Methods for gait event detection and analysis in ambulatory systems

Rueterbories, Jan; Spaich, Erika G.; Larsen, Birgit Tine; Andersen, Ole Kæseler

doi:10.1016/j.medengphy.2010.03.007

Cited by 316 publications

(252 citation statements)

References 41 publications

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“…Technological advancements have made them miniature, low-powered, durable, inexpensive and highly mobile, thus making it possible to collect long-term data from daily life. While some researchers have developed gait event detection algorithms from gyroscope data, others have developed from accelerometer signals [17]. In either of these situations, researchers could potentially benefit by applying improved algorithms to existing gait databases and utilizing them for future applications and further gait analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], a wavelet based method is used to search for peaks associated with HS and TO which is modified in [22], such that the method can be used with minimal time delay. On the other hand, accelerometers are also being increasingly used as they are low powered devices, in the range of few microamperes, and have been shown to provide reliable measures of gait parameters [17], [23]. Most algorithms analyze signals obtained from individual accelerometer axis by positioning the sensor in a specific pre-defined orientation [2], [3], [13], [24]- [28] with the assumption that the accelerometer shall stay statically positioned throughout the experiment.…”

Section: Introductionmentioning

confidence: 99%

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Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

Khandelwal

Wickström

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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PostprintThis is the accepted version of a paper published in IEEE transactions on neural systems and rehabilitation engineering. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans' natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

Khandelwal

Wickström

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…In this context, accelerometer-based approaches have been proposed to extract relevant gait events and gait phases [12]. Several previous gait event detection works using only accelerometers are based on learning methods, e.g., [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an accelerometer-based method for quantifying gait events

Boutaayamou

Schwartz

Stamatakis

et al. 2015

Medical Engineering & Physics

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An original signal processing algorithm is presented to automatically extract, on a stride-by-stride basis, four consecutive fundamental events of walking, heel strike (HS), toe strike (TS), heel-off (HO), and toe-off (TO), from wireless accelerometers applied to the right and left foot. First, the signals recorded from heel and toe three-axis accelerometers are segmented providing heel and toe flat phases. Then, the four gait events are defined from these flat phases. The accelerometer-based event identification was validated in seven healthy volunteers and a total of 247 trials against reference data provided by a force plate, a kinematic 3D analysis system, and video camera. HS, TS, HO, and TO were detected with a temporal accuracy ± precision of 1.3 ms ± 7.2 ms, -4.2 ms ± 10.9 ms, -3.7 ms ± 14.5 ms, and -1.8 ms ± 11.8 ms, respectively, with the associated 95% confidence intervals ranging from -6.3 ms to 2.2 ms. It is concluded that the developed accelerometer-based method can accurately and precisely detect HS, TS, HO, and TO, and could thus be used for the ambulatory monitoring of gait features computed from these events when measured concurrently in both feet.

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“…However additional effort is needed to minimize donning/doffing efforts and to overcome cosmetical aspects [19]. Furthermore, economical and practical issues demand a simple and inexpensive alternative for this function [8].…”

Section: Discussionmentioning

confidence: 99%

Low-cost piezoelectric footswitch system for measuring temporal parameters during walking

Balbinot

Schuch

Zaro

et al. 2014

IJET

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Human walking is one of the most investigated biomechanical events, and gait analysis depends on accurate measurement of heel strike (HS) and toe off (TO). The purpose of this study was to construct and validate a low-cost footswitch system for the measurement of temporal gait parameters. Ten young healthy subjects participated of the validation and test of the footswitch system with two different footwear, Bland-Altman analysis showed 98% and 95% of validation data within the limits of agreement, for HS and TO respectively (mean difference of 16ms±1ms and 20ms±9ms) and the temporal parameters measured during treadmill walking at a speed of 4.5km.h -1 showed results similar to those found in the literature for normal walking. The outcomes confirm low CoVs for the instrumented athletic and instability shoe, respectively: (1.52±0.61)% and (1.90±0.73)% for contact time, (2.17±0.95)% and (2.57±0.95)% for balance time, (0.84±0.28)% and (1.12±0.53)% for stride time. The low-cost footswitch system described and validated in the present study has an important practical applicability, mostly for emerging and developing countries biomechanics labs.

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Methods for gait event detection and analysis in ambulatory systems

Cited by 316 publications

References 41 publications

Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

Development and validation of an accelerometer-based method for quantifying gait events

Low-cost piezoelectric footswitch system for measuring temporal parameters during walking

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