Using a foot mounted accelerometer to detect changes in gait patterns

Patterson, M.R.; Caulfield, Brian

doi:10.1109/embc.2013.6611286

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Patterson and Caulfield used accelerometers to detect different gait conditions in people with normal and rigid ankles. ey used an algorithm that quantifies the relevant characteristics of the swing phase in the foot and found a clear distinction between gait patterns in the ankle movements [18].…”

Section: State Of the Artmentioning

confidence: 99%

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study

Sánchez-DelaCruz

Weber

Biswal

et al. 2019

Computational and Mathematical Methods in Medicine

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Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training process. Machine learning has revolutionized every research sector, including health care, by providing precise and accurate decisions involving minimal human interventions through pattern recognition. This is emphasized in this research, which addresses the issue of “support for diabetic neuropathy (DN) recognition.” DN is a disease that affects a large proportion of the global population. In this research, we have used gait biomarkers of subjects representing a particular sector of population located in southern Mexico to identify persons suffering from DN. To do this, we used a home-made body sensor network to capture raw data of the walking pattern of individuals with and without DN. The information was then processed using three sampling criteria and 23 assembled classifiers, in combination with a deep learning algorithm. The architecture of the best combination was chosen and reconfigured for better performance. The results revealed a highly acceptable classification with greater than 85% accuracy when using these combined approaches.

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Section: State Of the Artmentioning

confidence: 99%

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study

Sánchez-DelaCruz

Weber

Biswal

et al. 2019

Computational and Mathematical Methods in Medicine

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“…However, due to having features such as usability, portability, low cost, low power consumption, high sensitivity and small sizes, wearable sensors are commonly used in gait and other movement analyses research [1,6]. In gait research, wearable sensors have been mounted on feet/shoes [7][8][9][10][11], knees, thighs [12], ankles [5,13], shanks [14][15][16], chests [13] and waists [13,17] to analyse the gait parameters such as stride velocity, stride length, step length, cadence, step width, step angle, step time, swing time, stance time, gait phase, joint angles and momentum [6].…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies have used gyroscope data to track 'HS' and 'TO' in gait cycles from foot/shoe-mounted sensors [8,9,16,29,33] while others have used either accelerometry alone or both gyroscope and accelerometer data combined. With accelerometer data, MS [12], HS [10] and terminal contact (foot-off) [14] can be used to track gait cycles. When using both accelerometer and gyroscope data, HS, TO, MS and terminal stance can be used to track gait cycles [5,7].…”

Section: Introductionmentioning

confidence: 99%

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Jayasinghe

Hwang

Harwin

2022

Sensors

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A person’s walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg.

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“…In this article, we introduce a gait analysis approach for gait pattern examination with foot mounted accelerometers, which is suggested to be effective for gait monitoring [14]- [16]. We develop an information lossless algorithm to segment gait cycles for effective feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma-Specific Gait Pattern Assessment Using Body-Worn Sensors

Fallahzadeh²,

Ghasemzadeh³

2016

IEEE Sensors J.

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Many studies have reported that glaucoma patients experience mobility issues such as walking slowly and bumping into obstacles frequently. However, little is known to date about how a person's gait is impacted due to glaucoma. This article presents design and development of a gait analysis approach using a shoe-integrated sensing system and accompanying machine learning techniques to quantitatively examine gait patterns in glaucoma patients. The customized sensor platform is utilized in a clinical trial conducted with 9 glaucoma patients and 10 agematched healthy participants. The signal processing and machine learning algorithms automatically detect effective gait cycles and extract both steady-state and spatio-temporal gait features from the signal segments. We perform machine learning algorithms to distinguish glaucoma patients from healthy controls, and identify several prominent features with high discriminability between the two groups. The results demonstrate that classification algorithms can be used to identify gait patterns of glaucoma patients with an accuracy higher than 94% in a 10-meter-walk test. It is also demonstrated that gait features such as evenness of the sway speed along medio-lateral direction between the two feet are significantly different (p-value < 0.001) between older adults with and without glaucoma. These results suggest that emerging solutions, such as wearable sensing technologies, can be used for continuous and real-time assessment of gait and mobility problems in individuals with low vision, and may open new avenues for using changes in gait patterns for preventing life threatening situations such as falls.

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Using a foot mounted accelerometer to detect changes in gait patterns

Cited by 9 publications

References 22 publications

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

Glaucoma-Specific Gait Pattern Assessment Using Body-Worn Sensors

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