Gait Event Detection during Stair Walking Using a  Rate Gyroscope

Formento, Paola Andrea Lucia Catalfamo; Acevedo, Rubén; Ghoussayni, Salim; Ewins, David

doi:10.3390/s140305470

Cited by 64 publications

(52 citation statements)

References 38 publications

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“…Additionally, Formento et al adopted a gyroscope based heuristic algorithm for gait event detection and reported that two TO gait events were missed in 20 TO events on up stairs terrain. Furthermore, three TO events were missed while one was wrongly detected in a total of 27 TO events on down stairs terrain [30]. With this data, the F1 score for their method was also computed and reported in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…In their study, F1 scores of 0.96 and 0.76 for HS on up stairs and down stairs terrains were respectively obtained [2]. Formento et al proposed a rate gyroscope based heuristic algorithm for gait event detection, and recorded F1 scores of 0.95 and 0.93 for HS on up stairs and down stairs terrains respectively [30]. A major limitation of their study was that very few gait cycles were considered.…”

Section: Discussionmentioning

confidence: 99%

“…The maximum heel and toe FSR amplitudes were defined with respect to the FSR signal segments in the observation window and then the time instance of the correctly detected gait events of the proposed and FSR methods were compared. Additionally, the time of gait event detection was assessed for the proposed and FSR method using the absolute mean difference (AMD), mean difference (MD), and 95% confidence interval (CI) [30]. …”

Section: Methodsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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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“…A lot of studies have been devoted to the issue of determination of stress distributions in the femur equipped with various types of implants, and there are some papers in which loads affecting the hip joint in the conditions of movement were determined [1][2][3][4][5][6][7][8][9][10][11][12][13]. These loads were determined according to analytical and experimental methods.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Mechanical, and Material Factors on the Biological Adaptation Processes of the Femoral Bone Implants

Jasik

Mroczek

2016

Archives of Metallurgy and Materials

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Abstract. The study covers some aspects of the issue of determination of mutual connections between the mechanical and material factors, as well the biological implant adaptation processes. The main objective of the operation was adopted to develop models of cementless hip prosthesis company Fitmore Zimmer, taking into account the heterogeneity of material properties of bone tissue. These models were loaded in particular stages of the human gate and then they were used for the analysis of stress changes. The identification of the relations between the mechanical properties of osseous tissue required the conducting of computer simulations by means of the Finite element Method (FeM).

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“…Apart from fixed length segmentation, event-based segmentation techniques generally use a small fixed-length window to identify the events within one gait cycle (initial contact, flat foot or footoff) [16,17,18]. Chen et al extracted all peaks (mid-swing) from the signal and used the center of two consecutive peaks to identify the foot-flat segment [13].…”

Section: Introductionmentioning

confidence: 99%

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

Fida

Bibbo

Bernabucci

et al. 2015

Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare

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We propose an event-based dynamic segmentation technique for the classification of locomotion activities, able to detect the midswing, initial contact and end contact events. This technique is based on the use of a shank-mounted inertial sensor incorporating a tri-axial accelerometer and a tri-axial gyroscope, and it is tested on four different locomotion activities: walking, stair ascent, stair descent and running. Gyroscope data along one component are used to dynamically determine the window size for segmentation, and a number of features are then extracted from these segments. The event-based segmentation technique has been compared against three different fixed window size segmentations, in terms of classification accuracy on two different datasets, and with two different feature sets. The dynamic event-based segmentation showed an improvement in terms of accuracy of around 5% (97% vs. 92% and 92% vs. 87%) and 1-2% (89% vs. 87% and 97% vs. 96%) for the two dataset, respectively, thus confirming the need to incorporate an event-based criterion to increase performance in the classification of motion activities.

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Gait Event Detection during Stair Walking Using a Rate Gyroscope

Cited by 64 publications

References 38 publications

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

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

The Influence of Mechanical, and Material Factors on the Biological Adaptation Processes of the Femoral Bone Implants

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

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