Stance Phase Detection for Walking and Running Using an IMU Periodicity-based Approach

2015 ◽  
pp. 225-232 ◽  
Author(s):  
Yang Zhao ◽  
Markus Brahms ◽  
David Gerhard ◽  
John Barden
Keyword(s):  
Stance Phase ◽  
Phase Detection

scholarly journals ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Covariance Matrix ◽  
Stance Phase ◽  
Phase Detector ◽  
Vertical Position ◽  
Navigation Systems ◽  
Phase Detection ◽  
Zero Velocity ◽  
Position Errors ◽  
Series Of Experiments ◽  
Navigation Accuracy

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

scholarly journals A Neural Network for Stance Phase Detection in Smart Cane Users

2019 ◽  
pp. 310-321
Author(s):  
Juan Rafael Caro-Romero ◽  
Joaquin Ballesteros ◽  
Francisco Garcia-Lagos ◽  
Cristina Urdiales ◽  
Francisco Sandoval
Keyword(s):  
Neural Network ◽  
Stance Phase ◽  
Phase Detection

Soft Wearable Augmented Walking Suit With Pneumatic Gel Muscles and Stance Phase Detection System to Assist Gait

2018 ◽  
Vol 3 (4) ◽  
pp. 4257-4264 ◽  
Author(s):  
Chetan Thakur ◽  
Kazunori Ogawa ◽  
Toshio Tsuji ◽  
Yuichi Kurita
Keyword(s):  
Stance Phase ◽  
Detection System ◽  
Phase Detection

Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System

2015 ◽  
Vol 20 (6) ◽  
pp. 3170-3181 ◽  
Author(s):  
Zhelong Wang ◽  
Hongyu Zhao ◽  
Sen Qiu ◽  
Qin Gao
Keyword(s):  
Navigation System ◽  
Stance Phase ◽  
Phase Detection ◽  
Pedestrian Navigation

scholarly journals LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning

Micromachines ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 458 ◽  
Author(s):  
Wei Yang ◽  
Chundi Xiu ◽  
Jiarui Ye ◽  
Zhixing Lin ◽  
Haisong Wei ◽  
...  
Keyword(s):  
Large Scale ◽  
Stance Phase ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Survey Method ◽  
Phase Detection ◽  
Time Consumption ◽  
Site Survey ◽  
Radio Map

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

scholarly journals ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Covariance Matrix ◽  
Stance Phase ◽  
Phase Detector ◽  
Vertical Position ◽  
Navigation Systems ◽  
Phase Detection ◽  
Zero Velocity ◽  
Position Errors ◽  
Series Of Experiments ◽  
Navigation Accuracy

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

Sign in / Sign up

Export Citation Format

Share Document