arXiv subjects

Tamara Harris

Publications and source records attributed to Tamara Harris.

4 recordsLinked to original sources

arXiv2017

Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data

Objective. Using raw, sub-second level, accelerometry data, we propose and validate a method for identifying and characterizing walking in the free-living environment. We focus on the sustained harmonic walking (SHW), which we define as walking for at least 10 seconds with low variability of step frequency. Approach. We utilize the harmonic nature of SHW and quantify local periodicity of the tri-axial raw accelerometry data. We also estimate fundamental frequency of observed signals and link it to the instantaneous walking (step-to-step) frequency (IWF). Next, we report total time spent in SHW, number and durations of SHW bouts, time of the day when SHW occurred and IWF for 49 healthy, elderly individuals. Main results. Sensitivity of the proposed classification method was found to be 97%, while specificity ranged between 87% and 97% and prediction accuracy between 94% and 97%. We report total time in SHW between 140 and 10 minutes-per-day distributed between 340 and 50 bouts. We estimate the average IWF to be 1.7 steps-per-second. Significance. We propose a simple approach for detection of SHW and estimation of IWF, based on Fourier decomposition. The resulting approach is fast and allows processing of a week-long raw accelerometry data (approx. 150 million measurements) in relatively short time (~half an hour) on a common laptop computer (2.8 GHz Intel Core i7, 16 GB DDR3 RAM).

stat.AP↗

arXiv2017

Validation of gait characteristics extracted from raw accelerometry during walking against measures of physical function, mobility, fatigability, and fitness

Background. Wearable accelerometry devices allow collection of high-density activity data in large epidemiological studies both in-the-lab as well as in-the-wild (free-living). Such data can be used to detect and identify periods of sustained harmonic walking. This report aims to establish whether the micro- and macro-features of walking identified in the laboratory and free-living environments are associated with measures of physical function, mobility, fatigability, and fitness. Methods. Fifty-one older adults (median age 77.5) enrolled in the Developmental Epidemiologic Cohort Study in Pittsburgh, Pennsylvania were included in the analyses. The study included an in-the-lab component as well as 7 days of monitoring in-the-wild. Participants were equipped with hip-worn Actigraph GT3X+ activity monitors, which collect high-density raw accelerometry data. We applied a walking identification algorithm to the data and defined features of walking, such as participant-specific walking acceleration and cadence. The association between these walking features and physical function, mobility, fatigability, and fitness was quantified using linear regression analysis. Results. Micro-scale features of walking (acceleration and cadence) estimated from in-the-lab and in-the-wild data were associated with measures of physical function, mobility, fatigability, and fitness. In-the-lab median walking acceleration was strongly inversely associated with physical function, mobility, fatigability and fitness. Additionally, in-the-wild daily walking time was inversely associated with usual- and fast-paced 400m walking time. Conclusions. The proposed accelerometry-derived walking features are significantly associated with measures of physical function, mobility, fatigability, and fitness, which provides evidence of convergent validity.

q-bio.QM↗

arXiv2016

Stride variability measures derived from wrist- and hip-worn accelerometers

Many epidemiological and clinical studies use accelerometry to objectively measure physical activity using the activity counts, vector magnitude, or number of steps. These measures use just a fraction of the information in the raw accelerometry data as they are typically summarized at the minute level. To address this problem we define and estimate two gait measures of temporal stride-to-stride variability based on raw accelerometry data: Amplitude Deviation (AD) and Phase Deviation (PD). We explore the sensitivity of our approach to on-body placement of the accelerometer by comparing hip, left and right wrist placements. We illustrate the approach by estimating AD and PD in 46 elderly participants in the Developmental Epidemiologic Cohort Study (DECOS) who worn accelerometers during a 400 meter walk test. We also show that AD and PD have a statistically significant association with the gait speed and sit-to-stand test performance

q-bio.NC↗

arXiv2014

Movement Prediction Using Accelerometers in a Human Population

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.

stat.AP↗