Classification Of Walking Patterns Research Articles

Pedestrian network generation based on crowdsourced tracking data

ABSTRACT Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.

Comparison of Human Jogging and Walking Patterns Using Statistical Tabular, Scatter Distribution and Artificial Classifier

This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. Three methods were proposed to differentiate and separate both of the jogging and walking data set, they are statistical tabular, scatter distribution and artificial classifier recognition. Linear decision boundary and radial basis function kernel (RBF) were proposed to perform the separation works for artificial classifier recognition section. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum RBF kernel for jogging and walking pattern classification. RBF used in this study was successfully differentiate and classify the jogging and walking patterns of a human arm movement during performing these activities.

Walking Pattern Classification and Walking Distance Estimation Algorithms Using Gait Phase Information

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.

Toward the enhancement of biped locomotion and control techniques: walking pattern classification

A new walking pattern classification method is proposed for a 5-link 7-DOF biped robot walking on an uneven floor. This method extracts the patterns in the current floor position of the stance foot and the transitioning floor conditions of the swing foot during locomotion. When a global path composed of stairs, obstacles, etc., and certain walking parameters, such as the speed of walking and the total walking time, are put into the system, the guidance controller unit determines the trajectory of the footsteps in terms of step patterns by using a genetic algorithm-based optimization technique while ensuring the biped’s stability criterion. A demonstration of the biped with different pattern classes was realized by a dynamic simulator.

가속도 신호를 이용한 걸음걸이 패턴 분류

걸음걸이 패턴 분류는 많은 응용분야가 있을 뿐만 아니라 매우 중요한 연구 분야이다. 따라서 본 연구에서는 허리에 부착된 가속도 모듈로부터 획득된 신호를 이용하여 천천히 걷기(slow walking, S.W), 일반 걷기(normal walking, N.W), 빠르게 걷기(fast walking, F.W) 등의 보행 패턴을 분류하고자한다. 11명의 성인으로부터 블루트스 모듈을 이용하여 100Hz로 샘플링된 3축 가속도 신호를 획득하였다. 획득된 데이터는 웨이브렛 변환을 이용하여 분석하였다. 걸음걸이 패턴은 두가지의 파라미터들을 이용하여 분석되어지는데, 하나는 운동에 해당하는 웨이브렛 계수의 에너지(power)와 전 후방향의 전체 에너지사이의 비율(RPA)이고, 다른 파라미터는 전 후 방향과 상 하 수직 방향 사이에서 웨이브렛 계수의 제곱근 평균 비율(RAV)이다. 천천히 걷기는 다른 걷기와 비교했을 때 작은 RPA값을 가지게 되어 분류가 용이하며, 천천히 걷기는 RAV를 이용하여 일반 걷기와 구별되어 질 수 있었다. 따라서 본연구는 건강한 성인에게서 3축 가속도 신호를 획득한 후 웨이브렛 파라미터를 이용하여 걷기 패턴을 잘 구별할 수 있는 연구임을 확인 하였다. This classification of walking patterns is important and many kinds of applications. Therefore, we attempted to classify walking on level ground from slow walking to fast walking using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by bluetooth module at a sampling rate of 100 Hz eleven healthy. The data were analyzed using discrete wavelet transform. Walking patterns were classified using two parameters; One was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction(RAV). Slow walking could be distinguished by the smallest value in RPA from other walking pattern. Fast walking could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signal in healthy people.

Portable Activity Monitoring System for Temporal Parameters of Gait Cycles

A portable and wireless activity monitoring system was developed for the estimation of temporal gait parameters. The new system was built using three-axis accelerometers to automatically detect walking steps with various walking speeds. The accuracy of walking step-peak detection algorithm was assessed by using a running machine with variable speeds. To assess the consistency of gait parameter analysis system, estimated parameters, such as heel-contact and toe-off time based on accelerometers and footswitches were compared for consecutive 20 steps from 19 individual healthy subjects. Accelerometers and footswitches had high consistency in the temporal gait parameters. The stance, swing, single-limb support, and double-limb support time of gait cycle revealed ICCs values of 0.95, 0.93, 0.86, and 0.75 on the right and 0.96, 0.86, 0.93, 0.84 on the left, respectively. And the walking step-peak detection accuracy was 99.15% (±0.007) for the proposed method compared to 87.48% (±0.033) for a pedometer. Therefore, the proposed activity monitoring system proved to be a reliable and useful tool for identification of temporal gait parameters and walking pattern classification.

Extracting a diagnostic gait signature

This research addresses the question of the existence of prominent diagnostic signatures for human walking extracted from kinematics gait data. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.