Quantifying human movement using the Movn smartphone app: validation and field study

BACKGROUND: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people\u27s geocoded movement patterns to determine their interaction with the environment. OBJECTIVE: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. METHODS: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. RESULTS: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. CONCLUSIONS: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics

On Determining the Best Physiological Predictors of Activity Intensity Using Phone-Based Sensors

Abstract—Physical inactivity is a leading risk factor in worldwide deaths. This problem has led to the need for new research paradigms investigating the effect of sedentary behavior on negative health outcomes. Central to this need is the development of objective and ubiquitous sensors that provide accurate measurements of activity to assist in intervention. Phone-based kinematic sensors, such as accelerometers and gyroscopes, are one such option. Current kinematic sensor models have limited capability in adjusting for inter-personal physiological differences in the maps from movement to activity intensity since they focus on weight and height information. It would be useful to explore what features are the best descriptors for a population. We present a family of regression techniques that incorporate an arbitrary number of physiological features and use this framework to determine the best physiological features to map movement to energy expenditure. We do this for rest, treadmill and overground walking since these are the most common activities for which intervention is necessary. Sizebased features, such as height, weight and BMI were the best descriptors for personalization. BMI was the best descriptor for rest and height was the best descriptor for walking. Fitness based features, such as resting energy expenditure and resting heart rate, were the least useful descriptors, particularly for walking. I

An Inertial Sensor-based System to Develop Motor Capacity in Children with Cerebral Palsy

Abstract—Learning to communicate with alternative augmentative communication devices can be difficult because of the difficulty of achieving controlled interaction while simultaneously learning to communicate. What is needed is a device that harnesses a child’s natural motor capabilities and provides the means to reinforce them. We present a kinematic sensor-based system that learns a child’s natural gestural capability and allows him/her to practice those capabilities in the context of a game. Movement is captured with a single kinematic sensor that can be worn anywhere on the body. A gesture recognition algorithm interactively learns gesture models using kinematic data with the help of a nearby teacher. Learned gesture models are applied in the context of a game to help the child practice gestures to gain better consistency. The system was successfully tested with a child over two sessions. The system learned four candidate gestures: lift hand, sweep right, twist right and punch forward. These were then used in a game. The child showed better consistency in performing the gestures as each session progressed. We aim to expand on this work by developing qualitative scores of movement quality and quantifying algorithm accuracy on a larger population over long periods of time

Hierarchical Probabilistic Regression for AUV-based Adaptive Sampling of Marine Phenomena

Abstract — Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data. I

Collective Transport of Robots: Coherent, Minimalist Multi-robot Leader-following

Abstract — We study the collective transport of robots (CTR) problem. A large number of commodity mobile robots are to be moved from one location to another by a single operator. Joysticking each one or carrying them physically is impractical. None of the robots are particularly sophisticated in their ability to plan or reason. Prior work on flocking and formation control has addressed the transport of a robot group that maintains its integrity by explicitly controlling coherence. We show how flocking emerges as a consequence of each robot contending for space near the human operator. A coherent flock can be made to follow a leader in this manner thereby solving the CTR problem. We also present the design of a hand-worn IMU-based gesture interface which allows the human operator to issue simple commands to the group. A preliminary experimental evaluation of the system shows robust CTR with different leade