A Deep Learning Strategy for Stride Detection

Koroglu, M. Taha; Yilmaz, Alper; Saul, Can Jozef

doi:10.1109/icsens.2018.8589889

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Thus it is possible to analyze the human gait independent of location. These sensors are often used to determine strides or activities [6][7][8][9][10][11][12][13][14][15] .…”

Section: Sensorsmentioning

confidence: 99%

“…To measure and store the time series of gait wearable containing microcontrollers in combination with IMU sensors are often used [6][7][8][9][10][11][16][17][18][19][20][21][22] . In most cases, the motion of the lower extremities is measured [6][7][8][9][10][11]22] . Thereby conclusions can be made about the stride length, cadence, stride duration, gait phases, and symmetry [6,10,11,23] .…”

Section: Symmetrymentioning

confidence: 99%

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Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Steinmetzer¹,

Wilberg²,

Bönninger³

et al. 2020

Microprocessors and Microsystems

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Gait deviations such as asymmetry are one of the characteristic symptoms of motor dysfunctions that contribute to the risk of falls. Our objective is to measure gait abnormalities such as asymmetry of the lower limbs in order to evaluate the diagnosis more objectively. For the measurement we use inertial measurement unit (IMU) sensors and force sensors, which are integrated in wristbands and insoles. To extend the battery life of wearable devices, we only save data of the activity gait within the wearables. Therefore we perform activity recognition with a smartphone. Using convolutional neural network (CNN) we achieved an accuracy of 94.7 % of the activity gait recognition. Before recording we synchronize the wearable sensors and reach a maximum latencies of 3 ms. Before the analysis of the symmetry we detect the strides by using a CNN with an accuracy of 98.8 %. For the symmetry evaluation we used dynamic time warping (DTW). The DTW enables us to calculate symmetry of the complete time series of human gait.

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“…Thus it is possible to analyze the human gait independent of location. These sensors are often used to determine strides or activities [6][7][8][9][10][11][12][13][14][15] .…”

Section: Sensorsmentioning

confidence: 99%

Section: Symmetrymentioning

confidence: 99%

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Steinmetzer¹,

Wilberg²,

Bönninger³

et al. 2020

Microprocessors and Microsystems

View full text Add to dashboard Cite

show abstract

“…Movement disorders that influence the patient's gait are measured with sensors such as gyroscopes, accelerometers, magnetometers, pressure sensors and image sensors. Accelerometers and gyroscopes are often used in combination with a magnetometer [12][13][14][15][16][17]. The sensors can be integrated into smartphones [14,18] or attached to the ankle [12,16,19] or body [15].…”

Section: Introductionmentioning

confidence: 99%

“…In [15], foot-and body-mounted IMUs are used for stride detection. Strides were labelled using a threshold.…”

Section: Introductionmentioning

confidence: 99%

Comparison of algorithms and classifiers for stride detection using wearables

Steinmetzer

Bönninger

Reckhardt

et al. 2019

Neural Comput & Applic

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Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min-max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson's disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown. Keywords Stride detection Á Gait analysis Á Inertial sensors Á Parkinson's disease Á Validation Á Dynamic time warping Á Time up and go test Á Convolutional neural networks & Tobias Steinmetzer

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Robust Foot Motion Recognition Using Stride Detection and Weak Supervision-Based Fast Labeling

Hua

Wang

2021

IEEE Sensors J.

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A Deep Learning Strategy for Stride Detection

Cited by 3 publications

References 11 publications

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Comparison of algorithms and classifiers for stride detection using wearables

Robust Foot Motion Recognition Using Stride Detection and Weak Supervision-Based Fast Labeling

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