Adaptive method for real-time gait phase detection based on ground contact forces

Yu, Lian; Zheng, Jianbin; Wang, Yan; Song, Zhengge; Zhan, Enqi

doi:10.1016/j.gaitpost.2014.10.019

Cited by 40 publications

(35 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A gait or motion capture laboratory, equipped with force plates or instrumented treadmills and cameras, is the “gold standard” [ 5 , 13 , 14 , 15 ] for providing complete biomechanical analysis of the spatiotemporal, kinematic and kinetic parameters of gait, of which, gait event detection (GED) is a necessary component. Gait laboratories, however, require sophisticated motion capture systems, a well-trained team, abundant time and resources for analysis [ 10 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

Zahradka

Verma

Behboodi

et al. 2020

Sensors

View full text Add to dashboard Cite

Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to ‘gold standard’ force plate methods (GS) for determining IC and TC in adults (n = 6), typically developing children (n = 5) and children with cerebral palsy (n = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: −9.54 ± 0.66 ms, SK: −33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.

show abstract

Section: Introductionmentioning

confidence: 99%

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

Zahradka

Verma

Behboodi

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The marking points should be worn in specific positions on the human body according to the principles of human anatomy. Although optical gait analysis technology has achieved the highest-recognized accuracy, it must rely on the existence of indoor infrastructure, and the operation process is normally time consuming and labor intensive [14,15]. In addition, the laboratory environment does not reflect patients' real living conditions and mobility needs.…”

Section: Related Workmentioning

confidence: 99%

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

Qiu

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…The extracted features consist of the mean and variance of the acceleration, as shown in Eqs. (5) and (6). Mean:…”

Section: Data Processingmentioning

confidence: 99%

“…However, they have limited application space and can only be used indoors. (5) Lim et al used an electromyographic (EMG) sensor to collect data on human muscle activity and identified up to four gait stages. (6) However, the sensor requires a complex and specific mounting method, because it requires direct contact with the skin and is susceptible to sweat between the skin and the sensor, which reduces the accuracy of data collection.…”

Section: Introductionmentioning

confidence: 99%

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

View full text Add to dashboard Cite

Keywords: gait phase detection, inertial sensor, information fusion, new hidden Markov modelGait phase detection is important in the field of motion analysis and exoskeleton-assisted walking so that the accurate control of exoskeleton robots can be achieved. Therefore, to obtain accurate motion gait information and ensure good detection accuracy of the gait phase, in this study, a new hidden Markov model (N-HMM) algorithm is proposed to improve the accuracy of gait phase detection. A multisensor gait data acquisition system was developed to determine the acceleration and plantar pressure of the human body. Data were collected from 10 healthy subjects and sensors were attached to the subjects' legs and feet to detect the motion gait phase. A comparison of results with hidden Markov model (HMM) algorithms shows that the proposed algorithm improves the recall and precision rates by 3 and 3.5%, respectively. The N-HMM was used for gait phase detection and the detection accuracy of the N-HMM was compared with that of the HMM, support vector machine (SVM), decision tree, and back propagation (BP) network algorithms. The average accuracy of the N-HMM was 96.2%, outperforming all other algorithms. The results show that the N-HMM is capable of detecting the human gait phase with high accuracy. The results of this study provide a theoretical basis for the design and control of exoskeleton robots.

show abstract

Adaptive method for real-time gait phase detection based on ground contact forces

Cited by 40 publications

References 19 publications

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

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

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

Contact Info

Product

Resources

About