Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations

Rueterbories, Jan; Spaich, Erika G.; Andersen, Ole Kæseler

doi:10.1016/j.medengphy.2013.10.004

Cited by 75 publications

(91 citation statements)

References 21 publications

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“…Accelerometers can be an alternative method to detect gait events. Depending on the nature of the algorithm and location of the accelerometer, latency and detection rate were found to be ranging between 138 ms and 586 ms and between 95% and 109.4% respectively [1,24,25]. However, accelerometer readings are affected by gravity and require exact placement of the accelerometer on human body segment to ensure consistent performances.…”

Section: Discussionmentioning

confidence: 99%

A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits

Gouwanda

Gopalai

2015

Medical Engineering & Physics

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Section: Discussionmentioning

confidence: 99%

A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits

Gouwanda

Gopalai

2015

Medical Engineering & Physics

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“…However, it is quite likely that external factors might disturb the original configuration during long-term analysis [28], and thus either the axis alignment should be checked and readjusted frequently or the exact orientation of the accelerometer must be known throughout, to compensate for the misalignment of the axes. An alternative is to analyze the magnitude of the resultant accelerometer signal instead which makes it invariant to individual axis alignment, as done in [4], [29]. While some methodologies instruct subjects to walk in a straight line or a given path at a selfselected pace [4], [13], [27], [29], others either pre-define a set of walking speeds or ask the subjects to walk slowly, normal and fast, in order to test the algorithmic robustness to different velocities [3], [14], [24]- [26], [28].…”

Section: Introductionmentioning

confidence: 99%

“…An alternative is to analyze the magnitude of the resultant accelerometer signal instead which makes it invariant to individual axis alignment, as done in [4], [29]. While some methodologies instruct subjects to walk in a straight line or a given path at a selfselected pace [4], [13], [27], [29], others either pre-define a set of walking speeds or ask the subjects to walk slowly, normal and fast, in order to test the algorithmic robustness to different velocities [3], [14], [24]- [26], [28]. A number of algorithms apply thresholds either to filtered accelerometer signals or use them at some intermediate stage after signal transformation, to perform peak detection for identifying events [25], [28], [29].…”

Section: Introductionmentioning

confidence: 99%

“…Since they are data-driven approaches that resemble a black-box model [26], not only might they be difficult to interpret by clinicians [30] but it also remains unclear whether and how often such a system would need to be retrained with changing scenarios. Other approaches include [4], where a rule-based state machine is realized with four gait states, namely, mid-stance, pre-swing, swing and loading response; and the state transitions are determined by five reference signals derived from tri-axial accelerometer signals. In recent years, wavelet transforms are being increasingly used for gait analysis [31] and in particular to detect gait events [12], [27], [32]- [35].…”

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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“…Gait event identification can be used to control the on/off time of functional electrical stimulation devices for drop foot correction in stroke patients [1,2,3,4], operate an active orthotic device for ankle foot pathologies [5], assess rehabilitation effects in post-stroke patients with gait abnormality [6], and classify daily activity to aid exercise for health care in the elderly [7]. Toe off (TO) and heel strike (HS) are two key gait events commonly used to distinguish a gait cycle into either swing phase or stance phase.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

Zhou

Samuel

et al. 2016

Sensors

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Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

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Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations

Cited by 75 publications

References 21 publications

A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits

A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits

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

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

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