scholarly journals Wearable Magnetic Induction-based Approach Toward 3D Motion Tracking

2021 ◽  
Author(s):  
Negar Golestani ◽  
Mahta Moghaddam
Keyword(s):  
Magnetic Induction ◽  
Motion Tracking ◽  
Mean Squared Error ◽  
Average Distance ◽  
Tracking System ◽  
Body Movement ◽  
The Body ◽  
Machine Learning Techniques ◽  
Wide Range ◽  
Battery Capacity

Abstract Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.

scholarly journals Wearable magnetic induction-based approach toward 3D motion tracking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Negar Golestani ◽  
Mahta Moghaddam
Keyword(s):  
Magnetic Induction ◽  
Motion Tracking ◽  
Mean Squared Error ◽  
Average Distance ◽  
Tracking System ◽  
Body Movement ◽  
The Body ◽  
Machine Learning Techniques ◽  
Wide Range ◽  
Battery Capacity

AbstractActivity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.

scholarly journals A flexible motion tracking system based on inertial sensors

2018 ◽  
Vol 198 ◽  
pp. 04010
Author(s):  
Zhonghao Han ◽  
Lei Hu ◽  
Na Guo ◽  
Biao Yang ◽  
Hongsheng Liu ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Data Transfer ◽  
Inertial Sensors ◽  
Tracking System ◽  
Calibration Procedure ◽  
Human Motion ◽  
The Body ◽  
Support Vector ◽  
Motion Data ◽  
Motion Tracking System

As a newly emerging human-computer interaction, motion tracking technology offers a way to extract human motion data. This paper presents a series of techniques to improve the flexibility of the motion tracking system based on the inertial measurement units (IMUs). First, we built a most miniatured wireless tracking node by integrating an IMU, a Wi-Fi module and a power supply. Then, the data transfer rate was optimized using an asynchronous query method. Finally, to simplify the setup and make the interchangeability of all nodes possible, we designed a calibration procedure and trained a support vector machine (SVM) model to determine the binding relation between the body segments and the tracking nodes after setup. The evaluations of the whole system justify the effectiveness of proposed methods and demonstrate its advantages compared to other commercial motion tracking system.

scholarly journals Cricket umpire assistance and ball tracking system using a single smartphone camera

2017 ◽  
Author(s):  
Udit Arora ◽  
Sohit Verma ◽  
Sarthak Sahni ◽  
Tushar Sharma
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Motion Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ball Tracking ◽  
Histogram Of Gradients

Several ball tracking algorithms have been reported in literature. However, most of them use high-quality video and multiple cameras, and the emphasis has been on coordinating the cameras or visualizing the tracking results. This paper aims to develop a system for assisting the umpire in the sport of Cricket in making decisions like detection of no-balls, wide-balls, leg before wicket and bouncers, with the help of a single smartphone camera. It involves the implementation of Computer Vision algorithms for object detection and motion tracking, as well as the integration of machine learning algorithms to optimize the results. Techniques like Histogram of Gradients (HOG) and Support Vector Machine (SVM) are used for object classification and recognition. Frame subtraction, minimum enclosing circle, and contour detection algorithms are optimized and used for the detection of a cricket ball. These algorithms are applied using the Open Source Python Library - OpenCV. Machine Learning techniques - Linear and Quadratic Regression are used to track and predict the motion of the ball. It also involves the use of open source Python library VPython for the visual representation of the results. The paper describes the design and structure for the approach undertaken in the system for analyzing and visualizing off-air low-quality cricket videos.

scholarly journals Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants

2021 ◽  
Vol 118 (43) ◽  
pp. e2104925118
Author(s):  
Hyoyoung Jeong ◽  
Sung Soo Kwak ◽  
Seokwoo Sohn ◽  
Jong Yoon Lee ◽  
Young Joong Lee ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
The Body ◽  
Therapeutic Interventions ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Integrated Sensor ◽  
Full Body

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

Stereoscopic Motion Tracking System

2011 ◽  
Author(s):  
A. Calderon ◽  
M. Dembele ◽  
B. Hossain ◽  
Y. Noor ◽  
S. Ovsiew
Keyword(s):  
Cerebral Palsy ◽  
Physical Therapy ◽  
Neurological Disorders ◽  
Motion Tracking ◽  
Speech Therapy ◽  
Tracking System ◽  
Body Movement ◽  
Muscle Coordination ◽  
Specific Muscle ◽  
Motion Tracking System

The “National Institute of Neurological Disorders and Stroke” defines Cerebral Palsy as a neurological disorder that affects body movement and muscle coordination. This condition usually appears at birth or during the first three years of life [3]. Treatment for children with Cerebral Palsy is extensive and can include any or all of the following: physical/occupational therapy, speech therapy, medicine, surgery, and orthopedic devices. Physical therapy involves having the child perform several repetitions of a set of exercises that will target the specific muscle group that needs to be worked on. A technique that has recently been employed in physical therapy is the use of video games [2], this allows the therapist to have the child perform similar sets of exercises while at the same time motivate and entertain the child.

Robust Inertial Motion Tracking through Deep Sensor Fusion across Smart Earbuds and Smartphone

2021 ◽  
Vol 5 (2) ◽  
pp. 1-26
Author(s):  
Jian Gong ◽  
Xinyu Zhang ◽  
Yuanjun Huang ◽  
Ju Ren ◽  
Yaoxue Zhang
Keyword(s):  
Sensor Fusion ◽  
Motion Tracking ◽  
Large Scale ◽  
Body Movement ◽  
Motion Artifacts ◽  
Indoor Navigation ◽  
The Body ◽  
Data Driven ◽  
Whole Body ◽  
Attention Model

IMU based inertial tracking plays an indispensable role in many mobility centric tasks, such as robotic control, indoor navigation and virtual reality gaming. Despite its mature application in rigid machine mobility (e.g., robot and aircraft), tracking human users via mobile devices remains a fundamental challenge due to the intractable gait/posture patterns. Recent data-driven models have tackled sensor drifting, one key issue that plagues inertial tracking. However, these systems still assume the devices are held or attached to the user body with a relatively fixed posture. In practice, natural body activities may rotate/translate the device which may be mistaken as whole body movement. Such motion artifacts remain as the dominating factor that fails existing inertial tracing systems in practical uncontrolled settings. Inspired by the observation that human heads induces far less intensive movement relative to the body during walking, compared to other parts, we propose a novel multi-stage sensor fusion pipeline called DeepIT, which realizes inertial tracking by synthesizing the IMU measurements from a smartphone and an associated earbud. DeepIT introduces a data-driven reliability aware attention model, which assesses the reliability of each IMU and opportunistically synthesizes their data to mitigate the impacts of motion noise. Furthermore, DeepIT uses a reliability aware magnetometer compensation scheme to combat the angular drifting problem caused by unrestricted motion artifacts. We validate DeepIT on the first large-scale inertial navigation dataset involving both smartphone and earbud IMUs. The evaluation results show that DeepIT achieves multiple folds of accuracy improvement on the challenging uncontrolled natural walking scenarios, compared with state-of-the-art closed-form and data-driven models.

Key Points of Human Body Location Based on Single Depth Map

2012 ◽  
Vol 203 ◽  
pp. 76-82
Author(s):  
Hai Hu ◽  
Bin Li ◽  
Ben Xiong Huang ◽  
Xiao Lei He
Keyword(s):  
Human Body ◽  
Motion Tracking ◽  
Tracking System ◽  
Depth Map ◽  
Human Motion ◽  
The Body ◽  
Body Region ◽  
Automatic Initialization ◽  
Key Points ◽  
Background Segmentation

This paper presents a method of using single depth map to locate the key points of frontal human body. Human motion capture is the premise of motion analysis and understanding, and it has widely application prospects. There are many problems on former way to capture the state of human motion. For example, it can’t initialize automatically, it can not recover from tracking failure, it can not solve the problem caused by occlusion, or there are many constraints on participant, and so on. This article uses Kinect, which from Microsoft, to get depth maps, and use a single map as input to locate the key points of human body. First, depth map can reflect the distance, so background segmentation can be done easily by the characteristic. Then, extract the skeleton of the body’s silhouette. Finally, using the inherent connectivity features of human body, the key points of the body can be determined on the skeleton. Locating the key points from single depth map solve the problem of automatic initialization and recovery directly. The depth map can reflect distance on grayscale, which makes it easy to split the body region from the background. In addition, depth map contains some useful information can be used to solve the problem of occlusion. Using depth map can remove some constraints on the human body, as well as to reduce the influence of clothing and surround lighting, and so on. The experiment shows that this method is very accurate in locating the key points of frontal stand human body, and can solve some problems of occlusion. It is ideal used in a motion tracking system for automatic initialization and self-recovery when tracking failed

scholarly journals Cricket umpire assistance and ball tracking system using a single smartphone camera

2017 ◽  
Author(s):  
Udit Arora ◽  
Sohit Verma ◽  
Sarthak Sahni ◽  
Tushar Sharma
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Motion Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ball Tracking ◽  
Histogram Of Gradients

Several ball tracking algorithms have been reported in literature. However, most of them use high-quality video and multiple cameras, and the emphasis has been on coordinating the cameras or visualizing the tracking results. This paper aims to develop a system for assisting the umpire in the sport of Cricket in making decisions like detection of no-balls, wide-balls, leg before wicket and bouncers, with the help of a single smartphone camera. It involves the implementation of Computer Vision algorithms for object detection and motion tracking, as well as the integration of machine learning algorithms to optimize the results. Techniques like Histogram of Gradients (HOG) and Support Vector Machine (SVM) are used for object classification and recognition. Frame subtraction, minimum enclosing circle, and contour detection algorithms are optimized and used for the detection of a cricket ball. These algorithms are applied using the Open Source Python Library - OpenCV. Machine Learning techniques - Linear and Quadratic Regression are used to track and predict the motion of the ball. It also involves the use of open source Python library VPython for the visual representation of the results. The paper describes the design and structure for the approach undertaken in the system for analyzing and visualizing off-air low-quality cricket videos.

Creating the Wind

2020 ◽  
Vol 2 (4) ◽  
pp. 14-31
Author(s):  
Élodie Dupey García
Keyword(s):  
Indigenous Communities ◽  
The Body ◽  
Historical Sources ◽  
Natural Behavior ◽  
Sensory Experiences ◽  
Identity And Agency ◽  
Wide Range ◽  
Late Postclassic ◽  
Comparative Examination ◽  
Meteorological Phenomenon

This article explores how the Nahua of late Postclassic Mesoamerica (1200–1521 CE) created living and material embodiments of their wind god constructed on the basis of sensory experiences that shaped their conception of this divinized meteorological phenomenon. In this process, they employed chromatic and design devices, based on a wide range of natural elements, to add several layers of meaning to the human, painted, and sculpted supports dressed in the god’s insignia. Through a comparative examination of pre-Columbian visual production—especially codices and sculptures—historical sources mainly written in Nahuatl during the viceregal period, and ethnographic data on indigenous communities in modern Mexico, my analysis targets the body paint and shell jewelry of the anthropomorphic “images” of the wind god, along with the Feathered Serpent and the monkey-inspired embodiments of the deity. This study identifies the centrality of other human senses beyond sight in the conception of the wind god and the making of its earthly manifestations. Constructing these deity “images” was tantamount to creating the wind because they were intended to be visual replicas of the wind’s natural behavior. At the same time, they referred to the identity and agency of the wind god in myths and rituals.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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