Open AccessJournal Article
Tracking Activities in Complex Settings Using Smart Environment Technologies.
TLDR
This paper looks at approaches to perform real-time recognition of Activities of Daily Living, and enhances other related research efforts to develop approaches that are effective when activities are interrupted and interleaved.Abstract:
The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.read more
Citations
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Journal ArticleDOI
Activity recognition on streaming sensor data
TL;DR: The experiments conducted on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information into the feature leads to best performance for streaming activity recognition.
Journal ArticleDOI
Recognizing independent and joint activities among multiple residents in smart environments
TL;DR: This paper looks at approaches to perform real-time recognition of Activities of Daily Living, and enhances other related research efforts to develop approaches that are effective when activities are interrupted and interleaved.
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Recent trends in machine learning for human activity recognition—A survey
TL;DR: This article presents a comprehensive overview of recent machine learning and data mining techniques generally employed for AR and the underpinning problems and challenges associated with the existing systems.
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Literature review on monitoring technologies and their outcomes in independently living elderly people.
TL;DR: Monitoring technology is a promising field, with applications to the long-term care of elderly persons, however, monitoring technologies have to be brought to the next level, with longitudinal studies that evaluate their (cost-) effectiveness to demonstrate the potential to prolong independent living of elderly people.
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Sensor-Based Datasets for Human Activity Recognition – A Systematic Review of Literature
TL;DR: An analysis of the sensor-based data sets used in HAR to date is provided, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.
References
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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
TL;DR: The upper bound is obtained for a specific probabilistic nonsequential decoding algorithm which is shown to be asymptotically optimum for rates above R_{0} and whose performance bears certain similarities to that of sequential decoding algorithms.
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Inferring activities from interactions with objects
Matthai Philipose,Kenneth P. Fishkin,Mike Perkowitz,Donald J. Patterson,Dieter Fox,Henry Kautz,Dirk Hähnel +6 more
TL;DR: The key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution.
Proceedings ArticleDOI
Activity recognition and monitoring using multiple sensors on different body positions
TL;DR: The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented and the tradeoff between recognition accuracy and computational complexity is analyzed.
Journal ArticleDOI
Assessing the Quality of Activities in a Smart Environment
TL;DR: The results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology and will be useful for automating remote health monitoring and interventions.