Frontiers of Engineering Reports on Leading-Edge Engineering from the 2005 Symposium (2006) / Chapter Skim
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Large-Scale Activity-Recognition Systems
Large-Scale Activity-Recognition Systems
Pages 23-32
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From page 23... ...
Monitoring human activity is a basic aspect of reasoning about activity. In fact, monitoring is something we all do -- parents monitor children, adults monitor elderly parents, managers monitor teams, nurses monitor patients, and trainers monitor trainees; people following medication regimens, diets, recipes, or directions monitor themselves.
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From page 24... ...
Actual forms include activities in 23 categories, such as "housework" and "hygiene," which instantiate to tens of thousands of activities, such as "cleaning a bathtub" and "brushing teeth." Thus, an activity-recognition system that could track thousands of activities in non-laboratory conditions would remove a substantial burden from human monitors. Professional caregivers could, at any time, be provided with a version of this form with potentially troublesome areas highlighted.
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From page 25... ...
to Discriminating Among Activities activities Symbolic Inference Modules high-level features Feature Selection Modules raw sensor data FIGURE 2 A typical activity-recognition sensors system.
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From page 26... ...
These techniques differ in several ways, such as whether they support statistical, higher order, or temporal reasoning; the degree to which they learn and the amount of human intervention they require to learn; and the efficiency with which they process various kinds of features, especially higher dimensional features. In Figure 2, the variety of feature selections and inference algorithms is indicated by stacks of boxes.
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From page 27... ...
tag consists of an antenna, some protocol logic, and optional nonvolatile memory. RFID tags use the energy of the interrogating signal to return a 64-bit to 128-bit identifier unique to each tag, and when applicable, data stored in on-tag memory.
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From page 28... ...
Unfortunately, because a conventional RFID tag simply reports the presence of tagged objects in the reader's field, and not their use, long-range tags cannot tell us when objects are being used either. Long-range tags simply list all tagged objects in the room they are monitoring.
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From page 29... ...
Therefore, the statistical framework that processes the data must be able to cope with these false "hits." Early studies indicate that an iBracelet equipped with inexpensive inductively coupled tags are a practical means of detecting object touch, and therefore object use. Some people may consider wearing a bracelet an unacceptable requirement, however.
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From page 30... ...
takes five minutes on average; in each one-second window, there is a 40, 20, and 30 percent chance respectively of a kettle, stove, or faucet being used. Experiments in a real home with 14 subjects, each performing a randomly selected subset of 66 different activities selected from ADL forms, and using activity models constructed by hand to classify the resulting data automatically, have yielded higher than 70 percent (and often close to 90 percent)
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From page 31... ...
Traditional approaches to activity recognition have not been successful at monitoring large numbers of day-to-day activities in unstructured environments, partly because they were unable to identify reliably sufficiently discriminative highlevel features. A new family of sensors, based on RFID, is able to identify most of the objects used in activities simply and accurately, and even simple statistical models can classify large numbers of activities with reasonable accuracy.
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From page 32... ...
2005. Unsupervised Activity Recognition Using Auto matically Mined Common Sense.
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