Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

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This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring.

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Peerasak INTARAPAIBOON†a) , Student Member, Ekawit NANTAJEEWARAWAT †b) , and Thanaruk THEERAMUNKONG †c) , Members SUMMARY Based on sliding-window rule application and extraction filtering, we present a framework for extracting multi-slot frames describing chemical reactions from Thai free text with unknown target-phrase boundaries. A supervised rule learning algorithm is employed for automatic construction of pattern-based extraction rules from hand-tagged training phrases. A filtering method is devised for removal of incorrect extraction results based on features observed from text portions appearing between adjacent slot fillers in source documents. Extracted reaction frames are represented as concept expressions in description logics and are used as metadata for document indexing. A document knowledge base supporting semantics-based information retrieval is constructed by integrating document metadata with domain-specific ontologies. key words: information extraction, semantics-based information retrieval, ontology, description logics, automated reasoning IntroductionIn traditional keyword-based information retrieval systems, retrieval results are determined solely by appearance of query keywords in documents or in document indexes. In domain-specific applications, however, it is often desirable to describe an information need more precisely by specifying required relations between domain concepts. A user in the chemistry domain, for example, may wish to search for a document concerning "a chemical reaction that produces a compound containing a carbon atom." With the background knowledge that "propionaldehyde has some carbon atom as its component," the same user may furthermore expect the retrieval results to include a document containing a statement such as "propionaldehyde is obtained from the oxidation reaction of 1-propanol," which looks very different syntactically from the search condition specified above. It is anticipated that information extraction (IE) technology and recent development of machine-processable ontology languages, such as OWL [1], will contribute significantly to realization of such semantics-based information retrieval.In this paper, we present a framework for extracting multi-slot frames describing chemical reactions from chemistry thesis abstracts written in Thai. From input thesis abstracts, partially annotated with entity classes in a prepro- , is used as the core algorithm for constructing extraction rules. Pattern-based IE rules do not have ability to automatically segment input documents so that they can be applied only to relevant text portions. When applied to free text, a rule is usually applied to each individual sentence one by one. Identifying the boundary of a Thai sentence is, however, problematic. In Thai, there is no explicit end-sentence punctuation [4] and the notion of a sentence is unclear [2]. To apply IE rules without predetermining the boundaries of sentences and potential target phrases, rule application using sliding windows (RAW) is introduced. Using sliding...