Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion

Zhao, Hongyu; Wang, Zhelong; Qiu, Sen; Wang, Jiaxin; Xu, Fang; Wang, Zhengyu; Shen, Yanming

doi:10.1016/j.inffus.2019.03.002

Cited by 110 publications

(62 citation statements)

References 23 publications

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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%

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Hachaj

Piekarczyk

2019

Sensors

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The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.

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Section: Effective Methods Of Human Motion Analysis and Classificationmentioning

confidence: 99%

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Hachaj

Piekarczyk

2019

Sensors

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“…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%

Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits

Qiu

Wang

et al. 2020

Sensors

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Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.

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“…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%

Applications of MEMS Gyroscope for Human Gait Analysis

Zhao¹,

Qiu²,

Wang³

et al. 2020

Gyroscopes - Principles and Applications

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After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis.

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Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion

Cited by 110 publications

References 23 publications

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits

Applications of MEMS Gyroscope for Human Gait Analysis

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