Dataset Information

Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

ABSTRACT: Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects' habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

SUBMITTER: Tietsch M

PROVIDER: S-EPMC7768099 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 ms) and long-term (30 min) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that PCEN reduces temporal overfitting across dawn vs. dusk audio clips whereas context adaptation on PCEN-based summary statistics reduces spatial overfitting across sensor locations. Moreover, combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.

Project description:PurposeTo adapt and validate a previously developed decision tree for youth to identify bedrest for use in preschool children.MethodsParents of healthy preschool (3-6-year-old) children (n = 610; 294 males) were asked to help them to wear an accelerometer for 7 to 10 days and 24 hours/day on their waist. Children with ≥3 nights of valid recordings were randomly allocated to the development (n = 200) and validation (n = 200) groups. Wear periods from accelerometer recordings were identified minute-by-minute as bedrest or wake using visual identification by two independent raters. To automate visual identification, chosen decision tree (DT) parameters (block length, threshold, bedrest-start trigger, and bedrest-end trigger) were optimized in the development group using a Nelder-Mead simplex optimization method, which maximized the accuracy of DT-identified bedrest in 1-min epochs against synchronized visually identified bedrest (n = 4,730,734). DT's performance with optimized parameters was compared with the visual identification, commonly used Sadeh's sleep detection algorithm, DT for youth (10-18-years-old), and parental survey of sleep duration in the validation group.ResultsOn average, children wore an accelerometer for 8.3 days and 20.8 hours/day. Comparing the DT-identified bedrest with visual identification in the validation group yielded sensitivity = 0.941, specificity = 0.974, and accuracy = 0.956. The optimal block length was 36 min, the threshold 230 counts/min, the bedrest-start trigger 305 counts/min, and the bedrest-end trigger 1,129 counts/min. In the validation group, DT identified bedrest with greater accuracy than Sadeh's algorithm (0.956 and 0.902) and DT for youth (0.956 and 0.861) (both P<0.001). Both DT (564±77 min/day) and Sadeh's algorithm (604±80 min/day) identified significantly less bedrest/sleep than parental survey (650±81 min/day) (both P<0.001).ConclusionsThe DT-based algorithm initially developed for youth was adapted for preschool children to identify time spent in bedrest with high accuracy. The DT is available as a package for the R open-source software environment ("PhysActBedRest").

Project description:BackgroundGait event detection is critical for remote gait analysis. Algorithms using a thigh-worn accelerometer for estimating spatiotemporal gait variables have demonstrated clinical utility in monitoring the gait of patients with gait and balance impairment. However, one may obtain accurate estimates of spatiotemporal variables, but with biased estimates of foot contact and foot off events. Some biomechanical analyses depend on accurate gait phase segmentation, but previous studies using a thigh-worn accelerometer have not quantified the error in estimating foot contact and foot off events.MethodsGait events and spatiotemporal gait variables were estimated using a thigh-worn accelerometer from 32 healthy subjects across a range of walking speeds (0.56-1.78 m/s). Ground truth estimates were obtained using vertical ground reaction forces measured using a pressure treadmill. Estimation performance was quantified using absolute error, root mean square error, and correlation analysis.ResultsAcross all strides (N = 3,898), the absolute error in estimating foot contact, foot off, stride time, stance time, and swing time was similar to other accelerometer-based techniques (39 ± 28 ms, 28 ± 28 ms, 11 ± 14 ms, 46 ± 31 ms, and 45 ± 30 ms, respectively). The correlation between reference measurements and estimates of bout-average stride time, stance time, and swing time were 1.00, 0.92, and 0.80, respectively. The (5th, 95th) percentiles of the foot contact and foot off estimation errors were (-91 ms, 51 ms) and (-70 ms, 60 ms), the largest of which amounts to about three samples using the 31.25 Hz sampling frequency used in this study.SignificanceUse of the proposed algorithm for estimating spatiotemporal gait variables is supported by the strong correlations with reference measurements. The gait event estimation error distributions provide bounds on the estimated gait events for enforcing gait phase-dependent task constraints for biomechanical analysis.

Dataset Information

Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets