A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.

/pdf/the-recognition-of-human-movement-using-temporal-templates-5e8eze1cen.pdf

The recognition of human movement using temporal templates

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Human Motion Analysis

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

In Art as Experience, Dewey brings together the deep and enduring connection between art and human experience. Art is understood not as a commodity or solitary object but as an intensely meaningful...

Art as experience

Psychological studies indicate that people have a small but statistically significant ability to recognize the gaits of individuals that they know. Recently, there has been much interest in machine vision systems that can duplicate and improve upon this human ability for application to biometric identification. While gait has several attractive properties as a biometric (it is unobtrusive and can be done with simple instrumentation), there are several confounding factors such as variations due to footwear, terrain, fatigue, injury, and passage of time. This paper gives an overview of the factors that affect both human and machine recognition of gaits, data used in gait and motion analysis, evaluation methods, existing gait and quasi gait recognition systems, and uses of gait analysis beyond biometric identification. We compare the reported recognition rates as a function of sample size for several published gait recognition systems.

/pdf/biometric-gait-recognition-4dsrpkufhl.pdf

Biometric gait recognition

This paper describes SwarmArt, a collaborative project between computer science and art, which resulted in two installations of interactive art that incorporates swarm intelligence. We describe the scientific context of the artwork, technical details of the video system, through which user interaction is controlled, and the realization of the swarm-based simulations, which were projected onto a large screen. Finally, we explain and illustrate our two recent SwarmArt installations in a Calgary gallery.

http://pages.cpsc.ucalgary.ca/~boyd/papers/acmMM04-boyd-hushlak-jacob.pdf

SwarmArt: interactive art from swarm intelligence

We present a system that provides real-time audio feedback to athletes performing repetitive, periodic movements. The system synchronizes the temporal signal from a sensor placed on theathletes body witha model signal. The audio feedback tellsthe athlete how well they are synchronized withthe model, and whether ornot they are deviating from the model at critical points in the periodic motion. Because the feedback is continuous and in real-time ,t he athlete is able to correct their motion in response to the sounds they hear. The system uses simple, inexpensive instrumentation(the entire system costs less than $500) and avoids the uses of expensive and inconvenient motion capture systems. We demonstrate th ee ffectiveness of the system with a case study featuring a speedskater that had developed a significant anomaly in his technique.

http://pages.cpsc.ucalgary.ca/~boyd/papers/icad10-godbout-boyd.pdf

Corrective sonic feedback for speed skating: a case study

Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the purpose of this study was to determine if pre-intervention multi-sensor accelerometer data (e.g., back, thigh, shank, foot accelerometers) and patient reported outcome measures (e.g., pain, symptoms, function, quality of life) can retrospectively predict post-intervention response to a 6-week hip strengthening exercise intervention in a knee OA cohort. Thirty-nine adults with knee osteoarthritis completed a 6-week hip strengthening exercise intervention and were sub-grouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in patient reported outcome measures. Pre-intervention multi-sensor accelerometer data recorded at the back, thigh, shank, and foot and Knee Injury and Osteoarthritis Outcome Score subscale data were used as potential predictors of response in a discriminant analysis of principal components. The thigh was the single best placement for classifying responder sub-groups (74.4%). Overall, the best combination of sensors was the back, thigh, and shank (81.7%), but a simplified two sensor solution using the back and thigh was not significantly different (80.0%; p = 0.27). While three sensors were best able to identify responders, a simplified two sensor array at the back and thigh may be the most ideal configuration to provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.

Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jeffrey E. Boyd

Author Tools

Chat about Author

Papers

Biometric gait recognition

Biometric gait recognition

SwarmArt: interactive art from swarm intelligence

Corrective sonic feedback for speed skating: a case study

Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis.