An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
Knowledge and Information Systems
An optimum accelerometer configuration and simple algorithm for accurately detecting falls
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extracting a diagnostic gait signature
Pattern Recognition
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Activity recognition in the home using a hierarchal framework with object usage data
Journal of Ambient Intelligence and Smart Environments
An agent-based approach to care in independent living
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
Elicitation of neurological knowledge with ABML
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Automatic recognition of gait-related health problems in the elderly using machine learning
Multimedia Tools and Applications
Evaluation of an inexpensive depth camera for in-home gait assessment
Journal of Ambient Intelligence and Smart Environments
Nomadic gestures: A technique for reusing gesture commands for frequent ambient interactions
Journal of Ambient Intelligence and Smart Environments
Detection of daily living activities using a two-stage Markov model
Journal of Ambient Intelligence and Smart Environments - Intelligent agents in Ambient Intelligence and smart environments
A personalized exercise trainer for the elderly
Journal of Ambient Intelligence and Smart Environments
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In Europe, in particular, growing numbers of elderly people need sustainable elderly care, which the young are not able to provide. As an alternative, elderly care can be provided through home-based, automatic, health-monitoring systems. Here we propose data-mining algorithms in a system for the automatic recognition of health problems, activities and falls through the analysis of gait. The gait of the elderly is captured using a motion-capture system and the resulting time series of position coordinates are analyzed with a data-mining approach in order to classify it into five health states: 1 normal, 2 with hemiplegia, 3 with Parkinson's disease, 4 with pain in the back and 5 with pain in the leg, or into five activities/falls: 1 accidental fall, 2 unconscious fall, 3 walking, 4 standing/sitting, 5 lying down/lying. We propose and analyze four data-mining approaches: 1 CML --Classical machine-learning approach with raw sensor data, 2 SCML --Classical machine-learning approach with semantic attributes, 3 MDTW --Multidimensional dynamic time-warping approach with raw sensor data and 4 SMDTW --Multidimensional dynamic time-warping approach with semantic attributes. According to the results of the experiments, SMDTW achieved the highest classification accuracy of the four proposed approaches, and transforming the raw data into the semantic attributes significantly improved the performance of the approaches.Since the observed health problems are related also to postural instability and danger of falling, their early detection helps to prevent elderly people from falling.