Estimate spatial-temporal parameters of human gait using inertial sensors

Wang, Zhelong; Ji, Ran

doi:10.1109/cyber.2015.7288234

Cited by 30 publications

(21 citation statements)

References 12 publications

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“…[17,23]. Especially for spatial parameters, inertial sensors (accelerometers) can measure them by integral computation, but as we know, inertial sensors have chipset drift problems and integral computation accumulated errors, and this error is corrected by a zero velocity update.…”

Section: Discussionmentioning

confidence: 99%

“…As we know, a normal gait cycle includes a stance phase (60%) and a swing phase (40%) [17], and only in the stance phase, will the foot contact the ground and generate a walking sound impact. A footstep sound impact consists of several sub-impacts which are caused by different parts of one’s foot, e.g., the calcaneus touching the ground, metatarso-phalangeal ground touching and the phalanges touching the ground, etc.…”

Section: The Gait Temporal Parameter Estimation Algorithmmentioning

confidence: 99%

“…Inertial sensors, which can be embedded in wearable and portable electronic devices, have been widely used in gait recognition for individual identification [11,12], gait action recognition [13] and gait analysis [14,15,16,17] because they are inexpensive, not limited by environmental conditions, low-power and tiny. Most wearable GA methods only use inertial sensor-based methods [14,15,16,17]. The temporal parameters of inertial sensor-based methods are mainly measured by threshold-based peak detection methods, so generally it is not easy to achieve high detection accuracy because it’s almost impossible to find a fixed threshold adaptable to many kinds of conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The temporal parameters of inertial sensor-based methods are mainly measured by threshold-based peak detection methods, so generally it is not easy to achieve high detection accuracy because it’s almost impossible to find a fixed threshold adaptable to many kinds of conditions. In addition, as we know that inertial sensors have chipset drift problems and integral computation accumulated errors [17], thus requiring a lot of work and processing to reduce the noises in the signals recorded by them.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

Wang

Zhang

et al. 2016

Sensors

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Most existing wearable gait analysis methods focus on the analysis of data obtained from inertial sensors. This paper proposes a novel, low-cost, wireless and wearable gait analysis system which uses microphone sensors to collect footstep sound signals during walking. This is the first time a microphone sensor is used as a wearable gait analysis device as far as we know. Based on this system, a gait analysis algorithm for estimating the temporal parameters of gait is presented. The algorithm fully uses the fusion of two feet footstep sound signals and includes three stages: footstep detection, heel-strike event and toe-on event detection, and calculation of gait temporal parameters. Experimental results show that with a total of 240 data sequences and 1732 steps collected using three different gait data collection strategies from 15 healthy subjects, the proposed system achieves an average 0.955 F1-measure for footstep detection, an average 94.52% accuracy rate for heel-strike detection and 94.25% accuracy rate for toe-on detection. Using these detection results, nine temporal related gait parameters are calculated and these parameters are consistent with their corresponding normal gait temporal parameters and labeled data calculation results. The results verify the effectiveness of our proposed system and algorithm for temporal gait parameter estimation.

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Section: Discussionmentioning

confidence: 99%

Section: The Gait Temporal Parameter Estimation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

Wang

Zhang

et al. 2016

Sensors

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“…Generally, this kind of errors are corrected and revised by a zero velocity update (ZVU). Current detection methods of zero velocity are mainly determined by gait temporal parameters [22,23]. Hence the accuracy of spatial parameters measurement is hard to get high.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Spatial-Temporal Gait Parameters based on the Fusion of Inertial and Film-Pressure Signals

Wang

Zhang

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Gait analysis (GA) has been widely used in physical activity monitoring and clinical contexts, and the estimation of the spatial-temporal gait parameters is of primary importance for GA. With the quick development of smart tiny sensors, GA methods based on wearable devices have become more popular recently. However, most existing wearable GA methods focus on data analysis from inertial sensors. In this paper, we firstly present a two-foot-worn in-shoe system (Gaitboter) based on low-cost, wearable and multimodal sensors' fusion for GA, comprising an inertial sensor and eight film-pressure sensors with each foot for gait raw data collection while walking. Secondly, a GA algorithm for estimating the spatial-temporal parameters of gait is proposed. The algorithm fully uses the fusion of two kinds of sensors' signals: inertial sensor and film-pressure sensor, in order to estimate the spatialtemporal gait parameters, such as stance phase, swing phase, double stance phase, cadence, step time, stride time, stride length, velocity. Finally, to verify the effectiveness of this system and algorithm of the paper, an experiment is performed with 27 stoke patients from local hospital that the spatial-temporal gait parameters obtained with the system and the algorithm are compared with a GA tool used in medical laboratories. And the experimental results show that it achieves very good consistency and correlation between the proposed system and the compared GA tool.

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A survey of step length estimation models based on inertial sensors for indoor navigation systems

Soni

Trapasiya²

2021

Int J Communication

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Summary In many applications (i.e., navigation of vehicles in traffic, museum guide, guidance to firefighters in poor visibility, and elderly care assistance systems), location‐based services (LBS) rely on the global positioning system (GPS) for accurate positioning. LBS can be challenging to employ in an indoor environment because of the lack of a line‐of‐sight (LOS) component and other signal propagation issues. Over the last decade, researchers worldwide have been motivated to solve the problem of continuous indoor positioning in situations when GPS is ineffective. We thoroughly reviewed research conducted by many scholars and compiled this survey article, which provides a comprehensive overview of indoor pedestrian navigation systems, prevalent step length estimation models, and the current state of the art. There are two key strategies in which substantial work on indoor localization is carried out. One is a dead reckoning in which the present location of the pedestrian is calculated based on details about previous location, and the other is an inertial navigation system in which the forward acceleration from an inertial sensor strapped down to the user's body is integrated twice to establish the current location. The step length is a crucial component of the inertial sensor‐based navigation in both these strategies. For different users, step length is variable and also changes at different walking speeds for each user. As a result, the focus of this survey is on step length estimation models. The survey describes and compares various approaches to estimating step length from various perspectives, including the research method used, the length of the test path, various walking speeds, the location of the sensor device on the user's body, and the accuracy achieved in estimating step length. Because inertial sensors are embedded in smartphones, it is vital to identify the smartphone mode (i.e., phoning, texting, or pocket) during each step taken while using smartphone‐based indoor navigation. The report concludes with a quantitative evaluation of the performance of smartphone mode recognition modules. At the end, we list out challenges still to be addressed for scholars working on indoor navigation.

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Estimate spatial-temporal parameters of human gait using inertial sensors

Cited by 30 publications

References 12 publications

Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

Estimation of Spatial-Temporal Gait Parameters based on the Fusion of Inertial and Film-Pressure Signals

A survey of step length estimation models based on inertial sensors for indoor navigation systems

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