisticated lab-based equipment to analyse urine samples. Indirect calorimetry (Leonard 2012) is commonly used, which involves the measurement of oxygen and carbon dioxide exchange by ventilated mask or hood
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