Browsing by Author "Urbanek, Jacek K."

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation
    (Oxford University Press, 2021-04-10) Karas, Marta; Czkiewicz, Marcin Stra; Fadel, William; Harezlak, Jaroslaw; Crainiceanu, Ciprian M.; Urbanek, Jacek K.; Biostatistics, School of Public Health
    Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a equation M1-m outdoor walk of equation M2 study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online.
  • Thumbnail Image
    Item
    Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data
    (Springer, 2019-05-10) Fadel, William F.; Urbanek, Jacek K.; Albertson, Steven R.; Li, Xiaochun; Chomistek, Andrea K.; Harezlak, Jaroslaw; Biostatistics, School of Public Health
    Wearable accelerometers provide an objective measure of human physical activity. They record high frequency unlabeled three-dimensional time series data. We extract meaningful features from the raw accelerometry data and based on them develop and evaluate a classification method for the detection of walking and its sub-classes, i.e. level walking, descending stairs and ascending stairs. Our methodology is tested on a sample of 32 middle-aged subjects for whom we extracted features based on the Fourier and wavelet transforms. We build subject-specific and group-level classification models utilizing a tree-based methodology. We evaluate the effects of sensor location and tuning parameters on the classification accuracy of the tree models. In the group-level classification setting, we propose a robust feature inter-subject normalization and evaluate its performance compared to unnormalized data. The overall classification accuracy for the three activities at the subject-specific level was on average 87.6%, with the ankle-worn accelerometers showing the best performance with an average accuracy 90.5%. At the group-level, the average overall classification accuracy for the three activities using the normalized features was 80.2% compared to 72.3% for the unnormalized features. In summary, a framework is provided for better use and feature extraction from raw accelerometry data to differentiate among different walking modalities as well as considerations for study design.
  • Thumbnail Image
    Item
    Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data
    (IOP Publishing, 2018-02-28) Urbanek, Jacek K.; Zipunnikov, Vadim; Harris, Tamara; Fadel, William; Glynn, Nancy; Koster, Annemarie; Caserotti, Paolo; Crainiceanu, Ciprian; Harezlak, Jaroslaw; Biostatistics, School of Public Health
    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 sustained harmonic walking (SHW), which we define as walking for at least 10 s with low variability of step frequency. APPROACH: We utilize the harmonic nature of SHW and quantify the local periodicity of the tri-axial raw accelerometry data. We also estimate the fundamental frequency of the observed signals and link it to the instantaneous walking (step-to-step) frequency (IWF). Next, we report the 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: The sensitivity of the proposed classification method was found to be 97%, while specificity ranged between 87% and 97% and the prediction accuracy ranged between 94% and 97%. We report the total time in SHW between 140 and 10 min d-1 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 the detection of SHW and estimation of IWF, based on Fourier decomposition.
  • Thumbnail Image
    Item
    Stride variability measures derived from wrist- and hip-worn accelerometers
    (Elsevier, 2017-02) Urbanek, Jacek K.; Harezlak, Jaroslaw; Glynn, Nancy W.; Harris, Tamara; Crainiceanu, Ciprian; Zipunnikov, Vadim; Biostatistics, School of Public Health
    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 measures of temporal stride-to-stride gait 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 400m walk test. We also show that AD and PD have a statistically significant association with the gait speed and sit-to-stand test performance.
  • Thumbnail Image
    Item
    Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data
    (MDPI, 2020-11) Fadel, William F.; Urbanek, Jacek K.; Glynn, Nancy W.; Harezlak, Jaroslaw; Biostatistics, School of Public Health
    Various methods exist to measure physical activity. Subjective methods, such as diaries and surveys, are relatively inexpensive ways of measuring one’s physical activity; however, they are prone to measurement error and bias due to self-reporting. Wearable accelerometers offer a non-invasive and objective measure of one’s physical activity and are now widely used in observational studies. Accelerometers record high frequency data and each produce an unlabeled time series at the sub-second level. An important activity to identify from the data collected is walking, since it is often the only form of activity for certain populations. Currently, most methods use an activity summary which ignores the nuances of walking data. We propose methodology to model specific continuous responses with a functional linear model utilizing spectra obtained from the local fast Fourier transform (FFT) of walking as a predictor. Utilizing prior knowledge of the mechanics of walking, we incorporate this as additional information for the structure of our transformed walking spectra. The methods were applied to the in-the-laboratory data obtained from the Developmental Epidemiologic Cohort Study (DECOS).