Abstract
Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back.
Highlights
Ubiquitous consumer devices such as smartphones and wearables are equipped with low power inertial sensors such as accelerometers and gyroscopes capable of continuously recording their wearer’s movements
For the comparison with existing algorithms, we implemented the following widely used generic gait detection algorithms: STD-thresholding [33, 15]; short-time Fourier transform (STFT)-thresholding [36]; normalized autocorrelation step detection and counting (NASC) [43] and continuous wavelet transform (CWT) thresholding [81].We evaluate the performance of the original formulations of the algorithms, and the performance after applying our pre-processing pipeline and adjusting corresponding thresholds to maximize the balanced accuracy across participants using leave-one-subjectout cross-validation1:
We observe a difference in accuracy between Parkinson’s disease (PD) patients and controls, and between before and after medication intake for PD patients
Summary
Ubiquitous consumer devices such as smartphones and wearables are equipped with low power inertial sensors such as accelerometers and gyroscopes capable of continuously recording their wearer’s movements. In order to extract meaningful information about a patient’s free living gait, we need a robust framework for gait detection and characterization of the gait pattern When it comes to gait detection, most existing work can be classified into two systems: (1) activity recognition systems which classify (real time) data into a fixed number of pre-specified activities [11, 12, 13], and (2) gait detection systems that perform binary classification to determine whether a window of data belongs to the gait or no gait class[14, 15, 16]. Binary gait detection systems are often implemented using threshold values applied to statistical summaries of windowed data[17, 18, 15, 19, 20] Whereas this “low complexity” approach may have acceptable accuracy to globally describe how much users walk, problems can emerge when it is used as a starting point for evaluating the quality of the gait in health monitoring applications.
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