Purpose The study aims were: 1) to develop transparent algorithms that use short segments of training data for predicting activity types; and 2) to compare prediction performance of proposed algorithms using single accelerometers and multiple accelerometers. Methods Sixteen participants (age, 80.6 yr (4.8 yr); BMI, 26.1 kg·m−2 (2.5 kg·m−2)) performed fifteen life-style activities in the laboratory, each wearing three accelerometers at the right hip, left and right wrists. Triaxial accelerometry data were collected at 80 Hz using Actigraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers. Results With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2% to 99.9% based on the single-accelerometer movelet approach). For these activities wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from −2.7% to 5.8%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers. Discussion and conclusions It is possible to achieve high prediction accuracy at the secondlevel temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.
We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.
A model has been developed for describing the stresses that arise during binder burnout in three-dimensional porous bodies. The pressure gradient that arises from the decomposition of binder in the pore space is treated as an equivalent body force. For input into the mechanics model, the pressure distribution is obtained from the analytical solution for three-dimensional porous bodies with anisotropic permeability. The normal and shear stresses are then calculated from finite element analysis for bodies of parallelepiped geometry. In general, the normal stresses occur at the center of the body and are an order of magnitude larger than the shear stresses. Both the normal and shear stresses depend on the body size, the body geometry, and on the permeability.
Summary We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the data across subjects by matching the standing up and lying down portions of triaxial accelerometry data. This is necessary to account for differences between the variability in the position of the device relative to gravity, which are induced by body shape and size as well as by the ambiguous definition of device placement. We also normalize the data at the device level to ensure that the magnitude of the signal at rest is similar across devices. After normalization we use overlapping movelets (segments of triaxial accelerometry time series) extracted from some of the subjects to predict the movement type of the other subjects. The problem was motivated by and is applied to a laboratory study of 20 older participants who performed different activities while wearing accelerometers at the hip. Prediction results based on other people’s labeled dictionaries of activity performed almost as well as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible.
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