Tracking People Using Ankle-Level 2D LiDAR for Gait Analysis

Hasan, Mahmudul; Hanawa, Junichi; Goto, Riku; Fukuda, Hisato; Kuno, Yoshinori; Kobayashi, Yoshinori

doi:10.1007/978-3-030-51328-3_7

Cited by 11 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A foot-mounted sensor, for instance, can serve to localize a moving person or robot and generate a map of the indoor space while exploiting odometry. Low-cost 2D LiDAR has been recommended to preserve user’s privacy in human tracking ( Hasan et al, 2020 ) (See Table 7 ).…”

Section: Tracking Human Posementioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

View full text Add to dashboard Cite

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

show abstract

Section: Tracking Human Posementioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

View full text Add to dashboard Cite

show abstract

“…Localization is one of the key technologies for close-contact detection. Many researchers have proposed various methods using different types of devices such as radio frequency (RF) [15], light detection and ranging (LiDAR) [16], [17], and cameras [18]- [21]. Bluetooth and Wi-Fi are widely used for close-contact tracing owing to the wide availability of smartphones.…”

Section: A Localizationmentioning

confidence: 99%

“…Refs. [16], [17] proposed target localization using LiDAR fixed in the target environment. However, we need to deploy LiDAR while incurring deployment cost although it can localize and track targets accurately.…”

Section: A Localizationmentioning

confidence: 99%

Close-Contact Detection Using a Single Camera for Sports Considering Occlusion

et al. 2022

View full text Add to dashboard Cite

The Coronavirus disease 2019 (COVID-19) is still prevalent in the world. Exercise is important to maintain our health while dealing with infectious diseases. Social distancing is more important during exercise because we may not be able to wear masks to avoid breathing problems, heatstroke, etc. To maintain social distancing during exercise, we develop a close-contact detection system using a single camera especially for sports in schools and gyms. We rely on a single camera because of the deployment cost. The system recognizes people from a video and estimates the interpersonal distance for close-contact detection. The challenge is the occlusion of people, which leads to false negatives in close-contact detection.To solve the problem, we leverage the observation that most false negatives in human detection are caused by occlusion owing to other people. This is because there are few obstacles in sports facilities. Based on the above observation, we assume that a person still exists near the last detected position even when s/he disappears in the proximity of other people. For evaluation, we recorded 834 videos that were 112 min long in total including various scenarios with 2724 close-contacts. The results show that the F1-score of closecontact detection and tracking are 83.6% and 67.3%, respectively. We also confirmed that the start and end time errors are within 1 s for more than 80% of the close-contacts.

show abstract

“…In our previous work [16], we tried to establish a LiDARbased tracking system that can track pedestrians properly. We used the DBSCAN algorithm for clustering the data and KF to track a person's path.…”

Section: Related Work In Cluster-based Trackingmentioning

confidence: 99%

Person Tracking Using Ankle‐Level LiDAR Based on Enhanced DBSCAN and OPTICS

Hasan

Hanawa

Goto

et al. 2021

IEEJ Transactions Elec Engng

Self Cite

View full text Add to dashboard Cite

Along with the progress of deep learning techniques, people tracking using video cameras became easy and accurate. However, privacy and security issues are not enough to be concerned with vision-based monitoring. People may not be tolerated surveillance cameras installed everywhere in our daily life. A camera-based system may not work robustly in unusual situations such as smoke, fogs, or darkness. To cope with these problems, we propose a two-dimensional (2D) LiDAR-based people tracking technique based on clustering algorithms. A LiDAR sensor is a prominent approach for tracking people without disclosing their identity, even under challenging conditions. For tracking people, we propose modified density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify cluster structure (OPTICS) algorithms for clustering 2D LiDAR data. We have confirmed that our approach significantly improves the accuracy and robustness of people tracking through the experiments.

show abstract

Tracking People Using Ankle-Level 2D LiDAR for Gait Analysis

Cited by 11 publications

References 13 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

Close-Contact Detection Using a Single Camera for Sports Considering Occlusion

Person Tracking Using Ankle‐Level LiDAR Based on Enhanced DBSCAN and OPTICS

Contact Info

Product

Resources

About