Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6-minute bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.

Purpose Accelerometers are a valuable tool for objective measurement of physical activity (PA). Wrist-worn devices may improve compliance over standard hip placement, but more research is needed to evaluate their validity for measuring PA in free-living settings. Traditional cut-point methods for accelerometers can be inaccurate, and need testing in free-living with wrist-worn devices. In this study we developed and tested the performance of machine learned (ML) algorithms for classifying PA types from both hip and wrist accelerometer data. Methods Forty overweight or obese women (mean age = 55.2 ±15.3 yrs; BMI = 32.0 ± 3.7) wore two ActiGraph GT3X+ accelerometers (right hip, non-dominant wrist) for seven free-living days. Wearable cameras captured ground truth activity labels. A classifier consisting of a random forest and hidden Markov model classified the accelerometer data into four activities (sitting, standing, walking/running, riding in a vehicle). Free-living wrist and hip ML classifiers were compared to each other, to traditional accelerometer cut points, and to an algorithm developed in a laboratory setting. Results The ML classifier obtained an average of 89.4% and 84.6% balanced accuracy over the four activities using the hip and wrist accelerometer, respectively. In our dataset with an average of 28.4 minutes of walking or running per day, the ML classifier predicted an average of 28.5 minutes and 24.5 minutes of walking or running using the hip and wrist accelerometer, respectively. Intensity-based cutpoints and the laboratory algorithm significantly underestimated walking minutes. Conclusions Our results demonstrate the superior performance of our PA type classification algorithm, particularly in comparison to traditional cut-points. While the hip algorithm performed better, additional compliance achieved with wrist devices might justify using a slightly lower performing algorithm.

Abstract-The ability to automatically recognize a person's behavioral context can contribute to health monitoring, aging care and many other domains. Validating context recognition in-the-wild is crucial to promote practical applications that work in real-life settings. We collected over 300k minutes of sensor data with context labels from 60 subjects. Unlike previous studies, our subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that are harder to recognize. We demonstrate how fusion of multi-modal sensors is important for resolving such cases. We present a baseline system, and encourage researchers to use our public dataset to compare methods and improve context recognition in-the-wild.

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data.Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time.Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%.Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.

Purpose Walking for health is recommended by health agencies, partly based on epidemiological studies of self-reported behaviors. Accelerometers are now replacing survey data but it is not clear that intensity based cut points reflect the behaviors previously reported. New computational techniques can help classify raw accelerometer data into behaviors meaningful for public health. Methods 520 days of triaxial 30 hertz accelerometer data from 3 studies (n=78) were employed as training data. Study 1 included prescribed activities completed in natural settings. The other two studies included multiple days of free living data with SenseCam annotated ground truth. The two populations in the free living data sets were demographically and physical different. Random forest classifiers were trained on each data set and the classification accuracy on the training data set and applied to the other available data sets was assessed. Accelerometer cut points were also compared with the ground truth from the 3 datasets. Results The random forest classified all behaviors with over 80% accuracy. Classifiers developed on the prescribed data performed with higher accuracy than the free living data classifier, but did not perform as well on the free living datasets. Many of the observed behaviors occurred at different intensities than those identified by existing cut points. Conclusions New machine learned classifiers developed from prescribed activities (Study 1) were considerably less accurate when applied to free-living populations or to a functionally different population (Studies 2 & 3). These classifiers, developed on free living data, may have value when applied to large cohort studies with existing hip accelerometer data.