Short-Term Motion Tracking Using Inexpensive Sensors

2015 ◽  
pp. 583-601
Author(s):  
Filip Matzner ◽  
Roman Barták
Keyword(s):  
Motion Tracking ◽  
Short Term

scholarly journals Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

Cloud motion tracking for short-term on-site cloud coverage prediction

2017 ◽  
Author(s):  
D. M. L. H. Dissawa ◽  
M. P. B. Ekanayake ◽  
G. M. R. I. Godaliyadda ◽  
J. B. Ekanayake ◽  
A. P. Agalgaonkar
Keyword(s):  
Motion Tracking ◽  
Short Term ◽  
Cloud Coverage ◽  
Cloud Motion ◽  
Coverage Prediction

scholarly journals Embodied virtual reality for the study of real-world motor learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245717
Author(s):  
Shlomi Haar ◽  
Guhan Sundar ◽  
A. Aldo Faisal
Keyword(s):  
Virtual Reality ◽  
Motor Learning ◽  
Visual Feedback ◽  
Real World ◽  
Motion Tracking ◽  
Body Motion ◽  
Controlled Environment ◽  
Short Term ◽  
Learning And Adaptation ◽  
Full Body

Motor-learning literature focuses on simple laboratory-tasks due to their controlled manner and the ease to apply manipulations to induce learning and adaptation. Recently, we introduced a billiards paradigm and demonstrated the feasibility of real-world-neuroscience using wearables for naturalistic full-body motion-tracking and mobile-brain-imaging. Here we developed an embodied virtual-reality (VR) environment to our real-world billiards paradigm, which allows to control the visual feedback for this complex real-world task, while maintaining sense of embodiment. The setup was validated by comparing real-world ball trajectories with the trajectories of the virtual balls, calculated by the physics engine. We then ran our short-term motor learning protocol in the embodied VR. Subjects played billiard shots when they held the physical cue and hit a physical ball on the table while seeing it all in VR. We found comparable short-term motor learning trends in the embodied VR to those we previously reported in the physical real-world task. Embodied VR can be used for learning real-world tasks in a highly controlled environment which enables applying visual manipulations, common in laboratory-tasks and rehabilitation, to a real-world full-body task. Embodied VR enables to manipulate feedback and apply perturbations to isolate and assess interactions between specific motor-learning components, thus enabling addressing the current questions of motor-learning in real-world tasks. Such a setup can potentially be used for rehabilitation, where VR is gaining popularity but the transfer to the real-world is currently limited, presumably, due to the lack of embodiment.

scholarly journals Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model—An Example of Forward Reaching

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 292
Author(s):  
Kai-Yu Chen ◽  
Li-Wei Chou ◽  
Hui-Min Lee ◽  
Shuenn-Tsong Young ◽  
Cheng-Hung Lin ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Short Term Memory ◽  
Human Motion ◽  
Image Features ◽  
3D Image ◽  
Image System ◽  
Short Term ◽  
Human Motion Tracking ◽  
Mechanism Model ◽  
Long Short Term Memory

Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.

Tracking System and Its Application in Unmanned Automobile Navigation Based on Sparse Photoelectric Sensor Network

2020 ◽  
Vol 15 (7) ◽  
pp. 799-809
Author(s):  
Yuanfei Xue
Keyword(s):  
Obstacle Avoidance ◽  
Motion Tracking ◽  
Tracking System ◽  
Control Circuit ◽  
Sensor Nodes ◽  
Infrared Sensor ◽  
Short Term ◽  
Short Term Test ◽  
Photoelectric Sensor

Sensor tracking technology has broad prospects of application in the fields of smart home and environmental protection. The passive motion tracking method of sensor networks can realize the perception of location, temperature and other information without carrying sensor nodes. A sparse network tracking system based on infrared sensor nodes is proposed in this study, which can control the running automobiles with unmanned navigation. On the basis of the theory of diffraction, the way of spreading for wireless received signal strength (RSS) can be divided into "scattered waves" and "diffracted waves," which can be regarded as two components of infrared sensing wireless signals so as to further propose the RSS indicators of "long-term testing value" and "short-term test value." Based on these indicators, a measurement model based on diffraction effects and scattering effects is proposed, and an improved particle filter algorithm is used to update the motion tracking. The hardware design of each module in an unmanned vehicle includes the main controller, tracking circuit, serial port circuit, motor control circuit and infrared sensor control circuit of the car. In the experiment, the measurement accuracy of the tracking system based on the sparse infrared photoelectric sensor was first tested. In the simulation experiment, the long-term test value, the short-term test value and the actual measurement value were compared respectively. The test results show that the theoretical RSS value and the actual test result can be matched. Moreover, the infrared photoelectric tracking system is used to design the navigation control system of unmanned cars, helping the car to drive automatically through obstacle avoidance test and tracking obstacle avoidance test.

scholarly journals Sky Image-Based Localized, Short-Term Solar Irradiance Forecasting for Multiple PV Sites via Cloud Motion Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Lasanthika H. Dissawa ◽  
Roshan I. Godaliyadda ◽  
Parakrama B. Ekanayake ◽  
Ashish P. Agalgaonkar ◽  
Duane Robinson ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Ground Level ◽  
Forecasting Model ◽  
Time Interval ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Cloud Motion ◽  
The Individual ◽  
Operational Issues

Power generation through solar photovoltaics has shown significant growth in recent years. However, high penetration of solar PV creates power system operational issues as a result of solar PV variability and uncertainty. Short-term PV variability mainly occurs due to the intermittency of cloud cover. Therefore, to mitigate the effects of PV variability, a sky-image-based, localized, global horizontal irradiance forecasting model was introduced considering the individual cloud motion, cloud thicknesses, and the elevations of clouds above the ground level. The proposed forecasting model works independently of any historical irradiance measurements. Two inexpensive sky camera systems were developed and placed in two different locations to obtain sky images for cloud tracking and cloud-based heights. Then, irradiance values for onsite and for a PV site located with a distance of 2 km from the main camera were forecasted for 1 minute, 5 minutes, and 15 minutes ahead of real-time. Results show that the three-level cloud categorization and the individual cloud movement tracking method introduced in this paper increase the forecasting accuracy. For partially cloudy and sunny days, the forecasting model for 15 min forecasting time interval achieved a positive skill factor concerning the persistent model. The accuracy of determining the correct irradiance state for a 1 min forecasting time interval using the proposed model is 81%. The average measures of RMSE, MAE, and SF obtained using the proposed method for 15 min forecasting time horizon are 101 Wm-2, 64 Wm-2, and 0.26, respectively. These forecasting accuracy levels are much higher than the other benchmarks considered in this paper.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Ultrastructural Changes of Canine Kidneys During 24-Hour Perfusion on a LI-400 Preservation System

1971 ◽  
Vol 29 ◽  
pp. 316-317
Author(s):  
M. O. Magnusson ◽  
D. G. Osborne ◽  
T. Shimoji ◽  
W. S. Kiser ◽  
W. A. Hawk
Keyword(s):  
Electron Microscopy ◽  
Electrolyte Solution ◽  
Ultrastructural Changes ◽  
Midline Incision ◽  
Short Term ◽  
Pentobarbital Anesthesia ◽  
Pulsatile Perfusion

Short term experimental and clinical preservation of kidneys is presently best accomplished by hypothermic continuous pulsatile perfusion with cryoprecipitated and millipore filtered plasma. This study was undertaken to observe ultrastructural changes occurring during 24-hour preservation using the above mentioned method.A kidney was removed through a midline incision from healthy mongrel dogs under pentobarbital anesthesia. The kidneys were flushed immediately after removal with chilled electrolyte solution and placed on a LI-400 preservation system and perfused at 8-10°C. Serial kidney biopsies were obtained at 0-½-1-2-4-8-16 and 24 hours of preservation. All biopsies were prepared for electron microscopy. At the end of the preservation period the kidneys were autografted.

Polychlorinated biphenyl induced hepatocyte alterations

1987 ◽  
Vol 45 ◽  
pp. 716-717
Author(s):  
D.N. Collins ◽  
J.N. Turner ◽  
K.O. Brosch ◽  
R.F. Seegal
Keyword(s):  
Lipid Droplets ◽  
Wistar Rats ◽  
Homovanillic Acid ◽  
Polychlorinated Biphenyl ◽  
Corn Oil ◽  
Environmental Pollutants ◽  
Short Term ◽  
Pcb Congeners ◽  
Urinary Levels ◽  
The Brain

Polychlorinated biphenyls (PCBs) are a ubiquitous class of environmental pollutants with toxic and hepatocellular effects, including accumulation of fat, proliferated smooth endoplasmic recticulum (SER), and concentric membrane arrays (CMAs) (1-3). The CMAs appear to be a membrane storage and degeneration organelle composed of a large number of concentric membrane layers usually surrounding one or more lipid droplets often with internalized membrane fragments (3). The present study documents liver alteration after a short term single dose exposure to PCBs with high chlorine content, and correlates them with reported animal weights and central nervous system (CNS) measures. In the brain PCB congeners were concentrated in particular regions (4) while catecholamine concentrations were decreased (4-6). Urinary levels of homovanillic acid a dopamine metabolite were evaluated (7).Wistar rats were gavaged with corn oil (6 controls), or with a 1:1 mixture of Aroclor 1254 and 1260 in corn oil at 500 or 1000 mg total PCB/kg (6 at each level).

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