2019
DOI: 10.1016/j.inffus.2019.03.002
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Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion

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Cited by 114 publications
(65 citation statements)
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“…Sometimes an input raw signal is processed by convolution layer followed by recurrent network to avoid a sliding window design and then classified by a fully connected neural network [17]. In situations when appropriate motions features are already present an ANN can be applied directly [18,19]. In some research, authors also utilize DTW [20].…”
Section: Effective Methods Of Human Motion Analysis and Classificationmentioning
confidence: 99%
“…Sometimes an input raw signal is processed by convolution layer followed by recurrent network to avoid a sliding window design and then classified by a fully connected neural network [17]. In situations when appropriate motions features are already present an ANN can be applied directly [18,19]. In some research, authors also utilize DTW [20].…”
Section: Effective Methods Of Human Motion Analysis and Classificationmentioning
confidence: 99%
“…To obtain a smooth motion trajectory, some researchers even introduced smoothers for position estimation [39]; these seem to work well, but a delay would inevitably be introduced, which should be avoided, especially in real-time scenes. In addition, the real effects of smoothers on motion capture research are still disputable [40,41].…”
Section: • Gait Symmetrymentioning
confidence: 99%
“…The other type of inertial sensors used in our study is the ADIS16448 iSensor ® device [35][36][37], which combines industry-leading iMEMS ® technology with signal conditioning that optimizes dynamic performance and costs about $600. The ADIS16448 is packaged in a module that has a standard connector interface, as illustrated in Figure 11 that depicts the data acquisition process in a physical therapy and rehabilitation department of a public hospital.…”
Section: Analog Devices Imumentioning
confidence: 99%
“…Generally, these hybrid methods have better performance than the pure HMMs when dealing with high-dimensional data. Inspired by the existing methods, an adaptive hybrid method is presented in our previous study [36], by modeling human gait with a left-right HMM and employing a three-layer neural network (NN) to deal with the raw measurements.…”
Section: Machine Learning-based Gait Detectionmentioning
confidence: 99%