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Previously the National Health and Examination Survey measured physical activity with an accelerometer worn on the hip for seven days, but recently changed the location of the monitor to the wrist. PURPOSE This study compared estimates of physical activity intensity and type with an accelerometer on the hip versus the wrist. METHODS Healthy adults (n=37) wore triaxial accelerometers (Wockets) on the hip and dominant wrist along with a portable metabolic unit to measure energy expenditure during 20 activities. Motion summary counts were created, then receiver operating characteristic (ROC) curves were used to determine sedentary and activity intensity thresholds. Ambulatory activities were separated from other activities using the coefficient of variation (CV) of the counts. Mixed model predictions were used to estimate activity intensity. RESULTS The ROC for determining sedentary behavior had greater sensitivity and specificity (71% and 96%) at the hip than the wrist (53% and 76%), as did the ROC for moderate to vigorous physical activity on the hip (70% and 83%) versus the wrist (30% and 69%). The ROC for the CV associated with ambulation had a larger AUC at the hip compared to the wrist (0.83 and 0.74). The prediction model for activity energy expenditure (AEE) resulted in an average difference of 0.55 (+/− 0.55) METs on the hip and 0.82 (+/− 0.93) METs on the wrist. CONCLUSIONS Methods frequently used for estimating AEE and identifying activity intensity thresholds from an accelerometer on the hip generally do better than similar data from an accelerometer on the wrist. Accurately identifying sedentary behavior from a lack of wrist motion presents significant challenges.

This paper describes the motivation for, and overarching design of, an open-source hardware and software system to enable population-scale, longitudinal measurement of physical activity and sedentary behavior using common mobile phones. The “Wockets” data collection system permits researchers to collect raw motion data from participants who wear multiple small, comfortable sensors for 24 hours per day, including during sleep, and monitor data collection remotely.

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Generalizing knowledge about physical movement often requires significant amounts of data capture. Despite the large effort to collect and process activity examples, these systems can still fail to classify movements due to many reasons. Our system, called GestureNet, uses a very small dataset of activity templates to get useful query results for a generalized set of movements. Thus, many more movement profiles can be generated for activity recognition systems and gesture synthesis algorithms.We demonstrate a system that is able to support a larger set of computer animations based on a small set of base animations. A user can input any motion word recognized by GestureNet, and the system will respond with the closest animation match. GestureNet will also describe the degree to which the new activity is similar to the template profiles. One example is if the user inputs "baseball," the system will show the animation for Run. The commonsense database associates baseball with jogging, which is a type of running. Although the example gesture matrix is small, we demonstrate that our techniques can extend the system to describe variations of these activities (e..g sitting and squatting) which are not currently represented. We can expect that this solution will be useful in application domains where sensor data capture and activity profiles are costly to acquire (e.g. activity classification, animations and visualizations).