Accuracy of Estimates of Step Frequency From a Wearable Gait Monitor

Punt, Michiel; Wittink, Harriët; Bent, F van der; Dieën, Jh van

doi:10.7309/jmtm.4.1.2

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…Except for gait speed and stride time all these characteristics were determined in three directions using algorithms previously described by Rispens et al [ 8 ]. Estimation of gait quality characteristics was performed on each epoch of 8-s length, which was sufficient long for estimating spectral features [ 21 ]. For each characteristic, the median value over all gait epochs of a participant was used for statistical evaluation.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls

Punt

Bruijn

Schooten

et al. 2016

J NeuroEngineering Rehabil

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BackgroundFalls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest.MethodsThis study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model.ResultsWe found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults.ConclusionThe interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.

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Section: Methodsmentioning

confidence: 99%

Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls

Punt

Bruijn

Schooten

et al. 2016

J NeuroEngineering Rehabil

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“…However, none of the previous studies investigated gesture regularity as a way to detect smoking events. Regularity an important measure of periodicity and variations in periodicity, has been widely used in the studies of gait monitoring and analysis [14,15]. In this paper, we explore the hypothesis that periodicity of hand gestures during smoking may be an important predictor of smoking events.…”

Section: Introductionmentioning

confidence: 99%

“…In order to make the algorithm simple, lightweight, and computationally inexpensive, HMG detection was limited to a single axis of the inertial sensor. Also, the regularity of hand gestures was calculated by an unbiased autocorrelation procedure, which is commonly used in gait analysis studies [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Smoking detection based on regularity analysis of hand to mouth gestures

Senyurek

Imtiaz

Belsare

et al. 2019

Biomedical Signal Processing and Control

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A number of studies have been introduced for the detection of smoking via a variety of features extracted from the wrist IMU data. However, none of the previous studies investigated gesture regularity as a way to detect smoking events. This study describes a novel method to detect smoking events by monitoring the regularity of hand gestures. Here, the regularity of hand gestures was estimated from a one axis accelerometer worn on the wrist of the dominant hand. To quantify the regularity score, this paper applied a novel approach of unbiased autocorrelation to process the temporal sequence of hand gestures. The comparison of regularity score of smoking events with other activities substantiated that hand-to-mouth gestures are highly regular during smoking events and have the potential to detect smoking from among a plethora of daily activities. This hypothesis was validated on a dataset of 140 cigarette smoking events generated by 35 regular smokers in a controlled setting. The regularity of gestures detected smoking events with an F1-score of 0.81. However, the accuracy dropped to 0.49 in the free-living study of same 35 smokers smoking 295 cigarettes. Nevertheless, regularity of gestures may be useful as a supportive tool for other detection methods. To validate that proposition, this paper further incorporated the regularity of gestures in an instrumented lighter based smoking detection algorithm and achieved an improvement in F1-score from 0.89 (lighter only) to 0.91 (lighter and regularity of hand gestures).

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“…Wearable sensors, such as inertial measurement units (IMUs), are relatively cheap, light-weight, easy to use, and therefore a promising alternative approach for data collection in the home environment ( 19 , 20 ). They are particularly useful for gait analysis, as shown in a relatively large number of studies, e.g., in healthy adults ( 21 , 22 ) older adults ( 23 ), and patients with PD ( 24 ). However, for the use of such devices under medical conditions, a thorough validation of detection algorithms is necessary and must be performed in every single population presenting specific gait impairments ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson’s Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back

et al. 2017

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IntroductionInertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson’s disease (PD) and older adults in both a lab-based and home-like environment.MethodsIn this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm.ResultsA continuous wavelet transform (cwt)-based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99/99% accuracy. Similar results were obtained for TO (99/100%). In HS detection, Bland–Altman plots showed a mean difference of 0.002 s [95% confidence interval (CI) −0.09 to 0.10] between the algorithm and the optoelectronic system. The Bland–Altman plot for TO detection showed mean differences of 0.00 s (95% CI −0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83/88% specificity, and 88/89% accuracy. The detection of steps during non-turning phases reached 91/91% sensitivity, 90/90% specificity, and 91/91% accuracy.ConclusionThis cwt-based algorithm for step detection measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.

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Accuracy of Estimates of Step Frequency From a Wearable Gait Monitor

Cited by 3 publications

References 20 publications

Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls

Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls

Smoking detection based on regularity analysis of hand to mouth gestures

Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson’s Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back

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