Abstract
The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from () adult participants; age 18–64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.
Highlights
Engaging in sufficient amounts of physical activity (PA) is associated with decreased risk of premature mortality from cardiovascular diseases [1,2,3]
We have investigated some state-of-the-art features and introduced some novel ones as descriptors of PA for each window of accelerometer signals. 130 such features were extracted from every 10 second window (1000 samples) from different combinations of the four acceleration signals
(79–100%) and hidden Markov model (HMM) (85–99%), with some limitations in specificity regarding a few walking activities. We tested both the classifiers with the entire dataset in different feature spaces to find out the best classification space
Summary
Engaging in sufficient amounts of physical activity (PA) is associated with decreased risk of premature mortality from cardiovascular diseases [1,2,3]. The 2008 physical activity guidelines recommend engaging in at least 150 minutes per week of moderate-vigorous physical activity [4]. Without an accurate PA measurement tool, our ability to determine the relationship between physical activity and health, develop effective interventions to promote these healthy behaviors, and evaluate the effectiveness of these interventions, is severely limited. Human beings perform a wide range of complex activities, varying based on age, profession, time of the day and other demographics. Physical activities of many forms including daily household activities, walking, aerobics, and strength training are performed at various intensities (i.e., light, moderate or vigorous), based on the individual. We need measurement tools to quantify complex human activities accurately, and make necessary interventions to maintain healthy behaviors
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