Gait event detection using a multilayer neural network

Miller, Adam

doi:10.1016/j.gaitpost.2008.12.003

Cited by 54 publications

(34 citation statements)

References 11 publications

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“…Moreover, it substantially decreases data processing time for clinical gait labs (Miller 2009). The detection of gait events is essentially a classification problem; an application for which artificial neural networks are well suited.…”

Section: Discussionmentioning

confidence: 99%

“…The detection of gait events is essentially a classification problem; an application for which artificial neural networks are well suited. Miller (2009) used a single-hidden-layer, feedforward network for the purpose of classifying foot-contact and foot-off events using the sagittal plane coordinates of heel and toe markers. The timing of events detected using this method was compared to the timing of events detected by measuring the ground reaction force using a force plate for a total of 40 pathologic subjects divided into two groups: barefoot and shod/braced.…”

Section: Discussionmentioning

confidence: 99%

“…Another notable aspect of neural networks is that they can analyze great amounts of gait data, as evidenced in the studies of Holzreiter & Kohle (1997) and Lafuente et al (1997). Other benefits to gait analysis include their inherent non-linear mapping ability, demonstrated aptitude at capturing temporal dependence (Savelberg & Herzog, 1997), and short processing time (Miller, 2009). Another advantage is the ability to capture patterns in the data within their internal parameters known as weights and biases which may influence gait patterns (Hinton, 1992).…”

Section: Gait Classification Using Annsmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Networks (ANN) Applied for Gait Classification and Physiotherapy Monitoring in Post Stroke Patients

Kaczmarczyk¹,

Wit²,

Krawczyk³

et al. 2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Gait Classification Using Annsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Networks (ANN) Applied for Gait Classification and Physiotherapy Monitoring in Post Stroke Patients

Kaczmarczyk¹,

Wit²,

Krawczyk³

et al. 2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

View full text Add to dashboard Cite

“…[11]. These studies normally involve methods based on sliding-window statistics [12], time-frequency analysis [13], wavelet decomposition [14], and/or ANN [15]. This class of methods is able to describe, to a certain extent, how the subject is walking.…”

Section: Related Workmentioning

confidence: 99%

A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry

Sant'Anna

Wickström

2010

IEEE Trans. Inform. Technol. Biomed.

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Abstract-Gait analysis can convey important information about one's physical and cognitive condition. Wearable inertial sensor systems can be used to continuously and unobtrusively assess gait during everyday activities in uncontrolled environments. An important step in the development of such systems is the processing and analysis of the sensor data. This paper presents a symbol-based method used to detect the phases of gait and convey important dynamic information from accelerometer signals. The addition of expert knowledge substitutes the need for supervised learning techniques, rendering the system easy to interpret and easy to improve incrementally. The proposed method is compared to an approach based on peak-detection. A new symbol-based symmetry index is created and compared to a traditional temporal symmetry index and a symmetry measure based on cross-correlation. The symbol-based symmetry index exemplifies how the proposed method can extract more information from the acceleration signal than previous approaches.

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“…[13]. These studies normally involve methods based on sliding-window statistics [14], time-frequency analysis [15], wavelet decomposition [16], and/or ANN [17]. This class of methods is able to describe, to a certain extent, how the subject is walking.…”

Section: Related Workmentioning

confidence: 99%

A Linguistic Approach to the Analysis of Accelerometer Data for Gait Analysis

Sant'Anna¹,

Wickström²

2010

Mechatronic Systems and Control

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There is evidence that many cognitive conditions affect the human motor system. Gait analysis has lately been used as a means of studying this physical-cognitive correlation. The development of gait analysis systems, able to record and analyze gait during normal daily activities and in uncontrolled environment, is an important addition to this area of research. Lately, linguistic approaches have been studied as means to achieve activity classification from vision sensors. The present work aims to extend the linguistic approach to achieve quantitative analysis of gait from accelerometer data. The proposed method can be used to extend the Human Activity Language framework to include the analysis of inertial sensors such as accelerometers. Results show that the proposed method is more accurate and robust than previous methods and can be used to extract a number of clinically relevant gait measurements. A novel symmetry index is presented to exemplify how the proposed method is able to extract more information from accelerometer signals than previous methods.

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Gait event detection using a multilayer neural network

Cited by 54 publications

References 11 publications

Artificial Neural Networks (ANN) Applied for Gait Classification and Physiotherapy Monitoring in Post Stroke Patients

Artificial Neural Networks (ANN) Applied for Gait Classification and Physiotherapy Monitoring in Post Stroke Patients

A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry

A Linguistic Approach to the Analysis of Accelerometer Data for Gait Analysis

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